T2 mapping outperforms normalised FLAIR in identifying hippocampal sclerosis

Rationale Qualitatively, FLAIR MR imaging is sensitive to the detection of hippocampal sclerosis (HS). Quantitative analysis of T2 maps provides a useful objective measure and increased sensitivity over visual inspection of T2-weighted scans. We aimed to determine whether quantification of normalised FLAIR is as sensitive as T2 mapping in detection of HS. Method Dual echo T2 and FLAIR MR images were retrospectively analysed in 27 patients with histologically confirmed HS and increased T2 signal in ipsilateral hippocampus and 14 healthy controls. Regions of interest were manually segmented in all hippocampi aiming to avoid inclusion of CSF. Hippocampal T2 values and measures of normalised FLAIR Signal Intensity (nFSI) were compared in healthy and sclerotic hippocampi. Results HS was identified on T2 values with 100% sensitivity and 100% specificity. HS was identified on nFSI measures with 60% sensitivity and 93% specificity. Conclusion T2 mapping is superior to nFSI for identification of HS

Travel, health and well-being: A focus on past studies, a special issue, and future research

This introduction to the special issue on travel, health and well-being is subdivided into three parts. In Section 1 we provide a summary of existing literature analysing how health and well-being are related with transport and travel behaviour. An overview and short descriptions of the studies included in this special issue are given in Section 2. In Section 3 we conclude this editorial by uncovering research gaps and suggesting avenues for further research

Analyzing travel captivity by measuring the gap in travel satisfaction between chosen and alternative commute modes

In this study, we investigated travel captivity from the perspective of travel satisfaction. Using survey data from 565 commuters in Portland, Oregon, we compared satisfaction with the most recent commute trip (using the chosen mode) and hypothetical commute satisfaction if using an alternative mode. The difference in travel satisfaction between the chosen and alternative mode – referred to as the travel satisfaction gap – was used as a fine-grained proxy measure of travel captivity. Results indicate that active mode (walk/bicycle) users would be less satisfied when the alternative modes were auto or transit, while auto and transit commuters would be slightly more satisfied if they commuted by walking or bicycling. These outcomes suggest that auto users are most captive, while active travelers are mostly choice users. Results also show that respondents would be more satisfied with an alternative mode if it would enable more talking to other passengers

Using a single-channel reference with the MBSTOI binaural intelligibility metric

In order to assess the intelligibility of a target signal in a noisy environment, intrusive speech intelligibility metrics are typically used. They require a clean reference signal to be available which can be difficult to obtain especially for binaural metrics like the modified binaural short time objective intelligibility metric (MBSTOI). We here present a hybrid version of MBSTOI that incorporates a deep learning stage that allows the metric to be computed with only a single-channel clean reference signal. The models presented are trained on simulated data containing target speech, localised noise, diffuse noise, and reverberation. The hybrid output metrics are then compared directly to MBSTOI to assess performances. Results show the performance of our single channel reference vs MBSTOI. The outcome of this work offers a fast and flexible way to generate audio data for machine learning (ML) and highlights the potential for low level implementation of ML into existing tools

Heavy metal pollution and co-selection for antibiotic resistance: A microbial palaeontology approach

This is the final version. Available on open access from Elsevier via the DOI in this recordFrequent and persistent heavy metal pollution has profound effects on the composition and activity of microbial communities. Heavy metals select for metal resistance but can also co-select for resistance to antibiotics, which is a global health concern. We here document metal concentration, metal resistance and antibiotic resistance along a sediment archive from a pond in the North West of the United Kingdom covering over a century of anthropogenic pollution. We specifically focus on zinc, as it is a ubiquitous and toxic metal contaminant known to co-select for antibiotic resistance, to assess the impact of temporal variation in heavy metal pollution on microbial community diversity and to quantify the selection effects of differential heavy metal exposure on antibiotic resistance. Zinc concentration and bioavailability was found to vary over the core, likely reflecting increased industrialisation around the middle of the 20th century. Zinc concentration had a significant effect on bacterial community composition, as revealed by a positive correlation between the level of zinc tolerance in culturable bacteria and zinc concentration. The proportion of zinc resistant isolates was also positively correlated with resistance to three clinically relevant antibiotics (oxacillin, cefotaxime and trimethoprim). The abundance of the class 1 integron-integrase gene, intI1, marker for anthropogenic pollutants correlated with the prevalence of zinc- and cefotaxime resistance but not with oxacillin and trimethoprim resistance. Our microbial palaeontology approach reveals that metal-contaminated sediments from depths that pre-date the use of antibiotics were enriched in antibiotic resistant bacteria, demonstrating the pervasive effects of metal-antibiotic co-selection in the environment.University of Exete

Estimating blood pressure trends and the nocturnal dip from photoplethysmography

Objective: Evaluate a method for the estimation of the nocturnal systolic blood pressure dip from 24-hour blood pressure trends using a wrist-worn Photoplethysmography (PPG) sensor and a deep neural network in free-living individuals, comparing the deep neural network to traditional machine learning and non-machine learning baselines. Approach: A wrist-worn PPG sensor was worn by 106 healthy individuals for 226 days during which 5111 reference values for blood pressure were obtained with a 24-hour ambulatory blood pressure monitor as ground truth and matched with the PPG sensor data. Features based on heart rate variability and pulse morphology were extracted from the PPG waveforms. Machine learning models (linear regression, random forests, dense neural networks and long- and short-term memory neural networks) were then trained and evaluated in their capability of tracking trends in systolic and diastolic blood pressure, as well as the estimation of the nocturnal systolic blood pressure dip. Main results Best performance was obtained with a deep long- and shortterm memory neural network with a Root Mean Squared Error (RMSE) of 3.12±2.20 ∆mmHg and a correlation of 0.69 (p = 3 ∗ 10−5) with the ground truth Systolic Blood Pressure (SBP) dip. This dip was derived from trend estimates of blood pressure which had an RMSE of 8.22±1.49 mmHg for systolic and 6.55±1.39 mmHg for diastolic blood pressure. The random forest model showed slightly lower average error magnitude for SBP trends (7.86±1.57 mmHg), however Bland-Altmann analysis revealed systematic problems in its predictions that were less present in the long- and short-term memory model. Significance The work provides first evidence for the unobtrusive estimation of the nocturnal blood pressure dip, a highly prognostic clinical parameter. It is also the first to evaluate unobtrusive blood pressure measurement in a large data set of unconstrained 24-hour measurements in free-living individuals and provides evidence for the utility of long- and short-term models in this domain

HSPB1, HSPB6, HSPB7 and HSPB8 Protect against RhoA GTPase-Induced Remodeling in Tachypaced Atrial Myocytes

BACKGROUND: We previously demonstrated the small heat shock protein, HSPB1, to prevent tachycardia remodeling in in vitro and in vivo models for Atrial Fibrillation (AF). To gain insight into its mechanism of action, we examined the protective effect of all 10 members of the HSPB family on tachycardia remodeling. Furthermore, modulating effects of HSPB on RhoA GTPase activity and F-actin stress fiber formation were examined, as this pathway was found of prime importance in tachycardia remodeling events and the initiation of AF. METHODS AND RESULTS: Tachypacing (4 Hz) of HL-1 atrial myocytes significantly and progressively reduced the amplitude of Ca²⁺ transients (CaT). In addition to HSPB1, also overexpression of HSPB6, HSPB7 and HSPB8 protected against tachypacing-induced CaT reduction. The protective effect was independent of HSPB1. Moreover, tachypacing induced RhoA GTPase activity and caused F-actin stress fiber formation. The ROCK inhibitor Y27632 significantly prevented tachypacing-induced F-actin formation and CaT reductions, showing that RhoA activation is required for remodeling. Although all protective HSPB members prevented the formation of F-actin stress fibers, their mode of action differs. Whilst HSPB1, HSPB6 and HSPB7 acted via direct prevention of F-actin formation, HSPB8-protection was mediated via inhibition of RhoA GTPase activity. CONCLUSION: Overexpression of HSPB1, as well as HSPB6, HSPB7 and HSPB8 independently protect against tachycardia remodeling by attenuation of the RhoA GTPase pathway at different levels. The cardioprotective role for multiple HSPB members indicate a possible therapeutic benefit of compounds able to boost the expression of single or multiple members of the HSPB family