A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

From Springer Nature via Jisc Publications RouterHistory: received 2019-05-03, rev-recd 2019-11-12, accepted 2020-02-06, registration 2020-02-06, pub-electronic 2020-03-14, online 2020-03-14, pub-print 2021-07Publication status: PublishedFunder: University of ManchesterAbstract: Short-term traffic parameter forecasting is critical to modern urban traffic management and control systems. Predictive accuracy in data-driven traffic models is reduced when exposed to non-recurring or non-routine traffic events, such as accidents, road closures, and extreme weather conditions. The analytical mining of data from social networks – specifically twitter – can improve urban traffic parameter prediction by complementing traffic data with data representing events capable of disrupting regular traffic patterns reported in social media posts. This paper proposes a deep learning urban traffic prediction model that combines information extracted from tweet messages with traffic and weather information. The predictive model adopts a deep Bi-directional Long Short-Term Memory (LSTM) stacked autoencoder (SAE) architecture for multi-step traffic flow prediction trained using tweets, traffic and weather datasets. The model is evaluated on an urban road network in Greater Manchester, United Kingdom. The findings from extensive empirical analysis using real-world data demonstrate the effectiveness of the approach in improving prediction accuracy when compared to other classical/statistical and machine learning (ML) state-of-the-art models. The improvement in predictive accuracy can lead to reduced frustration for road users, cost savings for businesses, and less harm to the environment

Over-expression of Grhl2 causes spina bifida in the Axial defects mutant mouse

Cranial neural tube defects (NTDs) occur in mice carrying mutant alleles of many different genes, whereas isolated spinal NTDs (spina bifida) occur in fewer models, despite being common human birth defects. Spina bifida occurs at high frequency in the Axial defects (Axd) mouse mutant but the causative gene is not known. In the current study, the Axd mutation was mapped by linkage analysis. Within the critical genomic region, sequencing did not reveal a coding mutation whereas expression analysis demonstrated significant up-regulation of grainyhead-like 2 (Grhl2) in Axd mutant embryos. Expression of other candidate genes did not differ between genotypes. In order to test the hypothesis that over-expression of Grhl2 causes Axd NTDs, we performed a genetic cross to reduce Grhl2 function in Axd heterozygotes. Grhl2 loss of function mutant mice were generated and displayed both cranial and spinal NTDs. Compound heterozygotes carrying both loss (Grhl2 null) and putative gain of function (Axd) alleles exhibited normalization of spinal neural tube closure compared with Axd/+ littermates, which exhibit delayed closure. Grhl2 is expressed in the surface ectoderm and hindgut endoderm in the spinal region, overlapping with grainyhead-like 3 (Grhl3). Axd mutants display delayed eyelid closure, as reported in Grhl3 null embryos. Moreover, Axd mutant embryos exhibited increased ventral curvature of the spinal region and reduced proliferation in the hindgut, reminiscent of curly tail embryos, which carry a hypomorphic allele of Grhl3. Overall, our data suggest that defects in Axd mutant embryos result from over-expression of Grhl2

Effect of the Growth Assessment Protocol on the DEtection of Small for GestatioNal age fetus: process evaluation from the DESiGN cluster randomised trial

BACKGROUND: Reducing the rate of stillbirth is an international priority. At least half of babies stillborn in high-income countries are small for gestational-age (SGA). The Growth Assessment Protocol (GAP), a complex antenatal intervention that aims to increase the rate of antenatal detection of SGA, was evaluated in the DESiGN type 2 hybrid effectiveness-implementation cluster randomised trial (n = 13 clusters). In this paper, we present the trial process evaluation. METHODS: A mixed-methods process evaluation was conducted. Clinical leads and frontline healthcare professionals were interviewed to inform understanding of context (implementing and standard care sites) and GAP implementation (implementing sites). Thematic analysis of interview text used the context and implementation of complex interventions framework to understand acceptability, feasibility, and the impact of context. A review of implementing cluster clinical guidelines, training and maternity records was conducted to assess fidelity, dose and reach. RESULTS: Interviews were conducted with 28 clinical leads and 27 frontline healthcare professionals across 11 sites. Staff at implementing sites generally found GAP to be acceptable but raised issues of feasibility, caused by conflicting demands on resource, and variable beliefs among clinical leaders regarding the intervention value. GAP was implemented with variable fidelity (concordance of local guidelines to GAP was high at two sites, moderate at two and low at one site), all sites achieved the target to train > 75% staff using face-to-face methods, but only one site trained > 75% staff using e-learning methods; a median of 84% (range 78–87%) of women were correctly risk stratified at the five implementing sites. Most sites achieved high scores for reach (median 94%, range 62–98% of women had a customised growth chart), but generally, low scores for dose (median 31%, range 8–53% of low-risk women and median 5%, range 0–17% of high-risk women) were monitored for SGA as recommended. CONCLUSIONS: Implementation of GAP was generally acceptable to staff but with issues of feasibility that are likely to have contributed to variation in implementation strength. Leadership and resourcing are fundamental to effective implementation of clinical service changes, even when such changes are well aligned to policy mandated service-change priorities. TRIAL REGISTRATION: Primary registry and trial identifying number: ISRCTN 67698474. Registered 02/11/16. https://doi.org/10.1186/ISRCTN67698474

Pregnancy and neonatal outcomes of COVID-19: The PAN-COVID study

Objective To assess perinatal outcomes for pregnancies affected by suspected or confirmed SARS-CoV-2 infection. Methods Prospective, web-based registry. Pregnant women were invited to participate if they had suspected or confirmed SARS-CoV-2 infection between 1st January 2020 and 31st March 2021 to assess the impact of infection on maternal and perinatal outcomes including miscarriage, stillbirth, fetal growth restriction, pre-term birth and transmission to the infant. Results Between April 2020 and March 2021, the study recruited 8239 participants who had suspected or confirmed SARs-CoV-2 infection episodes in pregnancy between January 2020 and March 2021. Maternal death affected 14/8197 (0.2%) participants, 176/8187 (2.2%) of participants required ventilatory support. Pre-eclampsia affected 389/8189 (4.8%) participants, eclampsia was reported in 40/ 8024 (0.5%) of all participants. Stillbirth affected 35/8187 (0.4 %) participants. In participants delivering within 2 weeks of delivery 21/2686 (0.8 %) were affected by stillbirth compared with 8/4596 (0.2 %) delivering ≥ 2 weeks after infection (95 % CI 0.3–1.0). SGA affected 744/7696 (9.3 %) of livebirths, FGR affected 360/8175 (4.4 %) of all pregnancies. Pre-term birth occurred in 922/8066 (11.5%), the majority of these were indicated pre-term births, 220/7987 (2.8%) participants experienced spontaneous pre-term births. Early neonatal deaths affected 11/8050 livebirths. Of all neonates, 80/7993 (1.0%) tested positive for SARS-CoV-2. Conclusions Infection was associated with indicated pre-term birth, most commonly for fetal compromise. The overall proportions of women affected by SGA and FGR were not higher than expected, however there was the proportion affected by stillbirth in participants delivering within 2 weeks of infection was significantly higher than those delivering ≥ 2 weeks after infection. We suggest that clinicians’ threshold for delivery should be low if there are concerns with fetal movements or fetal heart rate monitoring in the time around infection