Evaluating the impact of an incentive scheme to encourage pregnant people to set a quit-smoking date

Purpose: This study aims to demonstrate the evaluation of an incentive scheme to encourage pregnant people to set a quit-smoking date. Design/methodology/approach: The paper outlines a collaborative approach, working with pregnant people, clinicians, tobacco dependency practitioners and academics to gain insights into their perspectives and experiences. Quantitative and qualitative data were analysed. Findings: The incentive scheme and appropriate support from clinicians have been shown to encourage pregnant people to set a quit date. The tobacco dependency practitioners helped remove barriers, such as the perception of the stigmatisation of smoking when pregnant. The practitioners also helped pregnant people make informed decisions to support successful behaviour change. The impact of the scheme resulted in improved infant health indicators. The scheme’s evaluation also supported establishing stakeholder knowledge exchange and learning processes. Research limitations/implications: This is a single-site study among a relatively small group of people designed to achieve a specific evaluation objective. Caution in generalising to wider settings should be exercised. Practical implications: This study highlights the efficacy of an incentive scheme, complemented with support from clinicians, and the significance of knowledge exchange and collaboration between stakeholders in health care with significance in similar settings. Originality/value: The paper details the incentive scheme input, actions, output, outcomes and impact involving a wider range of stakeholders, including the emotional consequences for participants, clinicians and academics

Towards more sustainable urban transportation for NetZero cities: assessing air quality and risk for e-scooter users using sensor fusion and artificial intelligence

The need to develop smart and NetZero cities and reduce carbon emission is driving innovation in cities around the world to use electric transportation technologies. Among that the use of e-scooters. Nottingham (UK) is one of the cities that has an e-scooter scheme where people could rent e-scooters to travel around the city. However, in the current situation, to ensure pedestrian safety e-scooters need to be ridden on the road amongst cars, most of them are fossil fuelled. This gives rise to two potential risks for e-scooter users: the air quality that they breathe and the physical risk of being near cars, where drivers may not be familiar with seeing e-scooters on the road. This paper uses a mixed methods approach by conducting surveys to drivers and e-scooter users, jointly with an experimental work to monitor the journey of e-scooter users combining air quality, GPS data and 360 degrees camera footage to assess the risk to e-scooter riders using sensor fusion and artificial intelligence. The results indicate that the suggested novel methodology is effective in understanding the current limitations and the potential air quality and physical risks to e-scooter users

Assessing air quality and physical risks to E-scooter riders in urban environments through artificial intelligence and a mixed methods approach

The need to develop green and smart transport solutions for NetZero cities to reduce carbon emissions through the use of clean energy is driving innovation in cities around the world. A result of this trend is a rise in micro-mobility solutions such as e-scooters in cities around the globe. Nottingham (UK) is one of the cities that conducted an e-scooter pilot scheme permitting the rental of e-scooters to travel around the city in a bid to encourage more sustainable personal transport use. However, to ensure pedestrian safety, e-scooters are required to be ridden on the road network among cars. Hence, giving rise to two potential risks for e-scooter users: the air quality that they breathe and the physical risk of being near cars, whose drivers may be unfamiliar with seeing e-scooters on the road. This study seeks to explore this interaction using a mixed methods approach to explore the experiences of e-scooter riders in respect to their physical safety and exposure to air pollution. The research makes use of two quantitative surveys an international e-scooter user survey n = 801 and a survey of UK car drivers n = 92, focussed qualitative e-scooter rider interviews and quantitative in-depth road data collection trials comprising of air quality particulate sensing, video capturing around the rider and GPS tracking. The in-depth road data was analysed using an AI approach utilising the ASPS approach, the automated sensor and signal processing approach, implemented for image and signal processing to detect the existence of cars alongside the pollution readings. The findings show that e-scooter riders are typically aware of physical dangers to their safety from other road users, as well as how their presence among pedestrians can impact on more vulnerable users; however, they were unaware of the prevalence and effects of air pollution on them whilst riding. The study highlights the need for a multifaceted approach to improvements in safety for micro-mobility users, predominately considering suitable infrastructure to sperate them from motor vehicles and pedestrians but also the need to consider the proximity to emission emitting vehicles, developing infrastructure in green spaces to address these air pollution levels

Predictions from Lattice QCD

In the past year, we calculated with lattice QCD three quantities that were unknown or poorly known. They are the $q^2$ dependence of the form factor in semileptonic $D\to Kl\nu$ decay, the decay constant of the $D$ meson, and the mass of the $B_c$ meson. In this talk, we summarize these calculations, with emphasis on their (subsequent) confirmation by experiments.Comment: v1: talk given at the International Conference on QCD and Hadronic Physics, Beijing, June 16-20, 2005; v2: poster presented at the XXIIIrd International Symposium on Lattice Field Theory, Dublin, July 25-3

Frequency of medically attended adverse events following tetanus and diphtheria toxoid vaccine in adolescents and young adults: a Vaccine Safety Datalink study

<p>Abstract</p> <p>Background</p> <p>Local reactions are the most commonly reported adverse events following tetanus and diphtheria toxoid (Td) vaccine and the risk of local reactions may increase with number of prior Td vaccinations.</p> <p>Methods</p> <p>To estimate the risk of medically attended local reactions following Td vaccination in adolescents and young adults we conducted a six-year retrospective cohort study assessing 436,828 Td vaccinations given to persons 9 through 25 years of age in the Vaccine Safety Datalink population from 1999 through 2004.</p> <p>Results</p> <p>Overall, the estimated risk of a medically attended local reaction was 3.6 events per 10,000 Td vaccinations. The lowest risk (2.8 events per 10,000 vaccinations) was found in the 11 to 15 year old age group. In comparison with that group, the event risks were significantly higher in both the 9 to 10 and 21 to 25 year old age groups. The risk of a local reaction was significantly higher in persons who had received another tetanus and diphtheria toxoid containing vaccine (TDCV) in the previous five years (incidence rate ratio, 2.9; 95% confidence interval, 1.2 to 7.2). Twenty-eight percent of persons with a local reaction to Td vaccine were prescribed antibiotics.</p> <p>Conclusion</p> <p>Medically attended local reactions were uncommon following Td vaccination. The risk of those reactions varied by age and by prior receipt of TDCVs. These findings provide a point of reference for future evaluations of the safety profile of newer vaccines containing tetanus or diphtheria toxoid.</p

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

Are autistic traits in the general population stable across development?

There is accumulating evidence that autistic traits (AT) are on a continuum in the general population, with clinical autism representing the extreme end of a quantitative distribution. While the nature and severity of symptoms in clinical autism are known to persist over time, no study has examined the long-term stability of AT among typically developing toddlers. The current investigation measured AT in 360 males and 400 males from the general population close to two decades apart, using the Pervasive Developmental Disorder subscale of the Child Behavior Checklist in early childhood (M = 2.14 years; SD = 0.15), and the Autism-Spectrum Quotient in early adulthood (M = 19.50 years; SD = 0.70). Items from each scale were further divided into social (difficulties with social interaction and communication) and non-social (restricted and repetitive behaviours and interests) AT. The association between child and adult measurements of AT as well the influence of potentially confounding sociodemographic, antenatal and obstetric variables were assessed using Pearson's correlations and linear regression. For males, Total AT in early childhood were positively correlated with total AT (r = .16, p = .002) and social AT (r = .16, p = .002) in adulthood. There was also a positive correlation for males between social AT measured in early childhood and Total (r = .17, p = .001) and social AT (r = .16, p = .002) measured in adulthood. Correlations for non-social AT did not achieve significance in males. Furthermore, there was no significant longitudinal association in AT observed for males or females. Despite the constraints of using different measures and different raters at the two ages, this study found modest developmental stability of social AT from early childhood to adulthood in boys

Neural correlates of enhanced visual short-term memory for angry faces: An fMRI study

Copyright: © 2008 Jackson et al.Background: Fluid and effective social communication requires that both face identity and emotional expression information are encoded and maintained in visual short-term memory (VSTM) to enable a coherent, ongoing picture of the world and its players. This appears to be of particular evolutionary importance when confronted with potentially threatening displays of emotion - previous research has shown better VSTM for angry versus happy or neutral face identities.Methodology/Principal Findings: Using functional magnetic resonance imaging, here we investigated the neural correlates of this angry face benefit in VSTM. Participants were shown between one and four to-be-remembered angry, happy, or neutral faces, and after a short retention delay they stated whether a single probe face had been present or not in the previous display. All faces in any one display expressed the same emotion, and the task required memory for face identity. We find enhanced VSTM for angry face identities and describe the right hemisphere brain network underpinning this effect, which involves the globus pallidus, superior temporal sulcus, and frontal lobe. Increased activity in the globus pallidus was significantly correlated with the angry benefit in VSTM. Areas modulated by emotion were distinct from those modulated by memory load.Conclusions/Significance: Our results provide evidence for a key role of the basal ganglia as an interface between emotion and cognition, supported by a frontal, temporal, and occipital network.The authors were supported by a Wellcome Trust grant (grant number 077185/Z/05/Z) and by BBSRC (UK) grant BBS/B/16178

Green Fluorescent Protein Labeling of Listeria, Salmonella, and Escherichia coli O157:H7 for Safety-Related Studies

Many food safety-related studies require tracking of introduced foodborne pathogens to monitor their fate in complex environments. The green fluorescent protein (GFP) gene (gfp) provides an easily detectable phenotype so has been used to label many microorganisms for ecological studies. The objectives of this study were to label major foodborne pathogens and related bacteria, including Listeria monocytogenes, Listeria innocua, Salmonella, and Escherichia coli O157:H7 strains, with GFP and characterize the labeled strains for stability of the GFP plasmid and the plasmid's effect on bacterial growth. GFP plasmids were introduced into these strains by a CaCl2 procedure, conjugation or electroporation. Stability of the label was determined through sequential propagation of labeled strains in the absence of selective pressure, and rates of plasmid-loss were calculated. Stability of the GFP plasmid varied among the labeled species and strains, with the most stable GFP label observed in E. coli O157:H7. When grown in nonselective media for two consecutive subcultures (ca. 20 generations), the rates of plasmid loss among labeled E. coli O157:H7, Salmonella and Listeria strains ranged from 0%–30%, 15.8%–99.9% and 8.1%–93.4%, respectively. Complete loss (>99.99%) of the plasmid occurred in some labeled strains after five consecutive subcultures in the absence of selective pressure, whereas it remained stable in others. The GFP plasmid had an insignificant effect on growth of most labeled strains. E. coli O157:H7, Salmonella and Listeria strains can be effectively labeled with the GFP plasmid which can be stable in some isolates for many generations without adversely affecting growth rates

A new molecular breast cancer subclass defined from a large scale real-time quantitative RT-PCR study

BACKGROUND: Current histo-pathological prognostic factors are not very helpful in predicting the clinical outcome of breast cancer due to the disease's heterogeneity. Molecular profiling using a large panel of genes could help to classify breast tumours and to define signatures which are predictive of their clinical behaviour. METHODS: To this aim, quantitative RT-PCR amplification was used to study the RNA expression levels of 47 genes in 199 primary breast tumours and 6 normal breast tissues. Genes were selected on the basis of their potential implication in hormonal sensitivity of breast tumours. Normalized RT-PCR data were analysed in an unsupervised manner by pairwise hierarchical clustering, and the statistical relevance of the defined subclasses was assessed by Chi2 analysis. The robustness of the selected subgroups was evaluated by classifying an external and independent set of tumours using these Chi2-defined molecular signatures. RESULTS: Hierarchical clustering of gene expression data allowed us to define a series of tumour subgroups that were either reminiscent of previously reported classifications, or represented putative new subtypes. The Chi2 analysis of these subgroups allowed us to define specific molecular signatures for some of them whose reliability was further demonstrated by using the validation data set. A new breast cancer subclass, called subgroup 7, that we defined in that way, was particularly interesting as it gathered tumours with specific bioclinical features including a low rate of recurrence during a 5 year follow-up. CONCLUSION: The analysis of the expression of 47 genes in 199 primary breast tumours allowed classifying them into a series of molecular subgroups. The subgroup 7, which has been highlighted by our study, was remarkable as it gathered tumours with specific bioclinical features including a low rate of recurrence. Although this finding should be confirmed by using a larger tumour cohort, it suggests that gene expression profiling using a minimal set of genes may allow the discovery of new subclasses of breast cancer that are characterized by specific molecular signatures and exhibit specific bioclinical features