3,061 research outputs found
Determinants of embryonic and foetal growth
The main aims of this thesis were:1. To investigate whether there are associations between determinants related to the living environment (in particular neighbourhood deprivation and air pollution) and embryonic growth, foetal growth and pregnancy outcomes;2. To assess the associations between maternal cardiometabolic determinants in pregnancy (lipid status and the presence of hypertensive disorders of pregnancy)and embryonic growth, foetal growth and childhood outcomes;3. To investigate the impact of neighbourhood deprivation on the effectiveness ofthe mHealth “Smarter Pregnancy” program, aimed at improving nutrition and lifestyle behaviours;<br/
Measuring the Health and Development of School-age Zimbabwean Children
Health, growth and development during mid-childhood (from 5 to 14 years) are poorly characterised, and this period has been termed the ‘missing middle’. This thesis describes the piloting and application of the School-Age Health, Activity, Resilience, Anthropometry and Neurocognitive (SAHARAN) toolbox to measure growth, cognitive and physical function amongst the SHINE cohort in rural Zimbabwe. The SHINE cluster-randomised trial tested the effects of a household WASH intervention and/or infant and young child feeding (IYCF) on child stunting and anaemia at age 18 months in rural Zimbabwe. SHINE showed that IYCF modestly increased linear growth and reduced stunting by age 18 months, while WASH had no effects. The SAHARAN toolbox was used to measure 1000 HIV-unexposed children (250 in each intervention arm), and 275 HIV-exposed children within the SHINE cohort to evaluate long-term outcomes. Children were re-enrolled at age seven years to evaluate growth, body composition, cognitive and physical function. Four main findings are presented from the SAHARAN toolbox measurements of this cohort. Firstly, child sex, growth and contemporary environmental conditions are associated with school-age physical and cognitive function at seven years. Secondly, early-life growth and baseline environmental conditions suggest the impact of early-life trajectories on multiple aspects of school-age growth, physical and cognitive function. Thirdly, the long-term impact of HIV-exposure in pregnancy is explored, which indicate reduced cognitive function, cardiovascular fitness and head circumference by age 7 years. Finally, associations with the SHINE trial early life interventions are explored, demonstrating that the SHINE early-life nutrition intervention has minimal impact by 7 years of age, except marginally stronger handgrip strength. The public health implications advocate that child interventions need to be earlier (including antenatal), broader (incorporating nurturing care), deeper (providing transformational WASH) and longer (supporting throughout childhood), as well as targeting particularly vulnerable groups such as children born HIV-free
Determinants of embryonic and foetal growth
The main aims of this thesis were:1. To investigate whether there are associations between determinants related to the living environment (in particular neighbourhood deprivation and air pollution) and embryonic growth, foetal growth and pregnancy outcomes;2. To assess the associations between maternal cardiometabolic determinants in pregnancy (lipid status and the presence of hypertensive disorders of pregnancy)and embryonic growth, foetal growth and childhood outcomes;3. To investigate the impact of neighbourhood deprivation on the effectiveness ofthe mHealth “Smarter Pregnancy” program, aimed at improving nutrition and lifestyle behaviours;<br/
Derogatory, Racist, and Discriminatory Speech (DRDS) in Video Gaming
Video games have been examined for their effects on cognition, learning, health, and physiological arousal, yet research on social dynamics within video gaming is limited. Studies have documented the presence of derogation, racism, and discrimination in this anonymous medium. However, gamers‟ firsthand experiences are typically examined qualitatively. Thus, this study aimed to establish a quantitative baseline for the frequency of derogatory, racist, and discriminatory speech (DRDS) in gaming. DRDS frequency, sexual harassment, and hate speech measures were administered to 150 individuals from online forums and social media groups. Descriptive and inferential analyses were used to gauge which factors affected DRDS rates. Sex, intergroup and fast-paced game types, time played with others, and identity portrayal showed positive correlations with DRDS. Results indicate an array of complex social and developmental factors contribute to experiencing, perceiving, and personally using DRDS. Implications include psychosocial health impacts similar to everyday harassment, with women being at a higher risk and age as a contributing factor
EHR-KnowGen: Knowledge-enhanced multimodal learning for disease diagnosis generation
Electronic health records (EHRs) contain diverse patient information, including medical notes, clinical events, and laboratory test results. Integrating this multimodal data can improve disease diagnoses using deep learning models. However, effectively combining different modalities for diagnosis remains challenging. Previous approaches, such as attention mechanisms and contrastive learning, have attempted to address this but do not fully integrate the modalities into a unified feature space. This paper presents EHR-KnowGen, a multimodal learning model enhanced with external domain knowledge, for improved disease diagnosis generation from diverse patient information in EHRs. Unlike previous approaches, our model integrates different modalities into a unified feature space with soft prompts learning and leverages large language models (LLMs) to generate disease diagnoses. By incorporating external domain knowledge from different levels of granularity, we enhance the extraction and fusion of multimodal information, resulting in more accurate diagnosis generation. Experimental results on real-world EHR datasets demonstrate the superiority of our generative model over comparative methods, providing explainable evidence to enhance the understanding of diagnosis results
Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea
ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK
Heterogeneous Integration of In-Memory Analog Computing Architectures with Tensor Processing Units
Tensor processing units (TPUs), specialized hardware accelerators for machine
learning tasks, have shown significant performance improvements when executing
convolutional layers in convolutional neural networks (CNNs). However, they
struggle to maintain the same efficiency in fully connected (FC) layers,
leading to suboptimal hardware utilization. In-memory analog computing (IMAC)
architectures, on the other hand, have demonstrated notable speedup in
executing FC layers. This paper introduces a novel, heterogeneous,
mixed-signal, and mixed-precision architecture that integrates an IMAC unit
with an edge TPU to enhance mobile CNN performance. To leverage the strengths
of TPUs for convolutional layers and IMAC circuits for dense layers, we propose
a unified learning algorithm that incorporates mixed-precision training
techniques to mitigate potential accuracy drops when deploying models on the
TPU-IMAC architecture. The simulations demonstrate that the TPU-IMAC
configuration achieves up to performance improvements, and
memory reductions compared to conventional TPU architectures for various CNN
models while maintaining comparable accuracy. The TPU-IMAC architecture shows
potential for various applications where energy efficiency and high performance
are essential, such as edge computing and real-time processing in mobile
devices. The unified training algorithm and the integration of IMAC and TPU
architectures contribute to the potential impact of this research on the
broader machine learning landscape
Using personalised cardiovascular models to identify new diagnostic predictors for pre-eclampsia
Haemodynamic adaptations play a crucial role in uteroplacental perfusion during pregnancy. In particular, modifications of the utero-ovarian arterial network cause a significant increase in blood volume distributed to the placenta and foetus. Failure to make these cardiovascular modifications results in complicated pregnancies caused by different disorders such as hypertension, pre-eclampsia, intrauterine growth restriction (IUGR), and placental insufficiency. In pre-eclampsia, the modifications of the utero-ovarian arterial network are unsuccessful and cause less blood volume to be distributed to the placenta and foetus. Pre-eclampsia is a hypertensive disorder that is still not fully understood, and clinicians still fail at identifying pre-eclamptic women during controls, especially at differentiating between hypertensive women and pre-eclamptic women. One reason for this is that clinicians rely heavily on blood pressure when diagnosing pre-eclampsia, and this biomarker has similar readings for both pre-eclampsia and hypertension. As part of the diagnosis of pre-eclampsia, proteinuria is used. In order to improve the diagnosis of pre-eclampsia, other biomarkers are being researched. A dataset of 21 patients was used to find novel biomarkers that can classify pre-eclampsia. The dataset is divided into two groups: uncomplicated pregnancies with hypertensive women and complicated pregnancies with pre-eclampsia. A computational model of the cardiovascular system is used to simulate blood and pressure solutions based on patient-specific observations in order to develop a new biomarker. The model employs 1D modelling which incorporates a wave intensity analysis that models forward and backward waves to provide more precise predictions of wave propagation across the artery system, particularly in the utero-ovarian system. The proposed biomarkers will include dimensionless terms formed by global maternal parameters such as systolic blood pressure, stroke volume, pulse wave velocity, etc., or local uterine parameters such as pressure and velocity in specific vessels of the uterine system. Afterwards, their ability as a classifier of pre-eclampsia will be investigated. Besides this, a case study of the prone position in pregnancy and its effects on cardiovascular changes will be carried out. To do this, the computational model will be used to study what happens when a pregnant woman is positioned in the prone position and how vital metrics like blood pressure and cardiac output are altered. It was found that the biomarkers based on the radial and arcuate arteries have a better classification ability for pre-eclampsia, even higher than the Doppler-measured Resistance Index (RI) and Pulsatility Index (PI). The novelty of this work is the introduction of new biomarkers through the use of a computational model, as well as the demonstration of the dependability and use of 1D modelling in pregnancy. The model demonstrated how biomarkers that could not be measured clinically may be easily calculated using 1D modelling and provide critical information about the utero-ovarian circulation. Future work should concentrate on changing the existing solver into a much faster and simpler solver, as well as validating the biomarkers in a larger dataset
- …