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Materials discovery using machine learning based on materials database and informatics
No full textThe discovery of new materials has been pivotal to technological advancements but is currently bottlenecked by the experimental validation of proposed hypothetical compounds. There are several obstacles to realizing the full potential of data-driven approaches, namely 1) identification of synthesizable materials 2) availability of high-quality data, and 3) omitted practical consideration.
To address these challenges, a dataset on solid-state synthesis of ternary metal oxides was manually curated. This data offers information on whether materials have been solid-state synthesized in the literature, along with some important synthesis conditions. We then applied this dataset in various ways that can help accelerate materials discovery. We first showed that a small set of high-quality data can be used to identify outliers in text-mined datasets. We then trained a transductive positive-unlabeled learning model to predict the solid-state synthesizability of the ternary metal oxides. Compared to previous studies that predict the general synthesizability, the model offers information on false positive rates.
In chapter 4, we trained models using different datasets and features to predict solid-state heating temperature and atmosphere. Comparison of models performance shows that the best overall models were trained using a combination of the two datasets. We also demonstrated that precursor features generated using high-quality data can eliminate the need for precursor information from other sources that are scattered, non-uniform, or incomplete.
In chapter 5, we conducted a screening study that incorporated precursor hazards as a criteria. Precursor hazards are often neglected during materials screening but are important for synthesis planning, lab workers’ safety, and environmental sustainability.
Overall, the work in this thesis provides a set of high-quality datasets to supplement text-mined datasets for solid-state synthesis by illustrating approaches to select hypothetical compositions, offering advice in choosing solid-state synthesis conditions, and raising awareness of practicalities when applying data-driven approaches.Open Acces
Electroencephalographic decoding of sound location: comparing free-field to headphone-based non-individual head-related transfer functions
No full textSound source localization relies on spatial cues, such as interaural time differences, interaural level differences, and monaural spectral cues. Individually measured head-related transfer functions (HRTFs) facilitate precise spatial hearing but are impractical to measure, necessitating non-individual HRTFs, which may compromise localization accuracy and externalization. To further investigate this phenomenon, the neurophysiological differences between free-field and non-individual HRTF listening are explored by decoding sound locations from EEG-derived event-related potentials. Twenty-two participants localized stimuli under both conditions with EEG responses recorded and logistic regression classifiers trained to distinguish sound source locations. Lower cortical response amplitudes were observed for KEMAR compared to free-field, especially in front-central and occipital-parietal regions. ANOVA identified significant main effects of auralization condition and location on decoding accuracy (DA), which was higher in free-field and interaural-cue-dominated locations. DA negatively correlated with front-back confusion rates, linking neural DA to perceptual confusion. These findings demonstrate that headphone-based non-individual HRTFs elicit lower amplitude cortical responses to static, azimuthally varying locations than free-field conditions. The correlation between EEG-based DA and front-back confusion underscores neurophysiological markers' potential for assessing spatial auditory discrimination
MMD two-sample testing in the presence of arbitrarily missing data
No full textIn many real-world applications, it is common that a proportion of the data may be missing or only partially observed. We develop a novel two-sample testing method based on the Maximum Mean Discrepancy (MMD) which accounts for missing data in both samples, without making assumptions about the missingness mechanism. Our approach is based on deriving the mathematically precise bounds of the MMD test statistic after accounting for all possible missing values. To the best of our knowledge, it is the only two-sample testing method that is guaranteed to control the Type I error for both univariate and multivariate data where data may be arbitrarily missing. Simulation results show that the method has good statistical power, typically for cases where 5% to 10% of the data are missing. We highlight the value of this approach when the data are missing not at random, a context in which either ignoring the missing values or using common imputation methods may not control the Type I error
Cohort profile for the Heat in Pregnancy- India (HiP-India) study [version 2; peer review: 1 approved]
No full textBackground:
Extreme heat exposure — defined as sustained ambient temperatures exceeding local thresholds — has been associated with several adverse pregnancy outcomes, including preterm birth, stillbirth, gestational diabetes and small for gestational age. However, the mechanisms linking environmental heat to these outcomes, and the biological markers that signify individual vulnerability, are not well understood. We present the protocol for a prospective cohort study within the Heat in Pregnancy-India consortium (HiP-India). This study aims to characterise the physiological and pathophysiological responses of the mother, placenta, and fetus to varying levels of temperature, humidity, and air pollution exposure, and to identify the critical windows and mechanisms of heat-related risk during pregnancy.
Methods:
600 women with singleton pregnancies, with confirmed gestational age by ultrasound between 11–14 weeks, will be recruited prospectively from three distinct climate zones in India: Gurugram, Delhi NCR ‘semi-arid’; Bilaspur, Chhattisgarh ‘humid sub-tropical and tropical wet and dry’; and Puducherry ‘tropical wet and dry’. Each participant will have their level of exposure to heat, humidity and air pollution measured for 24 hours each trimester in their home and/or workplace using individual and area monitoring devices. Perceived heat stress will be captured using a modified HOTHAPS questionnaire, while physiological heat strain will be measured through urinary specific gravity, core body temperature, heart rate and blood pressure. Within 48 hours of environmental monitoring, maternal haemodynamic parameters will be assessed non-invasively. Fetal ultrasound will be performed to evaluate growth and fetal-placental blood flow, and maternal blood samples collected to evaluate circulating biomarkers of placental function and stress. Cardiotocography will be conducted in the third trimester only. Delivery outcomes for both mothers and neonates will be extracted from hospital records and interviews. In a subset of 100 women, markers of lactation physiology will be recorded during the first 2 weeks after delivery.
Ethics and dissemination of results:
All necessary ethical approvals from relevant committees at participating institutions have been obtained. Written informed consent will be obtained from all participants. The findings from this study are expected to inform climate adaptation strategies and emergency response policies to protect pregnant populations from the impacts of extreme heat, both within India and in other similarly affected regions globally. Results are aimed for journal publication, communicate findings to participants in plain language, disseminating information at conferences and events of similar nature
Physics-guided ultrasonic framework for mapping SOC heterogeneity throughout battery thickness
No full textReproducible safety and efficacy of durvalumab with or without tremelimumab for hepatocellular carcinoma in clinical practice: Results of the DT-real study
No full textBackground & Aims
Durvalumab plus tremelimumab (STRIDE) has emerged as a first-line systemic treatment option for unresectable hepatocellular carcinoma (HCC). This international multicentre study aimed to evaluate the efficacy and tolerability of STRIDE or durvalumab monotherapy in routine clinical practice, comparing outcomes between patients within and outside key eligibility criteria for the HIMALAYA trial.
Methods
From a database of 1,423 patients with advanced/unresectable HCC treated with immunotherapy across 35 centres, we analysed 233 patients receiving STRIDE or durvalumab monotherapy. Patients were categorized as HIMALAYA-IN or HIMALAYA-OUT based on key trial eligibility criteria (no prior systemic therapy, ECOG-PS 0–1, Child-Pugh class A, no Vp4 thrombosis). Baseline characteristics were assessed for overall survival (OS) and hepatic decompensation using a multivariable Cox model and competing-risk analysis, respectively. Objective response rates and treatment-related adverse events were recorded.
Results
Of the 233 patients, 123 (53%) were HIMALAYA-IN and 110 (47%) were HIMALAYA-OUT. STRIDE was given in 95% of HIMALAYA-IN patients. After median follow-up of 6.0 months, median OS was 20.4 months (95% CI 11.7-NR) in the overall population. HIMALAYA-IN patients achieved significantly longer OS than HIMALAYA-OUT patients (23.0 vs. 12.2 months; hazard ratio 0.61; 95% CI 0.39-0.96; p = 0.03). Macrovascular invasion and hepatic decompensation were independent negative prognostic factors in the whole cohort. Hepatic decompensation occurred in 10.5% of patients within 12 months from treatment start. Objective response rate was 23.7% and 17.8% of HIMALAYA-IN and -OUT patients, respectively. Patients achieving disease control (whole cohort: 59.4%) demonstrated 24-month OS of 58.2% in HIMALAYA-IN and 44.8% in HIMALAYA-OUT groups. Grade 3-4 treatment-related adverse events occurred in 16.3% of patients.
Conclusions
STRIDE shows reproducible effectiveness and an acceptable safety profile in real-world practice. Achieving disease control and maintaining liver function emerged as key determinants of long-term survival benefit.
Impact and implications
The DT-real study validates the efficacy and safety of STRIDE (durvalumab plus tremelimumab) in routine clinical practice, with HIMALAYA trial-eligible patients achieving 23-month median survival, and showing a safety profile comparable to that observed in the pivotal trial. Nearly half of real-world patients received treatment despite not meeting original trial criteria, reflecting urgent clinical need in this population with limited therapeutic options. As for other immunotherapy-based combinations, hepatic decompensation is a critical determinant of survival. Patients achieving disease control demonstrated substantially improved 24-month overall survival rates compared to those with progressive disease, confirming the findings of the exploratory analyses of the HIMALAYA trial
General Practice community engagement approaches to tackle health inequalities (HiQUALITY) study
No full textThis report summarises key findings of the HiQUALITY (Health Inequality) Study commissioned by the NHSE Legacy and Health Equity Partnership (LHEP). The project was a partnership of LHEP, UKHSA London, NHSE London, and SCARU Imperial over a 6-month timeline (Sep 23-Mar 24).
The COVID-19 pandemic and cost of living crisis exacerbated existing health inequalities amongst London's diverse communities. Community engagement has been an important aspect of the multisectoral approach towards tackling these health inequalities, and General Practices (GPs) and Primary Care Networks (PCNs) have contributed to this work. A great deal of community engagement activity is already occurring in General Practice, however scoping conversations with leaders in this area had suggested that exploration of a baseline of activity and an understanding of need would be beneficial to inform possible next steps for community engagement in General Practice.
The HiQUALITY Study aimed to characterise variations in community engagement approaches by GPs/PCNs across London, exploring reasons behind these variations, perceived outcomes, best practice and what support is needed to sustain community engagement to reduce health inequalities
Exploring the potential of hybrid concentrated photovoltaic-thermoelectric systems: a multivariate approach to performance and material optimization
No full textPreprint versio
How can energy-system models inform technology development? Lessons learned for emerging energy-storage technologies
No full textEnergy-system models (ESMs) are used often to support policymakers, system operators, or investors, but much less for offering guidance to technology developers. This paper contributes to bridging the gap between the ESM discipline and technology developers. For the example of emerging energy-storage technologies, we identify and categorise information needs of technology developers. Moreover, we discuss, which information needs can be met by an advanced analysis of ESM results, and which needs require fundamental model developments. We demonstrate the capabilities of an advanced analysis for an application study using a model that optimises investment and dispatch to assess energy-storage technology requirements in a fully decarbonised European power system. Our analysis provides insights regarding requirements and opportunities of energy-storage technologies in terms of design parameters, operational patterns, and target markets. We show that technologies with low-cost power, e.g., lithium-ion batteries (LIB), are designed with low energy-to-power (E2P) ratios, while those with low-cost energy (e.g., H) have high E2P ratios. Concerning operational patterns, low-E2P energy-storage technologies cycle frequently (up to daily for LIB and vanadium-redox flow batteries), whereas H featuring a high-E2P cycles only a few times per year. Moreover, we find that requirements and cycling frequencies vary strongly between different target markets driven by their underlying electricity systems. We conclude that an advanced analysis can make a contribution to bridging the gap between ESMs and technology developers. Future work should improve the representation of technological details and develop inverse modelling approaches for technologies in very early development stages with still highly uncertain parameters