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1. Accessing Numerical Energy Hessians with Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis

Author

Wander, Brook, Musielewicz, Joseph, Cheula, Raffaele, and Kitchin, John R.

Subjects
Condensed Matter - Materials Science, Physics - Computational Physics
Abstract

Access to the potential energy Hessian enables determination of the Gibbs free energy, and certain approaches to transition state search and optimization. Here, we demonstrate that off-the-shelf pretrained Open Catalyst Project (OCP) machine learned potentials (MLPs) determine the Hessian with great success (58 cm$^{-1}$ mean absolute error (MAE)) for intermediates adsorbed to heterogeneous catalyst surfaces. This enables the use of OCP models for the aforementioned applications. The top performing model, with a simple offset correction, gives good estimations of the vibrational entropy contribution to the Gibbs free energy with an MAE of 0.042 eV at 300 K. The ability to leverage models to capture the translational entropy was also explored. It was determined that 94% of randomly sampled systems had a translational entropy greater than 0.1 eV at 300 K. This underscores the need to go beyond the harmonic approximation to consider the entropy introduced by adsorbate translation, which increases with temperature. Lastly, we used MLP determined Hessian information for transition state search and found we were able to reduce the number of unconverged systems by 65% to 93% overall convergence, improving on the baseline established by CatTSunami., Comment: 43 pages, 8 figures

Published
2024

2. Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances

Author

Musielewicz, Joseph, Lan, Janice, Uyttendaele, Matt, and Kitchin, John R.

Subjects
Computer Science - Machine Learning, Condensed Matter - Materials Science
Abstract

Graph neural networks (GNNs) have been shown to be astonishingly capable models for molecular property prediction, particularly as surrogates for expensive density functional theory calculations of relaxed energy for novel material discovery. However, one limitation of GNNs in this context is the lack of useful uncertainty prediction methods, as this is critical to the material discovery pipeline. In this work, we show that uncertainty quantification for relaxed energy calculations is more complex than uncertainty quantification for other kinds of molecular property prediction, due to the effect that structure optimizations have on the error distribution. We propose that distribution-free techniques are more useful tools for assessing calibration, recalibrating, and developing uncertainty prediction methods for GNNs performing relaxed energy calculations. We also develop a relaxed energy task for evaluating uncertainty methods for equivariant GNNs, based on distribution-free recalibration and using the Open Catalyst Project dataset. We benchmark a set of popular uncertainty prediction methods on this task, and show that latent distance methods, with our novel improvements, are the most well-calibrated and economical approach for relaxed energy calculations. Finally, we demonstrate that our latent space distance method produces results which align with our expectations on a clustering example, and on specific equation of state and adsorbate coverage examples from outside the training dataset.

Published
2024

3. The Social Ecology of Academic Achievement: Modeling Social Sources of Protection

Author

Jannessa L. Kitchin and Nancy J. Karlin

Abstract

The academic achievement gap in the USA persists, despite significant effort made to close it. This has led many researchers to investigate individual and social characteristics that reliably predict academic success in the face of adversity. Structural equation modeling was used to investigate six models with the aim of clarifying the interrelationships between dispositional, social, and school-based social protective factors and academic achievement with 535 college undergraduate students (mean age = 22.45). Determination, self-esteem, self-concept, and connection to school were found to have a positive relationship to academic achievement. Inconsistent with a large body of past research, several negative relationships were found in models involving self-esteem and self-concept between protective factors. Significant, cross-system, relationships were evident for protective factors. Results of this study provide support for the use of the social ecological model of resilience in academic achievement research so that interventions can be developed to help all students succeed.

Published
2024
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4. AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion

Author

Kolluru, Adeesh and Kitchin, John R

Subjects
Computer Science - Machine Learning, Physics - Chemical Physics
Abstract

Determining the optimal configuration of adsorbates on a slab (adslab) is pivotal in the exploration of novel catalysts across diverse applications. Traditionally, the quest for the lowest energy adslab configuration involves placing the adsorbate onto the slab followed by an optimization process. Prior methodologies have relied on heuristics, problem-specific intuitions, or brute-force approaches to guide adsorbate placement. In this work, we propose a novel framework for adsorbate placement using denoising diffusion. The model is designed to predict the optimal adsorbate site and orientation corresponding to the lowest energy configuration. Further, we have an end-to-end evaluation framework where diffusion-predicted adslab configuration is optimized with a pretrained machine learning force field and finally evaluated with Density Functional Theory (DFT). Our findings demonstrate an acceleration of up to 5x or 3.5x improvement in accuracy compared to the previous best approach. Given the novelty of this framework and application, we provide insights into the impact of pre-training, model architectures, and conduct extensive experiments to underscore the significance of this approach., Comment: 8 pages, 7 figures, ICML 2024

Published
2024

5. CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks

Author

Wander, Brook, Shuaibi, Muhammed, Kitchin, John R., Ulissi, Zachary W., and Zitnick, C. Lawrence

Subjects
Condensed Matter - Materials Science
Abstract

Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural network potential trained on the OC20 dataset, a related but different task, is able to find transition states energetically similar (within 0.1 eV) to density functional theory (DFT) 91% of the time with a 28x speedup. This speaks to the generalizability of the models, having never been explicitly trained on reactions, the machine learned potential approximates the potential energy surface well enough to be performant for this auxiliary task. We introduce the Open Catalyst 2020 Nudged Elastic Band (OC20NEB) dataset, which is made of 932 DFT nudged elastic band calculations, to benchmark machine learned model performance on transition state energies. To demonstrate the efficacy of this approach, we replicated a well-known, large reaction network with 61 intermediates and 174 dissociation reactions at DFT resolution (40 meV). In this case of dense NEB enumeration, we realize even more computational cost savings and used just 12 GPU days of compute, where DFT would have taken 52 GPU years, a 1500x speedup. Similar searches for complete reaction networks could become routine using the approach presented here. Finally, we replicated an ammonia synthesis activity volcano and systematically found lower energy configurations of the transition states and intermediates on six stepped unary surfaces. This scalable approach offers a more complete treatment of configurational space to improve and accelerate catalyst discovery., Comment: 50 pages, 15 figures, submitted to Nature Catalysis

Published
2024

6. Serum Troponin I Assessments in 5- to 30-Year-Olds After BNT162b2 Vaccination.

Author

Albertson, Timothy, Hansen, Caitlin, Bihari, Smiti, Gayed, Juleen, Xu, Xia, Simón-Campos, J, Dever, Michael, Cardona, Jose, Mitha, Essack, Baker, Jeffrey, Keep, Georgina, Oladipupo, Islamiat, Mensa, Federico, Feng, Ye, Ma, Hua, Koury, Kenneth, Mather, Susan, Ianos, Claudia, Anderson, Annaliesa, Türeci, Özlem, Şahin, Uǧur, Gruber, William, Gurtman, Alejandra, Sabharwal, Charu, and Kitchin, Nicholas

Subjects
BNT162b2, COVID-19, Clinical trials, Myocarditis, Pericarditis, Safety, Troponin I, Vaccines
Abstract

INTRODUCTION: Rare myocarditis and pericarditis cases have occurred in coronavirus disease 2019 (COVID-19) messenger RNA (mRNA) vaccine recipients. Troponin levels, a potential marker of myocardial injury, were assessed in healthy participants before and after BNT162b2 vaccination. METHODS: Vaccine-experienced 12- to 30-year-olds in phase 3 crossover C4591031 Substudy B (NCT04955626) who had two or three prior BNT162b2 30-μg doses were randomized to receive BNT162b2 30 μg followed by placebo, or placebo followed by BNT162b2 30 µg, 1 month apart. A participant subset, previously unvaccinated against COVID-19, in the phase 3 C4591007 study (NCT04816643) received up to three vaccinations (BNT162b2 10 μg or placebo [5- to 11-year-olds]) or open-label BNT162b2 30 μg (12- to 15-year-olds). Blood samples collected pre-vaccination, 4 days post-vaccination, and 1-month post-vaccination (C4591031 Substudy B only) were analyzed. Frequencies of elevated troponin I levels (male, > 35 ng/l; female, > 17 ng/l) were assessed. RESULTS: Percentages of 12- to 30-year-olds (n = 1485) in C4591031 Substudy B with elevated troponin levels following BNT162b2 or placebo receipt were 0.5% and 0.8% before vaccination, 0.7% and 1.0% at day 4, and 0.7% and 0.5% at 1 month, respectively. In Study C4591007 (n = 1265), elevated troponin I levels were observed in 0.2, 0.4, and 0.2% of 5- to 11-year-old BNT162b2 recipients at baseline and 4 days post-dose 2 and 3, respectively; corresponding values in 12- to 15-year-olds were 0.4, 0.4, and 0.7%. No 5- to 11-year-old placebo recipients had elevated troponin levels. No myocarditis or pericarditis cases or deaths were reported. CONCLUSIONS: Among 5- to

Published
2024

8. Generalization of Graph-Based Active Learning Relaxation Strategies Across Materials

Author

Wang, Xiaoxiao, Musielewicz, Joseph, Tran, Richard, Ethirajan, Sudheesh Kumar, Fu, Xiaoyan, Mera, Hilda, Kitchin, John R., Kurchin, Rachel C., and Ulissi, Zachary W.

Subjects
Condensed Matter - Materials Science
Abstract

Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput efforts. This has prompted an explosion in exploration of machine learning assisted techniques to improve the computational efficiency of DFT. In this study, we present a comprehensive investigation of the broader application of Finetuna, an active learning framework to accelerate structural relaxation in DFT with prior information from Open Catalyst Project pretrained graph neural networks. We explore the challenges associated with out-of-domain systems: alcohol ($C_{>2}$) on metal surfaces as larger adsorbates, metal-oxides with spin polarization, and three-dimensional (3D) structures like zeolites and metal-organic-frameworks. By pre-training machine learning models on large datasets and fine-tuning the model along the simulation, we demonstrate the framework's ability to conduct relaxations with fewer DFT calculations. Depending on the similarity of the test systems to the training systems, a more conservative querying strategy is applied. Our best-performing Finetuna strategy reduces the number of DFT single-point calculations by 80% for alcohols and 3D structures, and 42% for oxide systems.

Published
2023

9. From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Author

Shoghi, Nima, Kolluru, Adeesh, Kitchin, John R., Ulissi, Zachary W., Zitnick, C. Lawrence, and Wood, Brandon M.

Subjects
Computer Science - Machine Learning
Abstract

Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains. To address this, we introduce Joint Multi-domain Pre-training (JMP), a supervised pre-training strategy that simultaneously trains on multiple datasets from different chemical domains, treating each dataset as a unique pre-training task within a multi-task framework. Our combined training dataset consists of $\sim$120M systems from OC20, OC22, ANI-1x, and Transition-1x. We evaluate performance and generalization by fine-tuning over a diverse set of downstream tasks and datasets including: QM9, rMD17, MatBench, QMOF, SPICE, and MD22. JMP demonstrates an average improvement of 59% over training from scratch, and matches or sets state-of-the-art on 34 out of 40 tasks. Our work highlights the potential of pre-training strategies that utilize diverse data to advance property prediction across chemical domains, especially for low-data tasks. Please visit https://nima.sh/jmp for further information.

Published
2023

10. Chemical Properties from Graph Neural Network-Predicted Electron Densities

Author

Sunshine, Ethan M., Shuaibi, Muhammed, Ulissi, Zachary W., and Kitchin, John R.

Subjects
Condensed Matter - Materials Science
Abstract

According to density functional theory, any chemical property can be inferred from the electron density, making it the most informative attribute of an atomic structure. In this work, we demonstrate the use of established physical methods to obtain important chemical properties from model-predicted electron densities. We introduce graph neural network architectural choices that provide physically relevant and useful electron density predictions. Despite not training to predict atomic charges, the model is able to predict atomic charges with an order of magnitude lower error than a sum of atomic charge densities. Similarly, the model predicts dipole moments with half the error of the sum of atomic charge densities method. We demonstrate that larger data sets lead to more useful predictions in these tasks. These results pave the way for an alternative path in atomistic machine learning, where data-driven approaches and existing physical methods are used in tandem to obtain a variety of chemical properties in an explainable and self-consistent manner.

Published
2023

11. Subtracting the background by reducing cell-free DNA’s confounding effects on Mycobacterium tuberculosis quantitation and the sputum microbiome

Author

Charissa C. Naidoo, Rouxjeane Venter, Francesc Codony, Gemma Agustí, Natasha Kitchin, Selisha Naidoo, Hilary Monaco, Hridesh Mishra, Yonghua Li, Jose C. Clemente, Robin M. Warren, Leopoldo N. Segal, and Grant Theron

Subjects
PEMAX, Propidium monoazide, DNaseI, Mycobacterium tuberculosis, Xpert MTB/RIF, Sputum microbiome, Medicine, Science
Abstract

Abstract DNA characterisation in people with tuberculosis (TB) is critical for diagnostic and microbiome evaluations. However, extracellular DNA, more frequent in people on chemotherapy, confounds results. We evaluated whether nucleic acid dyes [propidium monoazide (PMA), PEMAX] and DNaseI could reduce this. PCR [16S Mycobacterium tuberculosis complex (Mtb) qPCR, Xpert MTB/RIF] was done on dilution series of untreated and treated (PMA, PEMAX, DNaseI) Mtb. Separately, 16S rRNA gene qPCR and sequencing were done on untreated and treated sputa before (Cohort A: 11 TB-negatives, 9 TB-positives; Cohort B: 19 TB-positives, PEMAX only) and 24-weeks after chemotherapy (Cohort B). PMA and PEMAX reduced PCR-detected Mtb DNA for dilution series and Cohort A sputum versus untreated controls, suggesting non-intact Mtb is present before treatment-start. PEMAX enabled sequencing-based Mycobacterium-detection in 7/12 (58%) TB-positive sputa where no such reads otherwise occurred. In Cohort A, PMA- and PEMAX-treated versus untreated sputa had decreased α- and increased β-diversities. In Cohort B, β-diversity differences between timepoints were only detected with PEMAX. DNaseI had negligible effects. PMA and PEMAX (but not DNaseI) reduced extracellular DNA in PCR and improved pathogen detection by sequencing. PEMAX additionally detected chemotherapy-associated taxonomic changes that would otherwise be missed. Dyes enhance microbiome evaluations especially during chemotherapy.

Published
2024
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12. Understanding mediated sports consumption by Irish children: a qualitative study exploring their exposure and understanding of gambling marketing, risks and harms

Author

Paul James Kitchin, Erin McEvoy, Aphra Kerr, and John O’Brennan

Subjects
Gambling, Gambling marketing, Sports media, Social media, Children, Public health, Public aspects of medicine, RA1-1270
Abstract

Abstract Background Gambling marketing communications create a public health risk by increasing the normalisation of gambling in sports. In a context where broad level studies report significant underage gambling, currently no evidence exists on how these communications are received by children in the Republic of Ireland (hereafter Ireland) and Northern Ireland. To build this evidence base and provide granular detail below broad level data, this study explored the sport consumption habits and views of Irish children (aged 14–17 years) on their exposure, awareness and perceptions of the relationship between sport and gambling. Methods Drawing on a constructivist approach to data collection, 6 face-to-face focus groups were staged with a total of 51 youth sport consumers from both sides of the border region on the island of Ireland. Results Four main themes were established. First, mediated sport consumption was predominantly via mobile social media. Second, we found that their exposure to gambling marketing was high and while frequently seen through social media channels it was also prevalent in peer-to-peer conversations and on the main street. Third, we found mixed responses on their perceptions about gambling and sport. While many felt that sport and gambling were a good fit, they were aware of the financial risks involved. Few children understood the wider social risks with gambling harm. Fourth, children were sceptical of claims made in gambling communications. These findings highlight concerns about how exposure to gambling marketing is impacting children’s views on sport and on gambling. These views need to be taken into consideration when broadcasters and sport organizations are entering into commercial associations. Conclusions Gambling marketing is noticed by children watching mass and social media, and in the towns in the border regions of the island of Ireland. Our study provides children’s viewpoints on this topic which complements the larger quantitative studies in Ireland and Northern Ireland that highlight the growing prevalence of children and gambling. This study not only extends the literature on the exposure, awareness and perceptions of children on the island of Ireland but provides crucial evidence to public health advocates in this region demonstrating the pervasiveness of gambling communications in and around children’s spaces.

Published
2024
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13. The Impacts of Select State-Level Policies on Increasing the Educational Attainment of Undocumented Immigrant Youth

Author

James C. Kitchin

Abstract

Undocumented children have a right to a public k-12 education in the United States, however there is no standard approach to higher education. About half of states allow undocumented immigrants to attend public colleges and universities at in-state rates of tuition (ISRT) and a subset of these also allow them access to state financial aid. This dissertation measures the impacts of these state-level policies that attempt to increase access to higher education for this group and studies the relationship between ISRT policies and college counseling of undocumented students at the high school level. The dissertation starts by measuring the impact of ISRT policies, financial aid policies, and DACA on a variety of educational and employment outcomes for undocumented immigrant youth. It uses Current Population Survey basic monthly data from July 1999-December 2015. A difference-in-differences research design is operationalized through a standard linear regression two-way fixed-effects model. The findings include a 4-percentage point increase in college attendance and a 2-percentage point increase in college graduation due to the implementation of ISRT policies. The dissertation also finds that granting access to state financial aid can double the impact of the ISRT policy. DACA serves to decrease the college enrollment and increase the employment of undocumented immigrant youth. The dissertation then reacts to emerging literature in the field of econometrics that critiques the difference-in-differences research design operationalized through the standard linear regression two-way fixed-effects model when there are multiple groups that adopt the treatment at different times. It uses the American Community Survey from 2001-2019 and the Census 2000 1% sample to apply a newer quantitative method, the Calloway-Sant' Anna difference-in-differences estimator. Even with a new, more robust quantitative method ISRT policies increase the college enrollment of undocumented immigrants by, on average, 3 percentage points. They increase college graduation rates by, on average, 1 percentage point. However, there is a lot of heterogeneity present in the results that have large policy implications. This is explored in depth. Lastly, the dissertation uses the High School Longitudinal Study of 2009 to perform a Latent Class Analysis that identifies three typologies of college counseling opportunity structures in U.S. high schools. It then uses regression analysis to explore the relationship between attending high school in a state with an ISRT policy and the rate that high school counselors and undocumented immigrant students speak about college in the ninth grade. States with an ISRT policy have a smaller percentage of high schools with counseling opportunity structures that encourage college going than states without an ISRT policy. Similarly, students who are undocumented are the least likely to attend schools with counseling opportunity structures that prioritize college. Even so, high school counselors and undocumented students in the ninth grade are more likely to speak about college than are high school counselors and documented or nonimmigrant ninth graders. However, this is only the case in states that have adopted an ISRT policy. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Published
2024

18. Beyond Independent Error Assumptions in Large GNN Atomistic Models

Author

Ock, Janghoon, Tian, Tian, Kitchin, John, and Ulissi, Zachary

Subjects
Physics - Computational Physics, Condensed Matter - Materials Science
Abstract

The practical applications of determining the relative difference in adsorption energies are extensive, such as identifying optimal catalysts, calculating reaction energies, and determining the lowest adsorption energy on a catalytic surface. Although Density Functional Theory (DFT) can effectively calculate relative values through systematic error cancellation, the accuracy of Graph Neural Networks (GNNs) in this regard remains uncertain. To investigate this issue, we analyzed approximately 483 million pairs of energy differences predicted by DFT and GNNs using the Open Catalyst 2020 - Dense dataset. Our analysis revealed that GNNs exhibit a correlated error that can be reduced through subtraction, thereby challenging the naive independent error assumption in GNN predictions and leading to more precise energy difference predictions. To assess the magnitude of error cancellation in chemically similar pairs, we introduced a new metric, the subgroup error cancellation ratio (SECR). Our findings suggest that state-of-the-art GNN models can achieve error reduction up to 77% in these subgroups, comparable to the level of error cancellation observed with DFT. This significant error cancellation allows GNNs to achieve higher accuracy than individual adsorption energy predictions, which can otherwise suffer from amplified error due to random error propagation.

Published
2023
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19. Short-read full-length 16S rRNA amplicon sequencing for characterisation of the respiratory bacteriome of captive and free-ranging African elephants (Loxodonta africana)

Author

Lauren C. Martin, Michaela A. O’Hare, Giovanni Ghielmetti, David Twesigomwe, Tanya J. Kerr, Rachiel Gumbo, Peter E. Buss, Natasha Kitchin, Sian M. J. Hemmings, Michele A. Miller, and Wynand J. Goosen

Subjects
16S rRNA sequencing, Respiratory microbiome, Elephant microbiome, iSeq 100, Medicine, Science
Abstract

Abstract Hypervariable region sequencing of the 16S ribosomal RNA (rRNA) gene plays a critical role in microbial ecology by offering insights into bacterial communities within specific niches. While providing valuable genus-level information, its reliance on data from targeted genetic regions limits its overall utility. Recent advances in sequencing technologies have enabled characterisation of the full-length 16S rRNA gene, enhancing species-level classification. Although current short-read platforms are cost-effective and precise, they lack full-length 16S rRNA amplicon sequencing capability. This study aimed to evaluate the feasibility of a modified 150 bp paired-end full-length 16S rRNA amplicon short-read sequencing technique on the Illumina iSeq 100 and 16S rRNA amplicon assembly workflow by utilising a standard mock microbial community and subsequently performing exploratory characterisation of captive (zoo) and free-ranging African elephant (Loxodonta africana) respiratory microbiota. Our findings demonstrate that, despite generating assembled amplicons averaging 869 bp in length, this sequencing technique provides taxonomic assignments consistent with the theoretical composition of the mock community and respiratory microbiota of other mammals. Tentative bacterial signatures, potentially representing distinct respiratory tract compartments (trunk and lower respiratory tract) were visually identified, necessitating further investigation to gain deeper insights into their implication for elephant physiology and health.

Published
2024
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20. Evaluating the antibody response elicited by diverse HIV envelope immunogens in the African green monkey (Vervet) model

Author

Thandeka Moyo-Gwete, Frances Ayres, Nonkululeko B. Mzindle, Zanele Makhado, Nelia P. Manamela, Simone I. Richardson, Dale Kitchin, Strauss van Graan, Joritha van Heerden, Nishal Parbhoo, Gerald K. Chege, and Penny L. Moore

Subjects
Medicine, Science
Abstract

Abstract African Green (Vervet) monkeys have been extensively studied to understand the pathogenesis of infectious diseases. Using vervet monkeys as pre-clinical models may be an attractive option for low-resourced areas as they are found abundantly and their maintenance is more cost-effective than bigger primates such as rhesus macaques. We assessed the feasibility of using vervet monkeys as animal models to examine the immunogenicity of HIV envelope trimer immunogens in pre-clinical testing. Three groups of vervet monkeys were subcutaneously immunized with either the BG505 SOSIP.664 trimer, a novel subtype C SOSIP.664 trimer, CAP255, or a combination of BG505, CAP255 and CAP256.SU SOSIP.664 trimers. All groups of vervet monkeys developed robust binding antibodies by the second immunization with the peak antibody response occurring after the third immunization. Similar to binding, antibody dependent cellular phagocytosis was also observed in all the monkeys. While all animals developed potent, heterologous Tier 1 neutralizing antibody responses, autologous neutralization was limited with only half of the animals in each group developing responses to their vaccine-matched pseudovirus. These data suggest that the vervet monkey model may yield distinct antibody responses compared to other models. Further study is required to further determine the utility of this model in HIV immunization studies.

Published
2024
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21. The Boundary and Overlap with Therapy in Executive Coaching – A Study Using Q Methodology

Author

Leoni Kitchin

Subjects
executive coaching, coaching, therapy, q methodology, boundaries, Special aspects of education, LC8-6691, Industrial psychology, HF5548.7-5548.85
Abstract

Most executive coaches are not therapeutically trained and there is concern that they might have difficulty identifying and managing boundaries with therapy. This study explores how executive coaches from a non-psychology background experience the boundary. Using Q methodology, coaches ranked statements representing the range of responses to working with the boundary and overlap, leading to a proposed typology of how practitioners perceive and work in this space. Four types of practitioners were identified: Conscious, Confused, Conscientious and Confident. The typology offers executive coaching stakeholders a framework for reflection and discussion to further consider the issue in practice.

Published
2024
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22. WhereWulff: A semi-autonomous workflow for systematic catalyst surface reactivity under reaction conditions

Author

Sanspeur, Rohan Yuri, Heras-Domingo, Javier, Kitchin, John R., and Ulissi, Zachary

Subjects
Condensed Matter - Materials Science
Abstract

This paper introduces WhereWulff, a semi-autonomous workflow for modeling the reactivity of catalyst surfaces. The workflow begins with a bulk optimization task that takes an initial bulk structure, and returns the optimized bulk geometry and magnetic state, including stability under reaction conditions. The stable bulk structure is the input to a surface chemistry task that enumerates surfaces up to a user-specified maximum Miller index, computes relaxed surface energies for those surfaces, and then prioritizes those for subsequent adsorption energy calculations based on their contribution to the Wulff construction shape. The workflow handles computational resource constraints such as limited wall-time as well as automated job submission and analysis. We illustrate the workflow for oxygen evolution (OER) intermediates on two double perovskites. WhereWulff nearly halved the number of Density Functional Theory (DFT) calculations from ~ 240 to ~ 132 by prioritizing terminations, up to a maximum Miller index of 1, based on surface stability. Additionally, it automatically handled the 180 additional re-submission jobs required to successfully converge 120+ atoms systems under a 48-hour wall-time cluster constraint. There are four main use cases that we envision for WhereWulff: (1) as a first-principles source of truth to validate and update a closed-loop self-sustaining materials discovery pipeline, (2) as a data generation tool, (3) as an educational tool, allowing users (e.g. experimentalists) unfamiliar with OER modeling to probe materials they might be interested in before doing further in-domain analyses, (4) and finally as a starting point for users to extend with reactions other than OER, as part of a collaborative software community.

Published
2023

23. Biomedical research grant resubmission: rates and factors related to success – a scoping review

Author

Clare L Ardern, David Moher, Karim Khan, Vanessa Kitchin, Hassan Khan, Anne M Lasinsky, and James Wrightson

Subjects
Medicine
Abstract

Objectives Most first-time biomedical research grant applications are not funded. In the challenging research funding climate, resubmitting a grant application is a necessary task for scientists. Identifying which factors influence their decision to resubmit and the success of resubmissions will inform funders and applicants. However, data on resubmissions are fragmented and under-reported. In this scoping review, we aimed to summarise (1) the outcomes of resubmitting biomedical research grant applications and (2) the demographic characteristics of scientists who resubmitted grant applications.Design Scoping review with reporting informed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses.Data sources MEDLINE, CINAHL, EMBASE, Cochrane Central Registrar of Controlled Trials CENTRAL, PsycINFO, Web of Science and grey literature sources were searched through November 2022.Eligibility criteria We included peer-reviewed and grey literature records from the biomedical sciences that reported outcomes of the resubmission process (eg, resubmission success rate, rate of resubmission) and information about the scientists who resubmit grant applications (eg, sex, race, career stage).Data extraction and synthesis Data were extracted independently by two reviewers. The data were cross-referenced and any conflicts were resolved via consensus. Data were summarised descriptively and presented in tables and figures.Results Resubmissions represented a substantial proportion of applications (lowest prevalence rate: 4%; highest prevalence rate: 56%) in a given funding cycle and were reliably more successful than first-time applications (lowest success rate: 16%; highest success rate: 82%)—a phenomenon associated with several sociodemographic, institutional and project-related factors. There was conflicting evidence about the relationship of sociodemographic-related, institution-related and project-related factors to resubmission likelihood and success.Conclusion The resubmission process is a time-consuming and often frustrating experience for researchers. Our review identified opportunities to streamline and improve the process to enhance the biomedical research landscape.

Published
2024
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24. Executive coaches' perceptions of the boundary and overlap between coaching and therapy : a study using Q methodology

Author

Kitchin, Leonora, Myers, Adrian, and Gannon, Judie

Abstract

Executive coaching of leaders and managers in the workplace is an established practice. Nonetheless, buyers and sellers of coaching still struggle to define the nature of coaching. One of the challenges in defining coaching is for the coach to be able to understand and manage boundaries between coaching and therapy. Most coaches are not therapeutically trained and there is concern that they might have difficulty identifying and managing boundaries. This study therefore set out to explore how executive coaches from a non-psychology background perceive and address what they consider to represent the boundary between coaching and therapy. Using Q methodology, twenty-six coaches were invited to rank order forty-eight statements to identify their viewpoints about how they perceived and expressed how they worked in areas of possible overlap between coaching and therapy. The study identified four viewpoints, which in turn led to the formulation of a proposed typology of how practitioners perceive and work at the boundary between coaching and therapy. Four types of practitioners were identified: Conscious, Confused, Conscientious and Confident. The study suggested that coaches held the view that they work within their competence but that a minority of coaches may not have a clear view of where the boundary is in their practice. The typology offers all stakeholders in the executive coaching market (coaches, coachees, sponsors, educators, supervisors and professional associations) a framework for discussion to further consider the boundary and overlap between coaching and therapy in practice.

Published
2023
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25. Serum Troponin I Assessments in 5- to 30-Year-Olds After BNT162b2 Vaccination

Author

Timothy E. Albertson, Caitlin Hansen, Smiti Bihari, Juleen Gayed, Xia Xu, J. Abraham Simón-Campos, Michael E. Dever, Jose F. Cardona, Essack Mitha, Jeffrey B. Baker, Georgina Keep, Islamiat Oladipupo, Federico J. Mensa, Ye Feng, Hua Ma, Kenneth Koury, Susan Mather, Claudia Ana Ianos, Annaliesa S. Anderson, Özlem Türeci, Uǧur Şahin, William C. Gruber, Alejandra Gurtman, Charu Sabharwal, Nicholas Kitchin, and the C4591031, C4591007 Clinical Trial Groups

Subjects
BNT162b2, Clinical trials, COVID-19, Myocarditis, Pericarditis, Safety, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Introduction Rare myocarditis and pericarditis cases have occurred in coronavirus disease 2019 (COVID-19) messenger RNA (mRNA) vaccine recipients. Troponin levels, a potential marker of myocardial injury, were assessed in healthy participants before and after BNT162b2 vaccination. Methods Vaccine-experienced 12- to 30-year-olds in phase 3 crossover C4591031 Substudy B (NCT04955626) who had two or three prior BNT162b2 30-μg doses were randomized to receive BNT162b2 30 μg followed by placebo, or placebo followed by BNT162b2 30 µg, 1 month apart. A participant subset, previously unvaccinated against COVID-19, in the phase 3 C4591007 study (NCT04816643) received up to three vaccinations (BNT162b2 10 μg or placebo [5- to 11-year-olds]) or open-label BNT162b2 30 μg (12- to 15-year-olds). Blood samples collected pre-vaccination, 4 days post-vaccination, and 1-month post-vaccination (C4591031 Substudy B only) were analyzed. Frequencies of elevated troponin I levels (male, > 35 ng/l; female, > 17 ng/l) were assessed. Results Percentages of 12- to 30-year-olds (n = 1485) in C4591031 Substudy B with elevated troponin levels following BNT162b2 or placebo receipt were 0.5% and 0.8% before vaccination, 0.7% and 1.0% at day 4, and 0.7% and 0.5% at 1 month, respectively. In Study C4591007 (n = 1265), elevated troponin I levels were observed in 0.2, 0.4, and 0.2% of 5- to 11-year-old BNT162b2 recipients at baseline and 4 days post-dose 2 and 3, respectively; corresponding values in 12- to 15-year-olds were 0.4, 0.4, and 0.7%. No 5- to 11-year-old placebo recipients had elevated troponin levels. No myocarditis or pericarditis cases or deaths were reported. Conclusions Among 5- to

Published
2024
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26. Reintroducing face-to-face support alongside remote support to form a hybrid stop smoking service in England: a formative mixed methods evaluation

Author

Nicholas Woodrow, Duncan Gillespie, Liz Kitchin, Mark O’Brien, Scott Chapman, Nai Rui Chng, Andrew Passey, Maria Raisa Jessica Aquino, Zoe Clarke, and Elizabeth Goyder

Subjects
Smoking cessation, Hybrid, Remote, Equity, Service reorganisation, Public aspects of medicine, RA1-1270
Abstract

Abstract Background During the COVID-19 pandemic, United Kingdom (UK) stop smoking services had to shift to remote delivery models due to social distancing regulations, later reintroducing face-to-face provision. The “Living Well Smokefree” service in North Yorkshire County Council adopted a hybrid model offering face-to-face, remote, or a mix of both. This evaluation aimed to assess the hybrid approach’s strengths and weaknesses and explore potential improvements. Methods Conducted from September 2022 to February 2023, the evaluation consisted of three components. First, qualitative interviews involved 11 staff and 16 service users, analysed thematically. Second, quantitative data from the QuitManager system that monitored the numbers and proportions of individuals selecting and successfully completing a 4-week quit via each service option. Third, face-to-face service expenses data was used to estimate the value for money of additional face-to-face provision. The qualitative findings were used to give context to the quantitative data via an “expansion” approach and complementary analysis. Results Overall, a hybrid model was seen to provide convenience and flexible options for support. In the evaluation, 733 individuals accessed the service, with 91.3% selecting remote support, 6.1% face-to-face, and 2.6% mixed provision. Remote support was valued by service users and staff for promoting openness, privacy, and reducing stigma, and was noted as removing access barriers and improving service availability. However, the absence of carbon monoxide monitoring in remote support raised accountability concerns. The trade-off in “quantity vs. quality” of quits was debated, as remote support reached more users but produced fewer carbon monoxide-validated quits. Primarily offering remote support could lead to substantial workloads, as staff often extend their roles to include social/mental health support, which was sometimes emotionally challenging. Offering service users a choice of support options was considered more important than the “cost-per-quit”. Improved dissemination of information to support service users in understanding their options for support was suggested. Conclusions The hybrid approach allows smoking cessation services to evaluate which groups benefit from remote, face-to-face, or mixed options and allocate resources accordingly. Providing choice, flexible provision, non-judgmental support, and clear information about available options could improve engagement and match support to individual needs, enhancing outcomes.

Published
2024
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27. Open Challenges in Developing Generalizable Large Scale Machine Learning Models for Catalyst Discovery

Author

Kolluru, Adeesh, Shuaibi, Muhammed, Palizhati, Aini, Shoghi, Nima, Das, Abhishek, Wood, Brandon, Zitnick, C. Lawrence, Kitchin, John R, and Ulissi, Zachary W

Subjects
Physics - Chemical Physics, Condensed Matter - Materials Science
Abstract

The development of machine learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While effective in interpolating between available materials, these approaches struggle to generalize across chemical space. The recent curation of large-scale catalyst datasets has offered the opportunity to build a universal machine learning potential, spanning chemical and composition space. If accomplished, said potential could accelerate the catalyst discovery process across a variety of applications (CO2 reduction, NH3 production, etc.) without additional specialized training efforts that are currently required. The release of the Open Catalyst 2020 (OC20) has begun just that, pushing the heterogeneous catalysis and machine learning communities towards building more accurate and robust models. In this perspective, we discuss some of the challenges and findings of recent developments on OC20. We examine the performance of current models across different materials and adsorbates to identify notably underperforming subsets. We then discuss some of the modeling efforts surrounding energy-conservation, approaches to finding and evaluating the local minima, and augmentation of off-equilibrium data. To complement the community's ongoing developments, we end with an outlook to some of the important challenges that have yet to be thoroughly explored for large-scale catalyst discovery., Comment: submitted to ACS Catalysis

Published
2022

29. Hyper-Dilaton Weyl Multiplet of 4D, ${\mathcal{N}}=2$ Conformal Supergravity

Author

Gold, Gregory, Khandelwal, Saurish, Kitchin, William, and Tartaglino-Mazzucchelli, Gabriele

Subjects
High Energy Physics - Theory, General Relativity and Quantum Cosmology, Mathematical Physics
Abstract

We define a new dilaton Weyl multiplet of ${\mathcal{N}}=2$ conformal supergravity in four dimensions. This is constructed by reinterpreting the equations of motion of an on-shell hypermultiplet as constraints that render some of the fields of the standard Weyl multiplet composite. The independent bosonic components include four scalar fields and a triplet of gauge two-forms. The resulting, so-called, hyper-dilaton Weyl multiplet defines a $24+24$ off-shell representation of the local ${\mathcal{N}}=2$ superconformal algebra. By coupling the hyper-dilaton Weyl multiplet to an off-shell vector multiplet compensator, we obtain one of the two minimal $32+32$ off-shell multiplets of ${\mathcal{N}}=2$ Poincar\'e supergravity constructed by M\"uller in 1986. On-shell, this contains the minimal ${\mathcal{N}}=2$ Poincar\'e supergravity multiplet together with a hypermultiplet where one of its physical scalars plays the role of a dilaton, while its three other scalars are dualised to a triplet of real gauge two-forms. Interestingly, a $BF$-coupling induces a scalar potential for the dilaton without a standard gauging., Comment: v3: typo corrected in eq. (2.33)

Published
2022
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33. Athletes' Perspectives of the Classification System in Para Alpine Skiing for Those With Visual Impairment.

Author

Douglas, Sara M., Kitchin, Paul J., Jackson, Andrew J., Barrett, Brendan T., and Little, Julie-Anne

Subjects
*QUALITATIVE research, *INTERVIEWING, *STATISTICAL sampling, *EXPERIENCE, *THEMATIC analysis, *SOUND recordings, *ATHLETES with disabilities, *SKIING, *RESEARCH methodology, *TRUST, *VIDEOCONFERENCING, *DISCRIMINATION against people with disabilities, *PSYCHOSOCIAL factors, *SNOW
Abstract

This study explored the classification experiences and views of Para Alpine skiers with visual impairment. Data from 11 interviews were analyzed using reflexive thematic analysis to generate three themes: Suitability—The skiers questioned the suitability of the visual measurements, testing environment, and the information they received regarding classification; Exclusivity—Skiers felt certain aspects of the system remain exclusive due to the restrictions of sport classes and lack of the athlete voice; and (Dis)trust—Skiers felt distrust in those implementing the system and in other athletes due to intentional misrepresentation. Speculation surrounding this resulted in the skiers' feeling doubt in their own classification. While there is not a "one size fits all" approach to classification, understanding skiers' experiences can be a vital first step and will help to guide future research into the evolution of this sport's classification. [ABSTRACT FROM AUTHOR]

Published
2024
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35. Indispensable, Interdependent, and Invisible: A Qualitative Inquiry into Library Systems Maintenance

Author

Tillman, Ruth Kitchin

Abstract

Over thirty years after such systems were first developed, the Integrated Library System underlies most operations of an academic library. Yet in the literature, its day-to-day maintenance is often reduced to a list of tasks. Through interviews with sixteen system maintainers, this study attempts to develop an experiential understanding of its maintenance. Findings suggest that most maintainers find such work meaningful but face barriers when colleagues and administrators don't understand what they do well enough to support it. This article proposes steps toward building a workplace where core maintenance tasks are recognized and supported.

Published
2023
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38. A rapid review of differences in cerebrospinal neurofilament light levels in clinical subtypes of progressive multiple sclerosis

Author

Haritha L. Desu, Katherine M. Sawicka, Emily Wuerch, Vanessa Kitchin, and Jacqueline A. Quandt

Subjects
multiple sclerosis, biomarkers, progression, neurofilament light, neurodegeneration, Neurology. Diseases of the nervous system, RC346-429
Abstract

BackgroundMultiple sclerosis (MS) is divided into three clinical phenotypes: relapsing–remitting MS (RRMS), secondary progressive MS (SPMS), and primary progressive MS (PPMS). It is unknown to what extent SPMS and PPMS pathophysiology share inflammatory or neurodegenerative pathological processes. Cerebrospinal (CSF) neurofilament light (NfL) has been broadly studied in different MS phenotypes and is a candidate biomarker for comparing MS subtypes.Research questionAre CSF NfL levels different among clinical subtypes of progressive MS?MethodsA search strategy identifying original research investigating fluid neurodegenerative biomarkers in progressive forms of MS between 2010 and 2022 was applied to Medline. Identified articles underwent title and abstract screen and full text review against pre-specified criteria. Data abstraction was limited to studies that measured NfL levels in the CSF. Reported statistical comparisons of NfL levels between clinical phenotypes were abstracted qualitatively.Results18 studies that focused on investigating direct comparisons of CSF NfL from people with MS were included in the final report. We found NfL levels were typically reported to be higher in relapsing and progressive MS compared to healthy controls. Notably, higher NfL levels were not clearly associated with progressive MS subtypes when compared to relapsing MS, and there was no observed difference in NfL levels between PPMS and SPMS in articles that separately assessed these phenotypes.ConclusionCSF NfL levels distinguish individuals with MS from healthy controls but do not differentiate MS subtypes. Broad biological phenotyping is needed to overcome limitations of current clinical phenotyping and improve biomarker translatability to decision-making in the clinic.

Published
2024
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39. Derivations of Quantum and Involution Generalized Weyl Algebras

Author

Kitchin, Andrew P.

Subjects
Mathematics - Quantum Algebra, Mathematics - Rings and Algebras
Abstract

We classify the derivations of degree-one generalized Weyl algebras over a univariate Laurent polynomial ring. In particular, our results cover the Weyl-Hayashi algebra, a quantization of the first Weyl algebra arising as a primitive factor algebra of $U_q^+(\mathfrak{so}_5)$, and a family of algebras which localize to the group algebra of the infinite group with generators $x$ and $y$, subject to the relation $xy = y^{-1}x.$, Comment: 16 pages

Published
2021
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40. Safety and Immunogenicity of the BNT162b2 Vaccine Coadministered with Seasonal Inactivated Influenza Vaccine in Adults

Author

Louise Murdoch, Karen Quan, James A. Baber, Agnes W. Y. Ho, Ying Zhang, Xia Xu, Claire Lu, David Cooper, Kenneth Koury, Stephen P. Lockhart, Annaliesa S. Anderson, Özlem Türeci, Uğur Şahin, Kena A. Swanson, William C. Gruber, Nicholas Kitchin, and the C4591030 Clinical Trial Group

Subjects
BNT162b2, Clinical trial, Coadministration, COVID-19, Immunogenicity, Influenza, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Introduction Vaccination is a critical tool for preventing coronavirus disease 2019 (COVID-19) and influenza illnesses. Coadministration of the COVID-19 vaccine, BNT162b2, with seasonal inactivated influenza vaccine (SIIV) can provide substantial benefits, including streamlining vaccine delivery. Methods In this phase 3 study, healthy 18- to 64-year-olds who had received three previous doses of BNT162b2 were randomized (1:1) to the coadministration group (month 0, BNT162b2 + SIIV; month 1, placebo) or the separate-administration group (month 0, placebo + SIIV; month 1, BNT162b2). The primary immunogenicity objective was to demonstrate that the immune responses elicited by BNT162b2 and SIIV [measured by full-length S-binding immunoglobulin G (IgG) levels and strain-specific hemagglutination inhibition assay (HAI) titers against four influenza strains 1 month post-vaccination, respectively] when coadministered were noninferior to those elicited by either vaccine administered alone, based on a prespecified 1.5-fold noninferiority margin [lower bound 95% CI for geometric mean ratio (GMR) > 0.67]. Reactogenicity and adverse event (AE) rates were evaluated. Results Randomized participants who received study vaccination (N = 1128; coadministration group, n = 564; separate-administration group, n = 564) had a median age of 39 years. Model-adjusted GMRs for coadministration to separate administration were 0.83 (95% CI 0.77, 0.89) for full-length S-binding IgG levels and 0.89–1.00 (lower bound of all 95% CIs > 0.67) for the four influenza strain-specific HAI titers, with all endpoints achieving the prespecified noninferiority criterion. Reactogenicity events were mostly mild or moderate when BNT162b2 was coadministered with SIIV. Serious AEs were reported in

Published
2023
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41. Machine-learning accelerated geometry optimization in molecular simulation

Author

Yang, Yilin, Jimenez-Negron, Omar A., and Kitchin, John R.

Subjects
Condensed Matter - Materials Science
Abstract

Geometry optimization is an important part of both computational materials and surface science because it is the path to finding ground state atomic structures and reaction pathways. These properties are used in the estimation of thermodynamic and kinetic properties of molecular and crystal structures. This process is slow at the quantum level of theory because it involves an iterative calculation of forces using quantum chemical codes such as density functional theory (DFT), which are computationally expensive, and which limit the speed of the optimization algorithms. It would be highly advantageous to accelerate this process because then one could either do the same amount of work in less time, or more work in the same time. In this work, we provide a neural network (NN) ensemble based active learning method to accelerate the local geometry optimization for multiple configurations simultaneously. We illustrate the acceleration on several case studies including bare metal surfaces, surfaces with adsorbates, and nudged elastic band (NEB) for two reactions. In all cases the accelerated method requires fewer DFT calculations than the standard method. In addition, we provide an ASE-optimizer Python package to make the usage of the NN ensemble active learning for geometry optimization easier.

Published
2021
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42. Map of the United States in North America with the British, French and Spanish dominions adjoining, according to the Treaty of 1783

Author

Kitchin, Thomas and Kitchin, Thomas

Subjects
United States Maps Early works to 1800 Facsimiles., United States
Abstract

Facsimile of 1783 map of the United States, showing drainage, states, towns, and ocean banks. Map extends west as far as the Falls of St. Anthony; north as far as Viner's Island (James Bay), Canada; east as far as St. John's, Newfoundland; and south as far as New Orleans.

Published
2024

44. Immunogenicity of the Monovalent Omicron XBB.1.5-Adapted BNT162b2 COVID-19 Vaccine against XBB.1.5, BA.2.86, and JN.1 Sublineages: A Phase 2/3 Trial

Author

Juleen Gayed, Vishva Bangad, Xia Xu, Federico Mensa, Mark Cutler, Özlem Türeci, Uǧur Şahin, Kayvon Modjarrad, Kena A. Swanson, Annaliesa S. Anderson, Alejandra Gurtman, and Nicholas Kitchin

Subjects
BA.2.86, BNT162b2, booster, COVID-19, JN.1, Omicron, Medicine
Abstract

We report neutralization titer data against contemporary SARS-CoV-2 sublineages from an ongoing, phase 2/3, open-label, clinical trial of a single dose (30 μg) of an Omicron XBB.1.5-adapted BNT162b2 monovalent mRNA vaccine. The trial included healthy participants who had received at least three previous doses of an mRNA vaccine authorized in the United States, with the most recent authorized vaccine dose being a bivalent Omicron BA.4/BA.5-adapted vaccine given at least 150 days before the study vaccination. In this analysis, Omicron XBB.1.5, BA.2.86, and JN.1 serum neutralizing titers were assessed at baseline and at 1 month after vaccination. Analyses were conducted in a subset of participants who were at least 18 years of age (N = 40) and who had evidence of previous SARS-CoV-2 infection. Immunogenicity was also evaluated in a group of participants who received bivalent BA.4/BA.5-adapted BNT162b2 in another study (ClinicalTrials.gov Identifier: NCT05472038) and who were matched demographically to the participants in the current trial. In this analysis, monovalent XBB.1.5-adapted BNT162b2 vaccine elicited higher XBB.1.5, BA.2.86, and JN.1 neutralizing titers than those elicited by bivalent BA.4/BA.5-adapted BNT162b2. Overall geometric mean fold rises in neutralizing titers from baseline to 1 month after vaccination were higher among participants who received XBB.1.5-adapted BNT162b2 than those who received bivalent BA.4/BA.5-adapted BNT162b2 for XBB.1.5 (7.6 vs. 5.6), slightly higher for JN.1 (3.9 vs. 3.5), and similar for BA.2.86 (4.8 vs. 4.9). ClinicalTrials.gov Identifier: NCT05997290.

Published
2024
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46. Ireland

Author

Carty, Catherine, Kitchin, Paul, van Lindert, Caroline, editor, Scheerder, Jeroen, editor, and Brittain, Ian, editor

Published
2023
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49. Early Elementary Science Instruction: Does More Time on Science or Science Topics/Skills Predict Science Achievement in the Early Grades?

Author

Curran, F. Chris and Kitchin, James

Abstract

Recent evidence points to the early elementary grades as a pivotal point for the development of science learning trajectories and achievement gaps. Using data from the Early Childhood Longitudinal Study, this study estimates the degree to which time spent on science and the breadth of science topics/skills covered predict science achievement in the earliest grades of elementary school. Using regression along with school fixed effects and student fixed effects models, we find suggestive evidence in some models (student fixed effects and regression with observable controls) that time on science instruction is related to science achievement but little evidence that the number of science topics/skills covered are related to greater science achievement. These results are generally consistent across student subgroups. We discuss the implications for early science policy and practice.

Published
2019

50. Exploratory mapping of tumor associated macrophage nanoparticle article abstracts using an eLDA topic modeling machine learning approach.

Author

Chloe Brown, Colette S M Bilynsky, Melanie Gainey, Sarah Young, John Kitchin, and Elizabeth C Wayne

Subjects
Medicine, Science
Abstract

The role of macrophages in regulating the tumor microenvironment has spurned the exponential generation of nanoparticle targeting technologies. With the large amount of literature and the speed at which it is generated it is difficult to remain current with the most up-to-date literature. In this study we performed a topic modeling analysis of 854 abstracts of peer-reviewed literature for the most common usages of nanoparticle targeting of tumor associated macrophages (TAMs) in solid tumors. The data spans 20 years of literature, providing a broad perspective of the nanoparticle strategies. Our topic model found 6 distinct topics: Immune and TAMs, Nanoparticles, Imaging, Gene Delivery and Exosomes, Vaccines, and Multi-modal Therapies. We also found distinct nanoparticle usage, tumor types, and therapeutic trends across these topics. Moreover, we established that the topic model could be used to assign new papers into the existing topics, thereby creating a Living Review. This type of "birds-eye-view" analysis provides a useful assessment tool for exploring new and emerging themes within a large field.

Published
2024
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