Narrow Set Research Articles

A more efficient CRISPR-Cas12a variant derived from Lachnospiraceae bacterium MA2020

CRISPR effector proteins introduce double-stranded breaks into the mammalian genome, facilitating gene editing by non-homologous end-joining or homology-directed repair. Unlike the more commonly studied Cas9, the CRISPR effector protein Cas12a/Cpf1 recognizes a T-rich protospacer adjacent motif (PAM) and can process its own CRISPR RNA (crRNA) array, simplifying the use of multiple guide RNAs. We observed that the Cas12a ortholog of Lachnospiraceae bacterium MA2020 (Lb2Cas12a) edited mammalian genes with efficiencies comparable to those of AsCas12a and LbCas12a. Compared to these well-characterized Cas12a orthologs, Lb2Cas12a is smaller and recognizes a narrow set of PAM TTTV. We introduced two mutations into Lb2Cas12a, Q571K and C1003Y, that increased its cleavage efficiency for a range of target sequences beyond those of the commonly used Cas12a orthologs AsCas12a and LbCas12a. In addition to the canonical TTTV PAM, this variant, Lb2-KY, also efficiently cleaved target regions with CTTN PAMs. Finally, we demonstrated that Lb2-KY ribonucleoprotein (RNP) complexes edited two hemoglobin target regions useful for correcting common forms of sickle-cell anemia more efficiently than commercial AsCas12a RNP complexes. Thus, Lb2-KY has distinctive properties useful for modifying a range of clinically relevant targets in the human genome.

CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?

CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?

The domain of existence of solitary waves in fluid-filled thin elastic tubes

Under given prestress conditions, solitary waves in fluid-filled elastic tubes are confined to a rather narrow set of combinations of the background fluid velocity and the wave speed. This set, which we call the domain of existence, is bounded by several curves that represent various physical and mathematical restrictions. Remarkably, these restrictions can be cast as purely algebraic conditions to be imposed upon the governing system of differential equations. Paramount among the physical restrictions are the avoidance of wrinkles and the self-impenetrability of the wave profile. In particular, the existence of a critical wave speed of impending wrinkling, independent of the background fluid velocity, is established rigorously.

Evaluating the social and ecological effectiveness of partially protected marine areas.

Marine protected areas (MPAs) are a primary tool for the stewardship, conservation, and restoration of marine ecosystems, yet 69% of global MPAs are only partially protected (i.e., are open to some form of fishing). Although fully protected areas have well‐documented outcomes, including increased fish diversity and biomass, the effectiveness of partially protected areas is contested. Partially protected areas may provide benefits in some contexts and may be warranted for social reasons, yet social outcomes often depend on MPAs achieving their ecological goals to distinguish them from open areas and justify the cost of protection. We assessed the social perceptions and ecological effectiveness of 18 partially protected areas and 19 fully protected areas compared with 19 open areas along 7000 km of coast of southern Australia. We used mixed methods, gathering data via semistructured interviews, site surveys, and Reef Life (underwater visual census) surveys. We analyzed qualitative data in accordance with grounded theory and quantitative data with multivariate and univariate linear mixed‐effects models. We found no social or ecological benefits for partially protected areas relative to open areas in our study. Partially protected areas had no more fish, invertebrates, or algae than open areas; were poorly understood by coastal users; were not more attractive than open areas; and were not perceived to have better marine life than open areas. These findings provide an important counterpoint to some large‐scale meta‐analyses that conclude partially protected areas can be ecologically effective but that draw this conclusion based on narrower measures. We argue that partially protected areas act as red herrings in marine conservation because they create an illusion of protection and consume scarce conservation resources yet provide little or no social or ecological gain over open areas. Fully protected areas, by contrast, have more fish species and biomass and are well understood, supported, and valued by the public. They are perceived to have better marine life and be improving over time in keeping with actual ecological results. Conservation outcomes can be improved by upgrading partially protected areas to higher levels of protection including conversion to fully protected areas.

Identification of HIF-dependent alternative splicing in gastrointestinal cancers and characterization of a long, coding isoform of SLC35A3

Intra-tumor hypoxia is a common feature in many solid cancers. Although transcriptional targets of hypoxia-inducible factors (HIFs) have been well characterized, alternative splicing or processing of pre-mRNA transcripts which occurs during hypoxia and subsequent HIF stabilization is much less understood. Here, we identify many HIF-dependent alternative splicing events after whole transcriptome sequencing in pancreatic cancer cells exposed to hypoxia with and without downregulation of the aryl hydrocarbon receptor nuclear translocator (ARNT), a protein required for HIFs to form a transcriptionally active dimer. We correlate the discovered hypoxia-driven events with available sequencing data from pan-cancer TCGA patient cohorts to select a narrow set of putative biologically relevant splice events for experimental validation. We validate a small set of candidate HIF-dependent alternative splicing events in multiple human gastrointestinal cancer cell lines as well as patient-derived human pancreatic cancer organoids. Lastly, we report the discovery of a HIF-dependent mechanism to produce a hypoxia-dependent, long and coding isoform of the UDP-N-acetylglucosamine transporter SLC35A3.

The SDGs as an Integrative Framework to Assess Coherence of Transnational Multistakeholder Partnerships for SIDS

Research in global climate governance recognizes the importance of transnational multistakeholder partnerships (often termed cooperative initiatives) in driving climate action from global to subnational levels. Large N studies of climate partnerships have shed light on cooperative governance's inclusiveness, thematic focus, geographic scope, degree of institutionalization, and contribution to the attainment of climate goals. However, a neglected aspect of partnership performance concerns its coherence, i.e., the extent to which portfolios of partnerships contribute to the balanced implementation of climate goals across the economic, social, and environmental dimensions of sustainable development. Climate action is a complex transboundary problem that spans several sectors and scales and increasingly, scholarship is mapping these linkages across issue areas and levels. Drawing on this evidence base, this paper conducts a large N study of 49 climate-related partnerships in Pacific SIDS (PSIDS) to assess whether and to what extent these partnerships taken together contribute to the balanced implementation of climate action in PSIDS. Using the Sustainable Development Goals (SDGs) as a framework to assess coherence and introducing a measure of partnership's Output-SDG-Fit, results indicate that these partnerships tend to cluster their activities around a narrow set of nexuses with the climate-ocean nexus receiving relatively many partnerships and the climate-development nexus highly underrepresented. The findings support the view that transnational cooperative climate governance in Pacific SIDS is incoherent and that a lack of development finance for many SIDS may be driving incoherence in PSIDS partnerships. The paper discusses the practical implications of this finding for the orchestration of more coherent portfolios of climate partnerships.

SARS-CoV-2 Ultraviolet Radiation Dose-Response Behavior.

Ultraviolet (UV) radiation in the wavelength range 200 nm ≤ λ ≤ 320 nm, which includes both the UV-C and UV-B portions of the spectrum, is known to be effective for inactivation of a wide range of microbial pathogens, including viruses. Previous research has indicated UV-C radiation to be effective for inactivation of severe acute respiratory syndrome coronavirus (SARS-CoV), the virus that caused an outbreak of SARS in 2003. Given the structural similarities of SARS-CoV and SARS-CoV-2, the cause of coronavirus disease 2019 (COVID-19), it is anticipated that UV radiation should be effective for inactivation of SARS-CoV-2 too. Recently published data support this assertion, but only for a narrow set of exposure and matrix conditions. Models based on genomic and other characteristics of viruses have been developed to provide predictions of viral inactivation responses to UV exposure at λ = 254 nm. The predictions of these models are consistent with reported measurements of viral inactivation, including for SARS-CoV-2. As such, current information indicates that UV-C irradiation should be effective for control of SARS-CoV-2, as well as for control of other coronaviruses; however, additional research is needed to quantify the effects of several important process variables, including the wavelength of radiation, the effects of relative humidity on airborne and surface-associated viruses, and the effects of the medium of exposure.

Academic Ableism: Disability and Higher Education (Corporealities: Discourses of Disability)

Academic Ableism: Disability and Higher Education (Corporealities: Discourses of Disability)

Private health care sector in Croatia

This paper investigates the trends in business activity of the private health care sector in Croatia from 2011 to 2018. Databases of Croatian provider of financial and electronic services - Financial Agency (FINA) - have been employed to explore key performance indicators of private health care sector companies, in particular trends in total employment, business revenues and operating profits. In addition, the most important features of voluntary health insurance (VHI) provided by private health insurance companies and the Croatian Health Insurance Fund (HZZO) have been presented. Furthermore, this paper provides both a relevant analysis of the private health care sector as well as private spending on health care in Croatia. The results indicate that users of health care services are willing to pay more to gain faster access and higher quality services. In 2018, expenditures for private health care services reached almost HRK 5 billion. Nearly 60% were out-of-pocket (OOP) payments and 40% were paid through the VHI. Despite the persistent recession, the private health care sector in Croatia experienced an average annual growth rate of 10% in the analysed period. More recently, the trend of introduction of more complex services within private providers can be observed indicating the rise in investments, and competitiveness. In conclusion, private health care sector’s presence in Croatia is a reality that should be addressed adequately. Original findings in this paper might serve as starting point for future discussions regarding the private health care sector role in the overall health care system financing. The paper brings a deeper insight into Croatian private health care sector market using original and most recent microdata thus shedding the light on important part of our health economy. Nevertheless, paper has certain limitations that are mainly reflected in relatively narrow set of indicators used in private health care sector business analysis. This though might be addressed properly in future research.

Advances in Low-Resource and Endangered Languages

This paper reports on the approaches and results for the collection, analysis, and processing of low-resource and endangered languages carried out under the Low-Resource Languages for Emergent Incidents (LORELEI) Program1. LORELEI was a multi-year research and development program designed to discover new methods of quickly ramping up human language technology capabilities for low-resource languages, grounded in situations such as humanitarian and disaster relief use cases. The goal was to advance human language technology methods to better enable rapid, low-cost development of capabilities, with a focus on developing methods that apply to languages of any type from any language family, thus eliminating the need to tailor specific technologies to a narrow set of input languages with specific typological characteristics. We report in detail on evaluation scenarios developed for the program.

CONTINUING PHANTOM INFLUX OF ENGLISH LEXIS VIA LOCALIZATION: AN ANALYSIS OF ENGLISH COMPUTER TERMINOLOGY LOCALIZED IN SERBIAN

The paper analyzes the English computer terminology localized into Serbian between 2010 to 2020 both from the purely lexical stand-point (type and status of borrowings, as per Prćić (2005)), and from the viewpoint of pragmatic borrowings (Andersen 2014), but in a narrower set of pragmatic borrowings limited to written modes of communication: direct and indirect borrowing of interjections, discourse markers, expletives, vocatives, and paralinguistic phenomena. The chosen period saw a rapid expansion of instant communication applications with rich sets of emoticons, proliferation of cloud services and replace- ment of traditional software with software as a service. The research aims to shed light on how much the new lexis from computer-mediated forms of communication, as well as pragmatic notions stemming from the emerging trend of IT companies crafting their content to adhere to strict stylistic and audience accommodation guidelines, have been taken over into Serbian. Preliminary findings indicate that there is a huge number of both lexical and pragmatic borrowings, including interjections and paralinguistic phenomena such as names of emoticons and short codes used to insert emoticons. This influx of new, raw and, due to style guides, conditionally justified English borrowings provides a new form of evidence of English having become a nativized foreign language (Prćić 2014) of online communication, which, as a knock-on effect, leads translators and localization teams to consider raw and normally unjustified borrowings as acceptable and appropriate for the target audience that consists of more and more digital natives. The research applies a combination of quantitative and qualitative research methods by means of annotating the 2020 version of Microsoft English-Serbian Bilingual Terminology Database.

Multi-verb constructions in Eastern Indonesia by Volker Unterladstetter

Reviewed by: Multi-verb constructions in Eastern Indonesia by Volker Unterladstetter Leah Pappas Volker Unterladstetter. 2020. Multi-verb constructions in Eastern Indonesia. Studies in Diversity Linguistics 28. Berlin: Language Science Press. 452 pp. ISBN 978-3-96110-216-7, digital. ISBN 978-3-96110-217-4, hardcover. Open Access. Multi-verb constructions (MVCs) have recently received attention in the literature on multi-verb strings. While some authors such as Nordhoff (2012) find value in keeping the term broad and descriptive, others such as Aikhenvald (2011) and Enfield (2008) seek to specify parameters that define MVCs. Despite the variation in these approaches, usually authors minimally identify MVCs to be verb strings within a single clause with an absence of linking elements between the verbs. MVC has become a popular term because traditionally much of the discussion around multi-verb strings has centered on the more theoretically entrenched term "serial verb construction" (SVC). Issues with the term "SVC" arise when one tries to define what constitutes an SVC; authors tend to vary as to whether to include a broad set of defining parameters or a narrow set of easily identifiable universals (Haspelmath 2016). However, based on the parameters that authors most-commonly adopt, SVCs are viewed as equally inflected verb strings within a single clause and intonation unit that show no signs of coordination, syntactic dependency, nor subordination. Yet, these parameters can sometimes be restrictive and are not always typologically applicable. Thus, the term MVC has been adopted into the serial verb literature by authors who wish to avoid extensive theoretical debates in their grammatical descriptions or by those who seek to include constructions that are clearly not SVCs in their analysis. For the time being, "multi-verb construction" remains a hyperonymic and manipulatable term to describe verb strings. The book under review provides a comprehensive overview of the semantic, syntactic, and areal features of MVCs in Eastern Indonesia (EI) based on data from sixteen Austronesian and sixteen non-Austronesian languages. Using several types of data—corpus data, published grammars, and glossed texts—the author grapples with a variety of analytical frameworks employed by different authors. In order to objectively approach this data and account for the immense grammatical diversity in EI languages, Unterladstetter adopts the term MVC. As a result, he is able to include a broad corpus of multi-verb strings which is not constrained by what one may define as "canonical" serial verb constructions. The resulting approach to typologizing MVCs systematizes the interplay between their syntactic and semantic parameters and has the potential for widespread applicability to MVCs anywhere in the world. The book provides several novel contributions including the idea of hierarchized MVCs, the [End Page 243] adoption of Davidsonian semantics and lexical decomposition in the MVC context, and a systematic approach to event analysis in language. This book consists of seven chapters. It begins with a general introduction (chapter 1), followed by more specific introductions to the languages of EI (chapter 2) and the literature around SVCs and MVCs (chapter 3). These reviews are followed by a description of the grammatical (chapter 4) and semantic (chapter 5) properties of MVCs in EI which are then combined to describe the various types of MVC constructions in the region (chapter 6). Finally, Unterladstetter concludes with a discussion that summarizes the contents of the book and identifies its broader implications and avenues for future research (chapter 7). Chapter 1 establishes the scope of the book and provides the background information necessary for any reader to understand the content. This chapter further introduces the broad goals of the book while briefly summarizing the material addressed in chapters 2 and 3. In addition to laying out the hypotheses that are investigated throughout the book (repeated below), Unterladstetter preliminarily defends his position to address MVCs rather than SVCs. All topics in chapter 1 are addressed in more detail in the following chapters. In chapter 1 (p. 15), the author identifies the following to be the primary hypotheses explored throughout the book: 1. Although the morphosyntactic make-up of MVCs in EI is remarkably similar across different construction types, these MVCs are constructed through different techniques (mentioned below in #H2) 2. Verbal interaction in...

Performance Modeling of Moving Target Defenses with Reconfiguration Limits

Moving Target Defense (MTD) has recently emerged as a game changer in the security landscape due to its proven potential to introduce asymmetric uncertainty that gives the defender a tactical advantage over the attacker. Many different MTD techniques have been developed, but, despite the huge progress made in this area, critical gaps still exist with respect to the problem of studying and quantifying the cost and benefits of deploying MTDs. In fact, all existing techniques address a very narrow set of attack vectors, and, due to the lack of shared metrics, it is difficult to quantify and compare multiple techniques. Building on our preliminary work in this field, we propose a quantitative analytic model for assessing the resource availability and performance of MTDs, and a method for maximizing a utility function that captures the tradeoffs between security and performance. The proposed model generalizes our previous model and can be applied to a wider range of MTDs and operational scenarios to improve availability and performance by imposing limits on the maximum number of resources that can be in the process of being reconfigured. The analytic results are validated by simulation and experimentation, confirming the accuracy of our model.

Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist

Optimizing economic and public policy is critical to address socioeconomic issues and trade-offs, e.g., improving equality, productivity, or wellness, and poses a complex mechanism design problem.A policy designer needs to consider multiple objectives, policy levers, and behavioral responses from strategic actors who optimize for their individual objectives.Moreover, real-world policies should be explainable and robust to simulation-to-reality gaps, e.g., due to calibration issues.Existing approaches are often limited to a narrow set of policy levers or objectives that are hard to measure, do not yield explicit optimal policies, or do not consider strategic behavior, for example.Hence, it remains challenging to optimize policy in real-world scenarios.Here we show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning (RL) and data-driven simulations.We validate our framework on optimizing the stringency of \USState{} policies and Federal subsidies during a pandemic, e.g., COVID-19, using a simulation fitted to real data.We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes.Their behavior can be explained, e.g., well-performing policies respond strongly to changes in recovery and vaccination rates.They are also robust to calibration errors, e.g., infection rates that are over or underestimated.As of yet, real-world policymaking has not seen adoption of machine learning methods at large, including RL and AI-driven simulations.Our results show the potential of AI to guide policy design and improve social welfare amidst the complexity of the real world.

Biased or balanced? Assessing BBC news and current affairs performance in covering the badger cull in England

This article represents the first systematic examination of BBC coverage of one of the most controversial rural issues in a generation, namely the culling of badgers (a protected species) to stop the spread of bovine TB in England. While the BBC has certain regulatory responsibilities set out in its guidelines to provide duly impartial coverage it has been regularly criticised for being biased. Little is known about the BBC's performance other than what is suggested by critics, previous research having focused on press coverage. Based on an original content analysis of news, current affairs and factual output this article assesses the BBC's coverage. It shows that while competing voices and perspectives were balanced its coverage was not that distinct from its commercial rivals, with both framing the issue as a conflict over badgers rather than about the spread of a disease affecting livestock and livelihoods, and both focusing on a narrow set of voices involved in the conflict. • Systematic examination of bias in BBC coverage of the culling of badgers to stop the spread of bovine TB in England. • No evidence of bias against certain rural communities, pursuits and businesses, nor failing to understand rural matters. • Selected indicators did reveal coverage was problematic. • BBC missed opportunities to broaden and contextualize the agenda, widen the selection of news actors, and challenge actor claims.

Generalist versus specialist CEOs and acquisitions: Two‐sided matching and the impact of CEO characteristics on firm outcomes

AbstractResearch SummaryTo address endogeneity concerns stemming from firm‐CEO matching, we deploy a two‐sided matching model that identifies the complementarities arising from the CEO‐firm match and subsequently account for these complementarities in empirical tests. Applying this approach, we examine how the nature of CEOs' human capital affects the acquisition behavior and performance of firms. We find that generalist CEOs (CEOs with a broader set of knowledge and skills) are more likely to engage in unrelated acquisitions than specialist CEOs (CEOs with a narrower but deeper set of knowledge and skills). We also find that the fit between the nature of CEOs' human capital and the type of acquisitions they undertake is associated with stronger performance. Our paper contributes to research on CEOs, human capital, M&As, and microfoundations.Managerial SummaryWe deploy an empirical approach that takes into account the complementarities that arise from the matching of CEOs and firms when testing hypotheses on how CEO attributes shape firm outcomes. Based on this approach, our study finds that CEOs with a broader set of managerial knowledge and skills (generalist CEOs) are more likely to engage in unrelated acquisitions (acquisitions outside a firm's main industry) than CEOs with a narrower but deeper set of knowledge and skills that is more closely tied to a particular industry, firm, or domain (specialist CEOs). We also find that the fit between the nature of CEOs' human capital and the type of acquisitions they engage in is associated with stronger performance.

Modelling Orebody Structures: Block Merging Algorithms and Block Model Spatial Restructuring Strategies Given Mesh Surfaces of Geological Boundaries

This paper describes a framework for capturing geological structures in a 3D block model and improving its spatial fidelity given new mesh surfaces. Using surfaces that represent geological boundaries, the objectives are to identify areas where refinement is needed, increase spatial resolution to minimize surface approximation error, reduce redundancy to increase the compactness of the model and identify the geological domain on a block-by-block basis. These objectives are fulfilled by four system components which perform block-surface overlap detection, spatial structure decomposition, sub-blocks consolidation and block tagging, respectively. The main contributions are a coordinate-ascent merging algorithm and a flexible architecture for updating the spatial structure of a block model when given multiple surfaces, which emphasizes the ability to selectively retain or modify previously assigned block labels. The techniques employed include block-surface intersection analysis based on the separable axis theorem and ray-tracing for establishing the location of blocks relative to surfaces. To demonstrate the robustness and applicability of the proposed block merging strategy in a more narrow setting, it is used to reduce block fragmentation in an existing model where surfaces are not given and the minimum block size is fixed. To obtain further insight, a systematic comparison with octree subblocking subsequently illustrates the inherent constraints of dyadic hierarchical decomposition and the importance of inter-scale merging. The results show the proposed method produces merged blocks with less extreme aspect ratios and is highly amenable to parallel processing. The overall framework is applicable to orebody modelling given geological boundaries, and 3D segmentation more generally, where there is a need to delineate spatial regions using mesh surfaces within a block model.

Data Quality and Trust: Review of Challenges and Opportunities for Data Sharing in IoT

Existing research recognizes the critical role of quality data in the current big-data and Internet of Things (IoT) era. Quality data has a direct impact on model results and hence business decisions. The growth in the number of IoT-connected devices makes it hard to access data quality using traditional assessments methods. This is exacerbated by the need to share data across different IoT domains as it increases the heterogeneity of the data. Data-shared IoT defines a new perspective of IoT applications which benefit from sharing data among different domains of IoT to create new use-case applications. For example, sharing data between smart transport and smart industry can lead to other use-case applications such as intelligent logistics management and warehouse management. The benefits of such applications, however, can only be achieved if the shared data is of acceptable quality. There are three main practices in data quality (DQ) determination approaches that are restricting their effective use in data-shared platforms: (1) most DQ techniques validate test data against a known quantity considered to be a reference; a gold reference. (2) narrow sets of static metrics are used to describe the quality. Each consumer uses these metrics in similar ways. (3) data quality is evaluated in isolated stages throughout the processing pipeline. Data-shared IoT presents unique challenges; (1) each application and use-case in shared IoT has a unique description of data quality and requires a different set of metrics. This leads to an extensive list of DQ dimensions which are difficult to implement in real-world applications. (2) most data in IoT scenarios does not have a gold reference. (3) factors endangering DQ in shared IoT exist throughout the entire big-data model from data collection to data visualization, and data use. This paper aims to describe data-shared IoT and shared data pools while highlighting the importance of sharing quality data across various domains. The article examines how we can use trust as a measure of quality in data-shared IoT. We conclude that researchers can combine such trust-based techniques with blockchain for secure end-to-end data quality assessment.

Healthcare use by people who use illicit opioids (HUPIO): development of a cohort based on electronic primary care records in England.

Background: People who use illicit opioids such as heroin have substantial health needs, but there are few longitudinal studies of general health and healthcare in this population. Most research to date has focused on a narrow set of outcomes, including overdoses and HIV or hepatitis infections. We developed and validated a cohort using UK primary care electronic health records (Clinical Practice Research Datalink GOLD and AURUM databases) to facilitate research into healthcare use by people who use illicit opioid use (HUPIO). Methods: Participants are patients in England with primary care records indicating a history of illicit opioid use. We identified codes including prescriptions of opioid agonist therapies (methadone and buprenorphine) and clinical observations such as 'heroin dependence'. We constructed a cohort of patients with at least one of these codes and aged 18-64 at cohort entry, with follow-up between January 1997 and March 2020. We validated the cohort by comparing patient characteristics and mortality rates to other cohorts of people who use illicit opioids, with different recruitment methods. Results: Up to March 2020, the HUPIO cohort included 138,761 patients with a history of illicit opioid use. Demographic characteristics and all-cause mortality were similar to existing cohorts: 69% were male; the median age at index for patients in CPRD AURUM (the database with more included participants) was 35.3 (IQR 29.1-42.6); the average age of new cohort entrants increased over time; 76% had records indicating current tobacco smoking; patients disproportionately lived in deprived neighbourhoods; and all-cause mortality risk was 5.4 (95% CI 5.3-5.5) times the general population of England. Conclusions: Primary care data offer new opportunities to study holistic health outcomes and healthcare of this population. The large sample enables investigation of rare outcomes, whilst the availability of linkage to external datasets allows investigation of hospital use, cancer treatment, and mortality.

Machine learning applied to simulations of collisions between rotating, differentiated planets

In the late stages of terrestrial planet formation, pairwise collisions between planetary-sized bodies act as the fundamental agent of planet growth. These collisions can lead to either growth or disruption of the bodies involved and are largely responsible for shaping the final characteristics of the planets. Despite their critical role in planet formation, an accurate treatment of collisions has yet to be realized. While semi-analytic methods have been proposed, they remain limited to a narrow set of post-impact properties and have only achieved relatively low accuracies. However, the rise of machine learning and access to increased computing power have enabled novel data-driven approaches. In this work, we show that data-driven emulation techniques are capable of classifying and predicting the outcome of collisions with high accuracy and are generalizable to any quantifiable post-impact quantity. In particular, we focus on the dataset requirements, training pipeline, and classification and regression performance for four distinct data-driven techniques from machine learning (ensemble methods and neural networks) and uncertainty quantification (Gaussian processes and polynomial chaos expansion). We compare these methods to existing analytic and semi-analytic methods. Such data-driven emulators are poised to replace the methods currently used in N-body simulations, while avoiding the cost of direct simulation. This work is based on a new set of 14,856 SPH simulations of pairwise collisions between rotating, differentiated bodies at all possible mutual orientations.