Significant heterogeneity in network structures reflecting individuals’ dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender). This makes it difficult to make inferences regarding these predefined subgroups. For this reason, researchers sometimes wish to identify subsets of individuals who have similarities in their dynamic processes regardless of any predefined category. This requires unsupervised classification of individuals based on similarities in their dynamic processes, or equivalently, in this case, similarities in their network structures of edges. The present paper tests a recently developed algorithm, S-GIMME, that takes into account heterogeneity across individuals with the aim of providing subgroup membership and precise information about the specific network structures that differentiate subgroups. The algorithm has previously provided robust and accurate classification when evaluated with large-scale simulation studies but has not yet been validated on empirical data. Here, we investigate S-GIMME’s ability to differentiate, in a purely data-driven manner, between brain states explicitly induced through different tasks in a new fMRI dataset. The results provide new evidence that the algorithm was able to resolve, in an unsupervised data-driven manner, the differences between different active brain states in empirical fMRI data to segregate individuals and arrive at subgroup-specific network structures of edges. The ability to arrive at subgroups that correspond to empirically designed fMRI task conditions, with no biasing or priors, suggests this data-driven approach can be a powerful addition to existing methods for unsupervised classification of individuals based on their dynamic processes.

Given the dramatic growth in the financial burden of cancer care over the past decades, individuals with cancer are increasingly susceptible to developing social needs (e.g., housing instability and food insecurity) and experiencing an adverse impact of these needs on care management and health outcomes. However, resources required to connect individuals with needed social and community services typically exceed the available staffing within clinical teams. Using input from focus groups, key informant interviews, user experience/user interface testing, and a multidisciplinary community advisory board, we developed a new technology solution, ConnectedNest, which connects individuals in need to community based organizations (CBOs) that provide services through direct and/or oncology team referrals, with interfaces to support all three groups (patients, CBOs, and oncology care teams). After prototype development, we conducted usability testing, with participants noting the importance of the technology for filling a current gap in screening and connecting individuals with cancer with needed social and community services. We employ a patient-empowered approach that engages the support of an individual’s healthcare team and community organizations. Future work will examine the integration and implementation of ConnectedNest for oncology patients, oncology care teams, and cancer-focused CBOs to build capacity for effectively addressing distress in this population.

This paper outlines the development, deployment and use, and testing of a tool for measuring and improving healthcare researcher embeddedness – i.e., being connected to and engaged with key leverage points and stakeholders in a health system. Despite the widely acknowledged importance of embeddedness for learning health systems and late-stage translational research, we were not aware of useful tools for addressing and improving embeddedness in scholar training programs. We developed the MN-LHS Embeddedness Tool covering connections to committees, working groups, leadership, and other points of contact across four domains: patients and caregivers; local practice (e.g., operations and workflows); local institutional research (e.g., research committees and agenda- or initiative-setting groups); and national (strategic connections within professional groups, conferences, etc.). We used qualitative patterns and narrative findings from 11 learning health system training program scholars to explore variation in scholar trajectories and the embeddedness tool’s usefulness in scholar professional development. Tool characteristics showed moderate evidence of construct validity; secondarily, we found significant differences in embeddedness, as a score, from baseline through program completion. The tool has demonstrated simple, practical utility in making embeddedness an explicit (rather than hidden) part of applied and learning health system researcher training, alongside emerging evidence for validity.

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimization. Here, an automated, HRR-compatible system produced high-fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimization of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.

We present the development and characterization of a high-stability, multi-material, multi-thickness tape-drive target for laser-driven acceleration at repetition rates of up to 100 Hz. The tape surface position was measured to be stable on the sub-micrometre scale, compatible with the high-numerical aperture focusing geometries required to achieve relativistic intensity interactions with the pulse energy available in current multi-Hz and near-future higher repetition-rate lasers ($>$kHz). Long-term drift was characterized at 100 Hz demonstrating suitability for operation over extended periods. The target was continuously operated at up to 5 Hz in a recent experiment for 70,000 shots without intervention by the experimental team, with the exception of tape replacement, producing the largest data-set of relativistically intense laser–solid foil measurements to date. This tape drive provides robust targetry for the generation and study of high-repetition-rate ion beams using next-generation high-power laser systems, also enabling wider applications of laser-driven proton sources.

A machine learning model was created to predict the electron spectrum generated by a GeV-class laser wakefield accelerator. The model was constructed from variational convolutional neural networks, which mapped the results of secondary laser and plasma diagnostics to the generated electron spectrum. An ensemble of trained networks was used to predict the electron spectrum and to provide an estimation of the uncertainty of that prediction. It is anticipated that this approach will be useful for inferring the electron spectrum prior to undergoing any process that can alter or destroy the beam. In addition, the model provides insight into the scaling of electron beam properties due to stochastic fluctuations in the laser energy and plasma electron density.

Humpback whales (Megaptera novaeangliae) exhibit maternally driven fidelity to feeding grounds, and yet occasionally occupy new areas. Humpback whale sightings and mortalities in the New York Bight apex (NYBA) have been increasing over the last decade, providing an opportunity to study this phenomenon in an urban habitat. Whales in this area overlap with human activities, including busy shipping traffic leading into the Port of New York and New Jersey. The site fidelity, population composition and demographics of individual whales were analysed to better inform management in this high-risk area. Whale watching and other opportunistic data collections were used to identify 101 individual humpback whales in the NYBA from spring through autumn, 2012–2018. Although mean occurrence was low (2.5 days), mean occupancy was 37.6 days, and 31.3% of whales returned from one year to the next. Individuals compared with other regional and ocean-basin-wide photo-identification catalogues (N = 52) were primarily resighted at other sites along the US East Coast, including the Gulf of Maine feeding ground. Sightings of mother-calf pairs were rare in the NYBA, suggesting that maternally directed fidelity may not be responsible for the presence of young whales in this area. Other factors including shifts in prey species distribution or changes in population structure more broadly should be investigated.

One significant difficulty in reliable quantification of the rates of mass-loss from hot, massive stars lies in uncertainties associated with quantifying temporal and spatial variability within stellar winds. The consequences of low-metallicity conditions for wind structure also merit continued investigation. We present initial results from ULLYSES data with the aim of identifying structure within the stellar winds of early B type supergiants with sub-solar metallicities in the Large and Small Magellanic Clouds. We demonstrate how single-epoch ULLYSES data can be used to investigate significant wind structure for these stars.

Diamondback moth, Plutella xylostella (Linnaeus) (Lepidoptera: Plutellidae), a globally important pest of Brassicaceae crops, migrates into all provinces of Canada annually. Life tables were used to determine the mortality levels contributed by the parasitoid complexes associated with diamondback moth in British Columbia, Ontario, Prince Edward Island, and insular Newfoundland. Overall, diamondback moth populations showed high generational mortality (> 90%) in all provinces, although parasitism levels were generally low. The net reproductive rate of increase in diamondback moth was less than 1.0 (populations declined) in both years in British Columbia and in each of two years in Newfoundland and Ontario, but it was greater than 1.0 in all three years in Prince Edward Island. Lower parasitism levels were found in Prince Edward Island (3.0–6.3%) compared with other provinces (8.4–17.6%, except one year in British Columbia). Diadegma insulare was the main larval parasitoid found; it was present in all provinces. Microplitis plutellae was present in all provinces except British Columbia. Oomyzus sokolowskii was found in British Columbia and Ontario. The parasitoid community documented from sentinel sampling was less diverse than that found through destructive sampling. Hypotheses are provided to explain the presence of major parasitoids. Increasing larval parasitism would have the largest effect on diamondback moth population growth in Canada.

In recent years, a variety of efforts have been made in political science to enable, encourage, or require scholars to be more open and explicit about the bases of their empirical claims and, in turn, make those claims more readily evaluable by others. While qualitative scholars have long taken an interest in making their research open, reflexive, and systematic, the recent push for overarching transparency norms and requirements has provoked serious concern within qualitative research communities and raised fundamental questions about the meaning, value, costs, and intellectual relevance of transparency for qualitative inquiry. In this Perspectives Reflection, we crystallize the central findings of a three-year deliberative process—the Qualitative Transparency Deliberations (QTD)—involving hundreds of political scientists in a broad discussion of these issues. Following an overview of the process and the key insights that emerged, we present summaries of the QTD Working Groups’ final reports. Drawing on a series of public, online conversations that unfolded at www.qualtd.net, the reports unpack transparency’s promise, practicalities, risks, and limitations in relation to different qualitative methodologies, forms of evidence, and research contexts. Taken as a whole, these reports—the full versions of which can be found in the Supplementary Materials—offer practical guidance to scholars designing and implementing qualitative research, and to editors, reviewers, and funders seeking to develop criteria of evaluation that are appropriate—as understood by relevant research communities—to the forms of inquiry being assessed. We dedicate this Reflection to the memory of our coauthor and QTD working group leader Kendra Koivu.1

Typical enteropathogenic Escherichia coli (tEPEC) infection is a major cause of diarrhoea and contributor to mortality in children <5 years old in developing countries. Data were analysed from the Global Enteric Multicenter Study examining children <5 years old seeking care for moderate-to-severe diarrhoea (MSD) in Kenya. Stool specimens were tested for enteric pathogens, including by multiplex polymerase chain reaction for gene targets of tEPEC. Demographic, clinical and anthropometric data were collected at enrolment and ~60-days later; multivariable logistic regressions were constructed. Of 1778 MSD cases enrolled from 2008 to 2012, 135 (7.6%) children tested positive for tEPEC. In a case-to-case comparison among MSD cases, tEPEC was independently associated with presentation at enrolment with a loss of skin turgor (adjusted odds ratio (aOR) 2.08, 95% confidence interval (CI) 1.37–3.17), and convulsions (aOR 2.83, 95% CI 1.12–7.14). At follow-up, infants with tEPEC compared to those without were associated with being underweight (OR 2.2, 95% CI 1.3–3.6) and wasted (OR 2.5, 95% CI 1.3–4.6). Among MSD cases, tEPEC was associated with mortality (aOR 2.85, 95% CI 1.47–5.55). This study suggests that tEPEC contributes to morbidity and mortality in children. Interventions aimed at defining and reducing the burden of tEPEC and its sequelae should be urgently investigated, prioritised and implemented.