Environmental Research Research Articles

Feasibility of structuring electronic health record data to facilitate real-world data research: ICAREdata methods applied to multicenter cancer clinical trials.

The use of electronic health record (EHR) data for research is limited by a lack of structure and a standard data model. The objective of the ICAREdata (Integrating Clinical Trials and Real-World Endpoints Data) project was to structure key research data elements in EHRs using a minimal Common Oncology Data Elements (mCODE) data model to extract and transmit data. The ICAREdata project captured two EHR data elements essential to clinical trials: cancer disease status and treatment plan change. The project was implemented in clinical sites participating in Alliance for Clinical Trials in Oncology trials. Data were extracted from EHRs and sent by secure Fast Healthcare Interoperability Resource messaging (a standard for exchanging EHRs) to a database. Selected elements were compared with corresponding data from the trial's electronic data capture (EDC) system, Medidata Rave. By December 2023, data were extracted and transmitted from 10 sites for 35 patients, involving 367 clinical encounters across 15 clinical trials. Data through March 2023 demonstrated that concordance for the elements treatment plan change and cancer disease status was 79% and 34%, respectively. When disease evaluation was reported by both EHR and EDC (n=15), there was 87% agreement on cancer disease status. Documentation, extraction, and aggregation of structured data elements in EHRs using mCODE and ICAREdata methods is feasible in multi-institutional cancer clinical trials. EDC as a reference data setallowed assessment of the completeness of EHR data capture. Future initiatives will focus on elements with shared definitions in clinical and research environments and efficient workflows. Clinical trials use electronic case report forms to report data, and data must be manually entered on these forms, which is costly and time consuming. ICAREdata methods use structured, organized data from clinical trials that can be more easily shared instead having to enter free text into electronic health records.

Teachers as Researchers: Practices, Perspectives, Problems, and a Plan of Action (4Ps)

This study explored the role of teachers in action research, extending beyond traditional classroom instruction to include professional development and scholarly inquiry. The primary aim was to investigate the practices involved in action research, teachers’ understanding of writing action research, and common challenges faced. Using a quantitative approach with a descriptive-correlational design, the study surveyed 497 teachers from 87 public schools across five districts in Agusan del Sur. Data collection focused on teachers’ techniques, skills, motivations, and problems related to action research. The study utilized stratified random sampling to ensure diverse representation and analyzed data using descriptive statistics and correlational analysis through IBM SPSS v29. Findings indicated that while teachers generally demonstrated competency and enthusiasm for action research, challenges such as time constraints, insufficient support, and research complexity were prevalent. The study revealed a strong correlation between teachers’ engagement in action research and their understanding of research methodologies and problem-solving abilities. Recommendations included providing ongoing professional development opportunities, enhancing institutional support, establishing mentorship programs, improving access to research resources, and creating recognition systems for research achievements. These measures aimed to bolster teachers’ research skills, overcome barriers, and foster a supportive environment for action research. The study emphasized the importance of further professional development and institutional support to enhance teachers’ competencies and address significant challenges in action research.

New magnetic proxies to reveal source and bioavailability of heavy metals in contaminated soils

Environmental magnetism plays an important role in monitoring heavy metal pollution, but most studies are confined to indicating only the levels of heavy metals using magnetic parameters. This study established new magnetic proxies for accurately depicting the sources and bioavailability of heavy metals in contaminated soils. We observed different relationships between χ and SIRM in the soils contaminated by non-ferrous metal smelting compared to those polluted by coal combustion and steel smelting. Furthermore, we found that the soft magnetic components (IRMsoft) in the soils were mainly controlled by the non-ferrous metal smelting activities, while the hard magnetic components (HIRM) might be affected by the iron erosion. These new magnetic proxies enriched the source composition spectrum and improved the accuracy of the source apportionment analyses (principal component analysis and positive matrix factorization), yielding a result that was comparable to that by Pb isotope fingerprinting. We also found strong relationships between magnetic parameters (especially IRMsoft) and bioavailable fractions of heavy metals, indicating that magnetic measurement may be a powerful tool for monitoring the bioavailability of heavy metals. This study expands the application fields of magnetism in environmental science research.

Systematic Review of Technology-Based STEM Education Research in the United States

This study reviewed technology-based STEM education research papers published during 2015–2022. Data collection and analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Eighty-two papers were analyzed. The findings provide a summary of research trends over the past eight years. Technology education effects on STEM education participation are no longer emphasized when assessing knowledge and skills. Rich research contents, such as environment and experience-related research are emphasized. Furthermore, this study clarified the characteristics of technology education research distribution in various stages of general education and revealed some important research concerns.

Green innovation for a sustainable environment: Modeling the new green knowledge production function for OECD countries

AbstractThe world community faces serious challenges such as climate change, biological loss, and environmental degradation. Developing ecological technologies is vital for coping with these unreversible crises. This study proposed a modified green knowledge production function to achieve sustainable development goals and generate policies for promoting environmental technologies. It examined the impact of market demands, environmental research and development expenditures, human capital, and environmental protection policies on the production of green environment technologies. Panel data included 16 OECD countries from 1990 to 2019. Data analysis strategies included the Gengenbach, Urbain, and Westerlund panel co‐integration test, fully modified least squares, mean group dynamic least squares, and estimators such as feasible generalized least squares and panel‐corrected standard errors for robust checks. A novel panel causality test via a half‐panel jackknife estimator was also applied for causality analyses. The findings showed that human capital, market size, and environmental policy stringency played significant roles in producing environmental technologies and innovations. Although environmental expenditures made by the government positively affected the supply of green innovation, no statistically significant results were observed. The study highlights the importance of enhancing environmental innovation factors by promoting policies, including human capital development, environmental sanctions, and market demands for a sustainable environment.

Exploring the relationship between patient safety culture and the full-range leadership theory in primary care settings: a conceptual analysis

PurposeThis study aims to investigate the conceptual relationship between full-range leadership theory (FRLT) and patient safety culture in primary care settings, aiming to understand how leadership styles influence the development and sustainability of a culture prioritizing patient safety.Design/methodology/approachUsing a conceptual analysis approach, the study builds a theoretical framework that integrates FRLT – which includes transformational, transactional and laissez-faire leadership styles – with the elements of patient safety culture. This framework serves as the basis for a comprehensive literature review, allowing for the formulation of hypotheses regarding the impact of each leadership style on patient safety culture.FindingsThe analysis demonstrates that transformational leadership bolsters patient safety culture by fostering open communication, encouraging error reporting and facilitating continuous improvement. Transactional leadership yields mixed effects, effectively supporting compliance and operational outcomes but showing limitations in promoting a proactive safety culture. Conversely, laissez-faire leadership is associated with negative outcomes for patient safety culture, mainly due to its passive and detached approach.Originality/valueThe originality of this study is rooted in its focused examination of FRLT’s impact on patient safety culture within primary care settings, the development of a unique conceptual framework and its contribution of actionable insights for health-care leadership. These elements collectively advance the understanding of how leadership can enhance patient safety culture, providing a solid foundation for future research and practical application in primary care environments.

Understanding engagement as a catalyst for leadership learning.

This article provides an overview of the theoretical foundations of leadership engagement. The authors synthesize research informing leadership engagement practice and research in college environments. They highlight evidence-based instructional strategies and high-impact practices for effective leadership engagement for college students, including considerations for identity-conscious leadership engagement. The article concludes with reflections on how leadership educators and scholars may continue to evolve practice and research for deeper leadership engagement opportunities.

Machine learning for environmental justice: Dissecting an algorithmic approach to predict drinking water quality in California

The potential for machine learning to answer questions of environmental science, monitoring, and regulatory enforcement is evident, but there is cause for concern regarding potential embedded bias: algorithms can codify discrimination and exacerbate systematic gaps. This paper, organized into two halves, underscores the importance of vetting algorithms for bias when used for questions of environmental science and justice. In the first half, we present a case study of using machine learning for environmental justice-motivated research: prediction of drinking water quality. While performance varied across models and contaminants, some performed well. Multiple models had overall accuracy rates at or above 90 % and F2 scores above 0.60 on their respective test sets. In the second half, we dissect this algorithmic approach to examine how modeling decisions affect modeling outcomes – and not only how these decisions change whether the model is correct or incorrect, but for whom. We find that multiple decision points in the modeling process can lead to different predictive outcomes. More importantly, we find that these choices can result in significant differences in demographic characteristics of false negatives. We conclude by proposing a set of practices for researchers and policy makers to follow (and improve upon) when applying machine learning to questions of environmental science, management, and justice.

Computation of Socio-Economic Status in the AWI-Gen Project.

Socio-economic status of participants in many public health, epidemiological, and genome-wide association studies is an important trait of interest. It is often used in these studies as a measure of direct interest or as a covariate. The Africa Wits INDEPTH Partnership for Genomic and Environmental Research (AWI-Gen) explores genomic and environmental factors in non-communicable diseases, particularly cardio-metabolic disease. In Phase I of AWI-Gen, approximately 12,000 participants were recruited at six sites in four African countries. Participants were asked questions about asset ownership. This technical note describes how AWI-Gen computed socio-economic status from the asset register.

The intersection of film, teaching, and inclusive education

Background: The convergence of film, teaching, and inclusive education research on education has received much attention recently because it can potentially improve educational experiences and create social change. Purpose: This intersection of disciplines provides educators an immense tool for engaging children, cultivating empathy, and addressing social and cultural inequities. Using film's emotional connection and narrative force, educators may build more diverse and efficient learning environments that meet the different needs of their students. Methods: This study used existing data from Scopus to analyze the intersection of film, teaching, and inclusive education using bibliometric quantitative publications studies to assess academic influence. It entails analyzing citations, publication trends, and other bibliographic information. Results: The findings underline the importance of utilizing films to encourage inclusive school practices. Films can reduce socioeconomic inequities, raise cultural knowledge, and improve inclusive teaching techniques. This study underlines the relevance of using film in instructional approaches to promote inclusive education practices and increase learning results. Films are exhibited to promote critical thinking about teaching methods, interpersonal relationships, and classroom ethics. Conclusion: Introducing film into teaching techniques can boost student engagement, improve critical thinking abilities, and raise cultural understanding. Subsequently, this bibliometric study provides a helpful overview of the present research environment in films, teaching, and inclusive education, serving as a foundation for academics to investigate and introduce new modern topics from various regional and cultural perspectives. Implications: Future research should examine the confluence of films, teaching, and inclusive education, as well as the ramifications of these encounters for educational settings, and compare them across study areas and locations or other relevant scopes.

Synthetic Data Generation in Hematology - Paving the Way for OMOP and FHIR Integration.

This study advances the utility of synthetic study data in hematology, particularly for Acute Myeloid Leukemia (AML), by facilitating its integration into healthcare systems and research platforms through standardization into the Observational Medical Outcomes Partnership (OMOP) and Fast Healthcare Interoperability Resources (FHIR) formats. In our previous work, we addressed the need for high-quality patient data and used CTAB-GAN+ and Normalizing Flow (NFlow) to synthesize data from 1606 patients across four multicenter AML clinical trials. We published the generated synthetic cohorts, that accurately replicate the distributions of key demographic, laboratory, molecular, and cytogenetic variables, alongside patient outcomes, demonstrating high fidelity and usability. The conversion to the OMOP format opens avenues for comparative observational multi-center research by enabling seamless combination with related OMOP datasets, thereby broadening the scope of AML research. Similarly, standardization into FHIR facilitates further developments of applications, e.g. via the SMART-on-FHIR platform, offering realistic test data. This effort aims to foster a more collaborative research environment and facilitate the development of innovative tools and applications in AML care and research.

How Data Infrastructure Deals with Bias Problems in Medical Imaging.

The paper discusses biases in medical imaging analysis, particularly focusing on the challenges posed by the development of machine learning algorithms and generative models. It introduces a taxonomy of bias problems and addresses them through a data infrastructure initiative: the PADME (Platform for Analytics and Distributed Machine-Learning for Enterprises), which is a part of the National Research Data Infrastructure for Personal Health Data (NFDI4Health) project. The PADME facilitates the structuring and sharing of health data while ensuring privacy and adherence to FAIR principles. The paper presents experimental results that show that generative methods can be effective in data augmentation. Complying with PADME infrastructure, this work proposes a solution framework to deal with bias in the different data stations and preserve privacy when transferring images. It highlights the importance of standardized data infrastructure in mitigating biases and promoting FAIR, reusable, and privacy-preserving research environments in healthcare.

Annual cyclical variations of urban green space cold island(GSCI) effect by combining remote sensing inversion and ENVI-met simulation

Combining multiple methods offers a practical approach to studying long-term variations in the urban green space cold island(GSCI) effect. This research integrates remote sensing inversion and numerical simulation to investigate the annual cycle of the GSCI in Huachong Park, Hefei City. Initially, 59 remote sensing images from various seasons between 2010 and 2020 were retrieved using Landsat series image data and atmospheric correction methods to invert Land Surface Temperatures(LST), which preliminary identified the GSCI's annual cycle variations. Subsequently, meteorological data for Hefei from 2010 to 2020 were extracted using the Solar Terms Typical Meteorological Day(STTMD) method to obtain representative annual meteorological data. These data were then input into the ENVI-met software for numerical simulations of the study area, capturing diurnal variations of the cold island effect at 24-time points and predicting annual changes in cold island intensity. The results indicate that: (1) The GSCI exhibits an annual cycle and seasonal variations characterized by “strong in summer and weak in winter, cooler in summer and warmer in winter”; (2) A progressive relationship exists between remote sensing inversion and ENVI-met numerical simulation in studying the temporal variation of the GSCI, with the integration of these methods yielding a more comprehensive spatiotemporal analysis of the GSCI over long-term scales;(3) The STTMD method effectively simplifies representative meteorological data, progressively combining remote sensing retrievals and numerical simulations to facilitate the acquisition of comprehensive spatiotemporal variations of the green space heat effect over extended periods. These findings advance understanding of the long-term dynamics of cold island effects within urban green spaces, providing valuable insights for urban planners and environmental researchers.

Project performance factors in the domestic business support system in Kazakhstan: An empirical study

The relevance of the study is due to the importance of assessing projects implemented within the framework of the state business support system, in which entrepreneurs take an active part by submitting applications for various types of support, including investment subsidies, technical assistance in the implementation of quality management systems, export support, and educational programs. The study identified the main factors determining the successful implementation of projects aimed at supporting and stimulating business development. The research design of project implementation effectiveness factors in the business support system is a strategic approach that allows you to organize and structure the analysis of theoretical concepts, and also provides a basis for subsequent experimental research in a real practical project management environment. The research methodology includes the use of structural modeling, which is a validated technique for testing multiple parallel hypotheses about the existence of cause-and-effect relationships. The conducted survey of business entities in several regions of Kazakhstan and the subsequent data analysis confirmed the significance of the identified factors, as a result of which the identified analysis allows to better understand the processes occurring in the business support system and offer practical recommendations for its improvement, which can also serve as a basis for the development of more effective strategies and programs to support entrepreneurship in Kazakhstan.

Dual-mode optical biocompatible-sensor enabled by enzyme-like activity of UiO-66 (Zr) for ultra-sensitive ROS detection in vitro

The simple, effective and highly sensitive detection of hydrogen peroxide (H2O2), which belongs to the reactive oxygen species (ROS), at low concentrations plays an indispensable role in the field of environmental protection, biological research and safety. In this study, a dual-mode optical biosensor, UiO-66@OPD, was developed based on the inherent peroxidase mimicking activity of UiO-66 (Zr) and the optical reaction of ortho-phenylenediamine (OPD) by extending the π-system through oxidative coupling, prototropism and elimination to form OPDox, thereby exhibiting strong orangish absorbance and greenish fluorescence. The catalase-mimicking activity of UiO-66 (Zr) was demonstrated by the catalytic oxidation of methylene blue in the presence of H2O2. Moreover, the Michaelis-Menten kinetic model confirmed the intrinsic peroxidase-like activity of UiO-66@OPD as a modified MOFzyme. The synthesized UiO-66 (Zr) facilitated the oxidation of OPD to OPDox by degrading H2O2 to the hydroxyl radicals. During the oxidation process, the absorption peak at 415 nm and the fluorescence peak at 565 nm of the synthesized probe were significantly enhanced by increasing the H2O2 concentration. Moreover, a colorimetric and fluorometric ultrasensitive sensor shows a good linear relationship between the intensity enhancement and H2O2 concentration in the range of 0–600 nM for absorption and fluorescence spectra with R2 = 0.9772, and R2 = 0.9948, respectively. To demonstrate the biological performance and biocompatibility of UiO-66@OPD as a biosensor, MTT evaluation was performed for the three cell lines MCF-10 A, HEK293 and A549, indicating high biocompatibility and good cell viability for biological applications. Ultimately, this convenient, environmentally friendly, biocompatible and cost-effective catalase-mimicking-based sensor system will open a new perspective for the development of portable kite-based biosensors In vitro.

Using a Structured Research Framework to Improve Mentoring Capacity in a Biophysics Research Lab

ABSTRACT Undergraduate research experiences (UREs) cultivate workforce skills, such as critical thinking, project management, and scientific communication. Many UREs in biophysical research have constraints related to limited resources, often resulting in smaller student cohorts, barriers for students entering a research environment, and fewer mentorship opportunities for graduate students. In response to those limitations, we have created a structured URE model that uses an asynchronous training style paired with direct-tiered mentoring delivered by peers, graduate students, and faculty. The adaptive undergraduate research training and experience (AURTE) framework was piloted as part of the Brown Experiential Learning program, a computational biophysics research lab. The program previously demonstrated substantial increases and improvements in the number of students served and skills developed. Here, we discuss the long-term effectiveness of the framework, impacts on graduate and undergraduate students, and efficacy in teaching research skills and computational-based biophysical methods. The longitudinal impact of our structured URE on student outcomes was analyzed by using student exit surveys, interviews, assessments, and 5 years of feedback from alumni. Results indicate high levels of student retention in research compared with university-wide metrics. Also, student feedback emphasizes how tiered mentoring enhanced research skill retention, while allowing graduate mentors to develop mentorship and workforce skills to expedite research. Responses from alumni affirm that workforce-ready skills (communicating science, data management, and scientific writing) acquired in the program persisted and were used in postgraduate careers. The framework reinforces the importance of establishing, iterating, and evaluating a structured URE framework to foster student success in biophysical research, while promoting mentorship skill training for graduate students. Future work will explore the adaptability of the framework in wet lab environments and probe the potential of AURTE in broader educational contexts.

Building inclusive research cultures in nursing - getting ready for the Research Excellence Framework 2029.

The research environment is a complex ecosystem but is vital for nurturing excellence, vitality and sustainability. The Research Excellence Framework (REF) applies to research in the four nations of the UK. The framework and principles for the next REF have been published, with submissions due in 2028 and the results to be published in 2029. The three elements of the REF have changed and new weightings applied, with 'People, Culture and Environment' forming 25% of the whole. To inform research leaders, investigators, clinical nurse researchers, and doctoral and postdoctoral students about how to prepare dynamic research strategies that prioritise inclusivity in talent management and succession. This article considers inclusivity in research cultures, what has been learned from the previous REF and what more needs to be done collectively across the sector and specifically in nursing. The discussion draws on the author's personal knowledge and experience as a research leader, senior manager and university governor. It is intended to be challenging and practically oriented. The article sets out provocations to shape an agenda for promoting inclusive research cultures to ensure organisational readiness for REF 2029. The article provides pragmatic suggestions for moving forward at pace with making the culture in nursing research more open, transparent and fair.

Co-Constructing “Third Spaces” for Engagement with and for Minoritized Community Groups and Environmental Scientists

The demographics of environmental and Earth scientists are not representative of the UK’s multicultural society. We sought to widen diversity and engage equitably through two related engaged research projects, “Walking the Walk” and “Landscape Stories.” This paper offers a critically reflexive account, based on the methodology of duoethnography, of how we co-constructed a “third space” with and for minoritized community groups and environmental scientists. We sought to create the conditions for inclusive leadership informed by connectedness, respect, humility and intentionality. We argue that for environmental and Earth science research to be more equitable and inclusive, members of project teams should be engaged and more representative of wider society. Following this, collective work to create third spaces requires: respect for diversity and different forms of expertise, knowledge starting points, power dynamics and esteem; a willingness to make connections across disciplines and sub-cultures, actively listening and (un)learning from different (knowledge) cultures; and a commitment to be respectful of hidden and manifest difference, exploring purpose and gaps in knowledge in more holistic ways.

Pushing the Limits of Participatory Video: Exploring Transgressive Voices through Researcher-Participant Minor Video-Making as a Non-Representational Practice

Abstract This article inquires into participatory researchers’ ‘representational’ practices in relation to sharing power with minority participants through collaborative video-making processes. The author argues that there is a limit to attaining such a political and ethical end because a ‘representational’ logic seemingly operates as the theoretical and methodological underpinning for participatory video, which undercuts its ability to represent the voices of collaborators. This article takes into account Shannon Walsh’s (2014) emphasis that “if participatory video is to be a significant method within a project for social change, we must push its limits, and its politics” (p. 140). This article does so by drawing on a ‘non-representational’ approach (Vannini, 2015) and the concept of ‘transgressive voices’ (Jackson & Mazzei, 2009). The author discusses his experience with Deleuze-inspired ‘minor video-making’ as a relational and affective practice in which participants, tangible and intangible research objects and environments, and the researcher himself became relationally entangled to falsify any predetermined essentialized identity and to compose a powerful new body. In the unfolding of transgressive voices in the specific liminal space or moments during the minor video-making, the author ‘intensively and immanently reads’ (Masny & Cole, 2012) the entanglements during storyboarding, rehearsing, shooting, and editing.

Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort.

Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.