Environmental Research Research Articles

Making use of manufacturing process variations: Machine learning approaches for efficient medical and biological study-based image compression and lossless transmission

This research paper explores advanced machine learning techniques for compressing and transmitting medical and biological study-based images without loss of critical diagnostic information. We investigate deep learning architectures including autoencoders, generative adversarial networks (GANs) and transformer models optimized for various medical and biological imaging modalities. Our proposed hybrid compression pipeline combines semantic segmentation, region-adaptive encoding, and learned post-processing to achieve state-of-the-art compression ratios while preserving clinically relevant features. Extensive experiments on large-scale datasets of X-rays, CT scans, MRI scans and microscopy images demonstrate the efficacy of our approach in terms of compression performance, reconstruction quality, and computational efficiency. We also present a novel blockchain-based system for secure and lossless transmission of the compressed medical and biological data. Our findings indicate that machine learning-driven compression can enable more efficient storage and sharing of medical and biological images in resource-constrained healthcare and research environments.

MPPT Optimal Torque Control Strategy for a PMSG-based Wind Turbine Emulator

A wind turbine emulator is a valuable tool, which enables real-time physical simulation of a wind turbine behavior using a dedicated experimental test setup, providing a controlled environment for research and development purposes. One significant advantage of using this tool is its ability to test control algorithms designed to maximize power extraction from the wind turbine. This paper continues previous work on developing a small-scale variable-speed wind turbine emulator based on a Permanent Magnet Synchronous Generator for stand-alone applications (Benhacine et al., 2024). This paper presents enhancements made to the emulator. These improvements facilitate the assessment of a maximum power point tracking control strategy designed to optimize power extraction from the wind turbine.

Advances and future directions of environmental risk research: A bibliometric review

Environmental risk is one of the world’s most significant threats, projected to be the leading risk over the next decade. It has garnered global attention due to increasingly severe environmental issues, such as climate change and ecosystem degradation. Research and technology on environmental risks are gradually developing, and the scope of environmental risk study is also expanding. Here, we developed a tailored bibliometric method, incorporating co-occurrence network analysis, cluster analysis, trend factor analysis, patent primary path analysis, and patent map methods, to explore the status, hotspots, and trends of environment risk research over the past three decades. According to the bibliometric results, the publications and patents related to environmental risk have reached explosive growth since 2018. The primary topics in environmental risk research mainly involve (a) ecotoxicology risk of emerging contaminants (ECs), (b) environmental risk induced by climate change, (c) air pollution and health risk assessment, (d) soil contamination and risk prevention, and (e) environmental risk of heavy metal. Recently, the hotspots of this field have shifted into artificial intelligence (AI) based techniques and environmental risk of climate change and ECs. More research is needed to assess ecological and health risk of ECs, to formulize mitigation and adaptation strategies for climate change risks, and to develop AI-based environmental risk assessment and control technology. This study provides the first comprehensive overview of recent advances in environmental risk research, suggesting future research directions based on current understanding and limitations.

Carbon neutralization frontier tracking

This systematic study on the international research trends in carbon neutrality underscores its critical role in combating global warming and advancing sustainable development. By leveraging the “Web of Science Core Collection” databases and employing CiteSpace software for visual analysis, we examined 2223 research papers to track the influence and trends of key countries, institutions, and authors. Our results reveal a significant increase in publication volume, indicating a robust development potential for carbon neutrality research. The study also identifies environmental science, environmental research, and energy and fuel science as central interdisciplinary hubs, highlighting the importance of cross-disciplinary collaboration. Notably, China leads in publication output but has room for improvement in citation impact, suggesting a need for enhanced research quality and international visibility. The study's findings are instrumental for guiding future research directions, policy-making, and interdisciplinary cooperation, particularly in the fields of environmental science and energy, to accelerate progress towards carbon neutrality and global climate governance.

Machine Learning Algorithms for Water Quality Management Using Total Dissolved Solids (TDS) Data Analysis

Our research project specifically focuses on evaluating groundwater quality in six West Texas counties. We aim to determine whether environmental changes have any impact on the levels of Total Dissolved Solids (TDS) in the water supplied to the public. To achieve this goal, we will be utilizing advanced machine learning algorithms to analyze TDS levels and create geospatial maps for each year between the 1990s and 2010s. To ensure the accuracy of our data, we have gathered information from two trusted sources: the Texas Water Development Board (TWDB) and the Groundwater Database (GWDB). We have analyzed the TDS and other elemental analyses from TWDB–GWDB lab reports and compared them with the quality cutoff set by the World Health Organization (WHO). Our approach involves a thorough examination of the data to identify any emerging patterns. The machine learning algorithm has been successfully trained and tested, producing highly accurate results that effectively predict water quality. Our results have been validated through extensive testing, highlighting the potential of machine learning approaches in the fields of environmental research. Overall, our findings will contribute to the development of more effective policies and regulations in predicting groundwater quality and improving water resource management in Texas. Therefore, this research provides important information for groundwater protection and the development of plans for water resource use in the future.

Engaging athletes as research participants. A document analysis of published sport science literature.

Sport science practitioners utilise findings from peer reviewed research to inform practice. Fewer studies are conducted with high performance athletes, however, than those involving recreationally active participants. Noting that research findings from recreational athletes may not be generalisable to the elite, there is a need to engage the latter cohort in research with better potential to influence health and performance. This study identified methods used to engage and recruit highly trained, elite and world class athletes as research participants. A document analysis was conducted using a purposive sample of peer-reviewed sport science literature. All articles published in 2022 from 18 highly ranked sport science journals were screened for inclusion. Studies investigating athletes ranked as highly trained/national level or above were included. All details related to participant recruitment were extracted from included articles, with the content being coded and thematically analysed using an interpretivist approach. A total of 439 studies from the 2356 screened were included in the analysis. Five primary themes of recruitment strategies were identified, beneath an overarching strategy of purposeful, convenience sampling. Recruitment themes related to the use of a gatekeeper, the research environment providing convenient access to athletes, promoting the study electronically, utilising professional networks and recruiting at training or competition. Engaging athletes through a gatekeeper is a prominent strategy to involve elite athletes in research. It is suggested that researchers work collaboratively with team or organisation personnel to promote recruitment, creating co-designed approaches that address issues most relevant to athletes and staff.

Prenatal exposure to phthalates and childhood wheeze and asthma in the PROGRESS cohort

IntroductionPrenatal phthalate exposure may influence lung development and lead to wheezing and asthma in childhood, and these associations may vary by sex. Despite ubiquity of exposure, there is limited epidemiologic data on these associations in Latin America. MethodsWe assessed 593 mother-child dyads enrolled in the Programming Research in Obesity, Growth, Environment, and Social Stressors birth cohort in Mexico City. We quantified 15 phthalate metabolites in 2nd and 3rd trimester maternal urine. Report of ever wheeze, wheeze in the past 12 months (current wheeze) and ever asthma were obtained using a validated survey when children were 4 and 6 years of age. We examined individual associations with modified Poisson models. Mixture effects were assessed using Bayesian Weighted Quantile Sum (BWQS) regression. All models were adjusted for child's sex, maternal age and education at enrollment, and parity. ResultsIn Poisson models, a doubling of mono (carboxy-isononyl) phthalate (MCNP) during the 2nd trimester was associated with higher risk of wheeze (RR: 1.14, 95 % CI: 1.01, 1.29), and asthma (RR: 1.44, 95 % CI: 1.05, 1.97) at 4 years of age. Higher concentrations of the sum of di-isononyl phthalate metabolites (∑DiNP) during the 2nd trimester were also associated with asthma at 4 years of age (RR: 1.30, 95 % CI: 1.04, 1.61). Mixture associations of phthalate metabolite concentrations during the 2nd trimester and asthma at 4 years of age were stronger in males (BWQS, OR: 1.94, 95 % CI: 0.90, 4.60; 90 % CrI: 1.04, 3.73) compared to females (BWQS, OR: 1.23, 95 % CI: 0.56, 2.88; 90 % CrI: 0.61, 2.55). Additionally, we observed stronger inverse associations between prenatal phthalate mixtures during the 3rd trimester and current wheeze at 4 and 6 years of age in females (BWQS, OR: 0.54, 90 % CrI: 0.35, 0.82; OR: 0.45, 90 % CrI: 0.22, 0.84) compared to males (BWQS, OR: 0.95, 90 % Cri: 0.68, 1.35; OR: 0.97, 90 % CrI: 0.59, 1.54). ConclusionsPrenatal phthalate metabolite concentrations were associated with respiratory outcomes in childhood, with some evidence of sex specific effects. Future work investigating phthalate exposure and wheeze trajectories/lung function will be important for understanding how these may predict later disease.

Bio-Optical Properties and Ocean Colour Satellite Retrieval along the Coastal Waters of the Western Iberian Coast (WIC)

Essential Climate Variables (ECVs) like ocean colour provide crucial information on the Optically Active Constituents (OACs) of seawater, such as phytoplankton, non-algal particles, and coloured dissolved organic matter (CDOM). The challenge in estimating these constituents through remote sensing is in accurately distinguishing and quantifying optical and biogeochemical properties, e.g., absorption coefficients and the concentration of chlorophyll a (Chla), especially in complex waters. This study evaluated the temporal and spatial variability of bio-optical properties in the coastal waters of the Western Iberian Coast (WIC), contributing to the assessment of satellite retrievals. In situ data from three oceanographic cruises conducted in 2019–2020 across different seasons were analyzed. Field-measured biogenic light absorption coefficients were compared to satellite estimates from Ocean-Colour Climate Change Initiative (OC-CCI) reflectance data using semi-analytical approaches (QAA, GSM, GIOP). Key findings indicate substantial variability in bio-optical properties across different seasons and regions. New bio-optical coefficients improved satellite data retrieval, reducing uncertainties and providing more reliable phytoplankton absorption estimates. These results highlight the need for region-specific algorithms to accurately capture the unique optical characteristics of coastal waters. Improved comprehension of bio-optical variability and retrieval techniques offers valuable insights for future research and coastal environment monitoring using satellite ocean colour data.

Differences in changes of data completeness after the implementation of an electronic medical record in three surgical departments of a German hospital–a longitudinal comparative document analysis

PurposeThe European health data space promises an efficient environment for research and policy-making. However, this data space is dependent on high data quality. The implementation of electronic medical record systems has a positive impact on data quality, but improvements are not consistent across empirical studies. This study aims to analyze differences in the changes of data quality and to discuss these against distinct stages of the electronic medical record’s adoption process.MethodsPaper-based and electronic medical records from three surgical departments were compared, assessing changes in data quality after the implementation of an electronic medical record system. Data quality was operationalized as completeness of documentation. Ten information that must be documented in both record types (e.g. vital signs) were coded as 1 if they were documented, otherwise as 0. Chi-Square-Tests were used to compare percentage completeness of these ten information and t-tests to compare mean completeness per record type.ResultsA total of N = 659 records were analyzed. Overall, the average completeness improved in the electronic medical record, with a change from 6.02 (SD = 1.88) to 7.2 (SD = 1.77). At the information level, eight information improved, one deteriorated and one remained unchanged. At the level of departments, changes in data quality show expected differences.ConclusionThe study provides evidence that improvements in data quality could depend on the process how the electronic medical record is adopted in the affected department. Research is needed to further improve data quality through implementing new electronical medical record systems or updating existing ones.

An exploratory graphical analysis of the Montgomery-Åsberg Depression Rating Scale pre- and post-treatment using pooled antidepressant trial secondary data

BackgroundThe 10-item Montgomery-Åsberg Depression Rating Scale (MADRS) is a commonly used measure of depression in antidepressant clinical trials. Numerous studies have adopted classical test theory perspectives to assess the psychometric properties of this scale, finding generally positive results. However, its network configural structure and stability is unexplored across different time-points and treatment groups. AimsTo assess the network structure and stability of the MADRS in clinical settings pre- and post-treatment, and to determine a configurally invariant and stable model across time-points and treatment groups (placebo and intervention). MethodIndividual participant data for 6440 participants from 14 clinical trials of major depressive disorder was obtained from the data repository Vivli.org. Exploratory Graphical Analysis (EGA) was used to identify empirical models pre-treatment (baseline) and post-treatment (8-week outcome). Bootstrapping techniques were applied to obtain optimised configurally invariant models. ResultsEmpirical models presented with performance issues at baseline and for the placebo group at outcome. An abbreviated 8-item single-community model was found to be stable and configurally invariant across time-points and treatment groups. Symptoms such as low mood and lassitude showed most centrality across all models. LimitationsMetric invariance could not be explored due to research environment limitations. ConclusionsAn 8-item one-community variant of the MADRS may provide optimal performance when conducting network analyses of antidepressant clinical trial outcomes. Findings suggest that interventions targeting low mood and lassitude might be most efficacious in treating depression among clinical trial participants. Further considerations of the potential impact on trial design and analysis should be explored.

Accelerated Longitudinal Glycemic Changes in Relation to Urinary Toxic/Essential Metals and Metal Mixtures Among Mexican Americans Living in Starr County, Texas.

Metal and metalloid exposures (hereafter "metals") are associated with adverse health outcomes, including type 2 diabetes; however, previous studies were largely cross-sectional or underpowered. Furthermore, underserved racial and ethnic groups are underrepresented in environmental health research despite having higher rates of type 2 diabetes and a greater risk of metal exposures. Consequently, we evaluated continuous glycemic traits in relation to baseline urinary toxic metal, essential metal, and metal mixtures in a cohort of Mexican American adults. A total of 510 participants were selected based upon self-reported diabetes status and followed over 3 years. Urinary metals were assessed at baseline. Linear mixed-effects models were used to estimate per-month changes in hemoglobin A1c, fasting plasma glucose, and postload glucose in relation to urinary metal levels. Multiple statistical approaches were used to assess the associations between glycemic traits and metal mixtures. After adjustment, higher urinary levels of arsenic, selenium, copper, molybdenum, nickel, and tin were associated with faster increases in measures of glycemia. The toxic metal mixture composed of arsenic, lead, cadmium, nickel, and tin was associated with faster increases in postload glucose. Using postload glucose criteria, highest versus lowest arsenic was predicted to accelerate conversion of normoglycemia to prediabetes and diabetes by 23 and 65 months, respectively. In this underrepresented, high-risk Mexican American population, exposure to toxic metals and alterations in essential metal homeostasis were associated with faster increases in glycemia over time that may accelerate type 2 diabetes development.

Mary Main: portrait and tribute

ABSTRACT Brett Kahr has referred to the death of Mary Main as the loss of “the queen of attachment research.” However, how well is Main’s work actually known? In this portrait and tribute, I argue that Main’s transformative contributions have become a taken-for-granted part of the basic environment of attachment research, but that readers have faced obstacles in understanding and responding to their strengths and limitations. Drawing from interviews with Main, in this paper I describe some of her early life experiences and mental and physical health challenges, which she felt had influenced what was possible for her in her research. I then highlight less well-known aspects of her ideas regarding the role of attention within attachment strategies, the nature of disorganised attachment, the implications of alarming caregiving behaviours, and what is ultimately measured by the Adult Attachment Interview. My goal throughout is to help reader see both how much Main’s rich and exciting works still have to teach, and identify their many loose threads still to follow.

Perspectives on inequalities in social science research and environmental sustainability in India

While the Anthropocene has seen the dissolution of nature-society dualism, disciplinary barriers have been deep-seated and limited a complete integrated analysis of the environmental crisis. Concerned by the scarcity of literature on structural and institutional barriers that leverage upon and extend this deep-seated duality between natural and social science, this paper maps the broader field of social science research in India. It also explores the epistemic effects of such inequalities on environmental research and its ability to meaningfully engage with interactions between the social and the natural worlds.

Association Between Glycemic Variability and Persistent Acute Kidney Injury After Noncardiac Major Surgery: A Multicenter Retrospective Cohort Study.

While the relationship between glycemic variability (GV) and acute kidney injury (AKI) has been a subject of interest, the specific association of GV with persistent AKI beyond 48 hours postoperative after noncardiac surgery is not well-established. This retrospective cohort study aimed to describe the patterns of different GV metrics in the immediate 48 hours after noncardiac surgery, evaluate the association between GV indices and persistent AKI within the 7-day postoperative window, and compare the risk identification capabilities of various GV for persistent AKI. A total of 10,937 patients who underwent major noncardiac surgery across 3 medical centers in eastern China between January 2015 and September 2023 were enrolled. GV was characterized using the coefficient of variations (CV), mean amplitude of glycemic excursions (MAGE), and the blood glucose risk index (BGRI). Multivariable logistic regression was used to examine the relationship between GV and AKI. Optimal cutoff values for GV metrics were calculated through the risk identification models, and an independent cohort from the INformative Surgical Patient dataset for Innovative Research Environment (INSPIRE) database with 7714 eligible cases served to externally validate the risk identification capability. Overall, 274 (2.5%) of the 10,937 patients undergoing major noncardiac surgery met the criteria of persistent AKI. Higher GV was associated with an increased risk of persistent AKI (CV: odds ratio [OR] = 1.26, 95% confidence interval [CI], 1.08-1.46; MAGE: OR = 1.31, 95% CI, 1.15-1.49; BGRI: OR = 1.18, 95% CI, 1.08-1.29). Compared to models that did not consider glycemic factors, MAGE and BGRI independently contributed to predicting persistent AKI (MAGE: areas under the curve [AUC] = 0.768, P = .011; BGRI: AUC = 0.764, P = .014), with cutoff points of 3.78 for MAGE, and 3.02 for BGRI. The classification of both the internal and external validation cohorts using cutoffs demonstrated good performance, achieving the best AUC values of 0.768 for MAGE in the internal cohort and 0.777 for MAGE in the external cohort. GV measured within 48 hours postoperative period is an independent risk factor for persistent AKI in patients undergoing noncardiac surgery. Specific cutoff points can be used to stratify at-risk patients. These findings indicate that stabilizing GV may potentially mitigate adverse kidney outcomes after noncardiac surgery, highlighting the importance of glycemic control in the perioperative period.

Ethical analysis of informed consent methods in longitudinal cohort studies: A Chinese perspective.

In longitudinal cohort studies involving large populations over extended periods, informed consent entails numerous urgent challenges. This paper explores challenges regarding informed consent in long-term, large-scale longitudinal cohort studies based on the longitudinal and dynamic nature of such research. It analyzes and evaluates widely recognized broad consent and dynamic consent methods, highlighting limitations concerning their ability to adapt to evolving research objectives and participant perspectives. This paper discusses trust-based informed consent and emphasizes the needs to establish and maintain trust with research participants and to balance information disclosure with respect for participants' autonomy. Informed consent in long-term studies is an evolving process that must adapt to changing research environments. Based on participant trust, researchers should observe and assess potential research risks. Finally, the paper recommends enhancing institutional credibility, implementing reconsent procedures, and ensuring robust ethical oversight to safeguard participants' rights despite the complexity of modern biomedical research.

Child Health and Mortality Prevention Surveillance (CHAMPS): Manhiça site description, Mozambique

The Manhiça Health Research Centre (Manhiça HDSS) was established in 1996 in Manhiça, a rural district at Maputo Province in the southern part of Mozambique with approximately 49,000 inhabited households, a total population of 209.000 individuals, and an annual estimated birth cohort of about 5000 babies. Since 2016, Manhiça HDSS is implementing the Child Health and Mortality Prevention Surveillance (CHAMPS) program aiming to investigate causes of death (CoD) in stillbirths and children under the age of 5 years using an innovative post-mortem technique known as Minimally Invasive Tissue sampling (MITS), comprehensive pathogen screening using molecular methods, clinical record abstraction and verbal autopsy. Both in-hospital and community pediatric deaths are investigated using MITS. For this, community-wide socio-demographic approaches (notification of community deaths by key informants, formative research involving several segments of the community, availability of free phone lines for notification of medical emergencies and deaths, etc.) are conducted alongside to foster community awareness, involvement and adherence as well as to compute mortality estimates and collect relevant information of health and mortality determinants. The main objective of this paper is to describe the Manhiça Health and Demographic Surveillance System (HDSS) site and the CHAMPS research environment in place including the local capacities among its reference hospital, laboratories, data center and other relevant areas involved in this ambitious surveillance and research project, whose ultimate aim is to improve child survival through public health actions derived from credible estimates and understanding of the major causes of childhood mortality in Mozambique.

Artificial Intelligence in Pain Management: Advancing Translational Science in Digital Health Research from Bench to Bedside

Artificial Intelligence (AI) is rapidly transforming the landscape of healthcare, with particularly profound implications in the field of pain management. This chapter delves into the integration of AI-driven tools that revolutionize the way pain is assessed, monitored, and treated. Through the use of predictive modeling, real-time monitoring, and personalized treatment plans, AI significantly enhances the precision, efficiency, and effectiveness of pain management strategies. The discussion extends to various AI applications, shedding light on the ethical considerations that accompany these technological advancements, as well as outlining future research directions. Collectively, these insights underscore the immense potential of AI to not only improve pain management practices but also to significantly elevate patient outcomes. Central to this transformation is the role of translational science in bridging the gap between theoretical AI models and their practical, clinical applications. This "bench to bedside" approach ensures that innovations in AI are not merely confined to research environments but are actively integrated into real-world patient care. For instance, AI-powered predictive analytics in pain management, driven by sophisticated machine learning algorithms, have progressed from computational experiments to clinical trials, and ultimately, to widespread implementation in healthcare settings. These AI models are now being utilized in hospitals to assess patient pain levels in real-time, predict opioid requirements, and optimize pain management protocols. This progression exemplifies how translational science is facilitating a paradigm shift in healthcare, positioning AI as an indispensable tool in modern pain management.

Mineral Composition and Distribution of Silt and Oxides in Shatt Al-Arab Deposits, Basra, Iraq

Understanding the mineral composition and distribution of river deposits is crucial for environmental management, resource utilization, and geological studies. This study aims to investigate the mineral composition and distribution of silt and oxides in the deposits of the Shatt al-Arab River, specifically in the Faw area of Basra Governorate. Samples were collected from depths of 0-30, 30-60, and 60-90 cm. The silt fraction (2-53 micrometers) was isolated, and mineral analysis was conducted using XRD and a point-counting device. Morphological characteristics were determined using a polarized light microscope. XRD analysis revealed the presence of minerals such as quartz, calcite, dolomite, smectite, kaolinite, chlorite, illite, hematite, magnetite, and albite at varying depths. Polarized light microscopy identified additional minerals within light and heavy fractions. The study found a dominance of monocrystalline quartz and opaque minerals, with magnetite being more prevalent than hematite across all depths and a high correlation coefficient with depth (0.96). Depth-specific variations in mineral composition suggest the need for further research in different locations and at greater depths. These findings provide insights into sedimentary processes and potential resource utilization in the region, making this study significant for regional geological and environmental research. Future research should focus on the temporal changes in mineral composition and their impact on sediment dynamics to enhance the understanding of regional geological history and resource management.

De novo Cu-MOF@CNS nanocomposite coated on a cotton fibrils framework for sustainable solar-driven desalination.

Environmental researchers are extremely concerned about addressing the declining availability of drinking water, which is a critical issue in many nations. Solar-driven desalination is an emerging and pioneering renewable approach to reduce potable water scarcity that is suitable for remote locations, developing countries, and disaster zones as it does not require additional energy supply. However, there are still issues with the scalable preparation of photothermal materials, such as achieving low cost and widening the assortment of useful applications. Conventional carbon- and metal-based absorbers are intricate and fragile, which make them difficult to install and transport in places with minimal infrastructure. Thus, a universal process for creating adaptable solar evaporators is sorely required. Herein, we have come up with a holistic approach using a solar absorber (GJ-01(Cal)) derived from a Cu-MOF (HKUST-1) and carbon nanosheets (CNSs) for generating potable water from saline water using solar radiation. The as-synthesized material provides high-performance photothermal water evaporation when illuminated under solar irradiation at the air-water interface. Moreover, its porous structure, high photothermal conversion efficiency, rapid water flow, and heat insulation make it appropriate for saline water desalination. CNS play a pivotal role in improving the photothermal features of the solar absorber (GJ-01(Cal)) in terms of conjugation to promote Cu(0) species and pyrrolic nitrogen (P-N) amplification and thereby enrich the p-type nature of the absorber's triphasic configuration. With these photothermal factors, the localised surface plasmon resonance (LSPR) of electrons increases and achieves high solar spectrum absorption. The GJ-01(Cal) was further coated on porous cotton fibrils (CF) that regulate photothermal interfacial evaporation (PTIE) by allowing water transportation via capillary action. This assemblage of the nanocomposite on CF efficiently evaporates water at a higher surface temperature of ∼47 °C under one solar illumination, achieving 4.23 kg m-2 h-1 of evaporation flux and 96.5% light-to-heat conversion efficiency. Interestingly, the GJ-01(Cal) coated over CF can be recycled at least 10 times. Additionally, it offers scalable production for higher photothermal efficiency with a flexible substrate as a solar evaporator and is beneficial for society paving new horizons towards a sustainable environment.

What if we consider research teams as teams?

Research teams are an important means by which knowledge is generated in Health Professions Education (HPE). Although funding agencies encourage the formation of interdisciplinary and interprofessional research teams, we know little about how our interdisciplinary and interprofessional research teams are functioning, nor how best to ensure their success. Indeed, while HPE Scholarship Units and research environments have been the object of study, little work has been focused on research teams themselves. In this article, the authors propose that research teams should be studied as unique instantiations of teams where several individuals work together towards a common goal. Considering research teams as a team can encourage attention to how effective teams are built, supported, and celebrated, it can acknowledge that competent individuals may form incompetent teams, and it opens important avenues for future research. Turning our attention to better understanding how and when research teams thrive should support the development of more effective teams; resulting in reduced waste and redundancy, better mobilization of team members' time and skills, and enhanced knowledge generation. Considering research teams as teams, encourages an understanding that these teams require care, commitment, and effort to sustain them, and it acknowledges that pursuing research in a team context is both a collaborative and a social endeavour.