Insights into PFAS contaminants before and after sewage discharges into a marine protected harbour.

Per and polyfluoroalkyl substances (PFAS) and their degradation products are a concern to human and ecosystem health. Wastewater treatment plants are not efficient at removing PFAS compounds and are thought to be a major source of these compounds to marine environments. The sewerage infrastructure in the UK, has over 20,000 combined stormwater overflows (CSOs). These CSOs are relief values whereby untreated wastewater can discharge under permit from the Environment Agency with exceptional rain/snowfall conditions. CSOs discharged 3.6 million monitored hours of untreated wastewater into English rivers and coasts in 2023. Concerns have been raised about the proximity of these CSO discharges to highly protected marine habitats. This study is the first to determine that PFAS concentrations are elevated in a highly protected marine bay (Langstone Harbour, England) following recent sewage releases compared to an extended period without discharge. Analysis was carried out into a suite of 54 PFAS compounds of which only one (PFHpA) was detectable above LOD prior to discharges but 8 afterwards. These included banned PFOS (Linear and Branched 8.6 ng/L ∓ 0.90) and PFOA (2.9 ng/L ∓ 0.29) which were above annual average EQS for inland and 'other' surface waters. Most of the PFAS compounds detected doubled in concentration above LODs. These two-fold increases we discuss are likely conservative estimates based on the use of LODs and tidal conditions. Additional Oysters (Crassostrea gigas) and Seaweed (Fucus vesiculosus) were taken revealing high concentrations of the shorter chain PFBA (6.99µg/kg ∓ 2.42 ww) in seaweed samples. These seaweeds were calculated to have conservative bioaccumulation factors (BAF) > 6000 for PFBA indicating these algae might be an important reservoir of some PFAS contamination. We discuss these results in the context of the largescale discharges of untreated wastewater nationally and globally, and call upon a need for a better understanding of the transfer of PFAS contaminants into marine food chains.

EthoCRED: a framework to guide reporting and evaluation of the relevance and reliability of behavioural ecotoxicity studies.

Behavioural analysis has been attracting significant attention as a broad indicator of sub-lethal toxicity and has secured a place as an important subdiscipline in ecotoxicology. Among the most notable characteristics of behavioural research, compared to other established approaches in sub-lethal ecotoxicology (e.g. reproductive and developmental bioassays), are the wide range of study designs being used and the diversity of endpoints considered. At the same time, environmental hazard and risk assessment, which underpins regulatory decisions to protect the environment from potentially harmful chemicals, often recommends that ecotoxicological data be produced following accepted and validated test guidelines. These guidelines typically do not address behavioural changes, meaning that these, often sensitive, effects are not represented in hazard and risk assessments. Here, we propose a new tool, the EthoCRED evaluation method, for assessing the relevance and reliability of behavioural ecotoxicity data, which considers the unique requirements and challenges encountered in this field. This method and accompanying reporting recommendations are designed to serve as an extension of the "Criteria for Reporting and Evaluating Ecotoxicity Data (CRED)" project. As such, EthoCRED can both accommodate the wide array of experimental design approaches seen in behavioural ecotoxicology, and could be readily implemented into regulatory frameworks as deemed appropriate by policy makers of different jurisdictions to allow better integration of knowledge gained from behavioural testing into environmental protection. Furthermore, through our reporting recommendations, we aim to improve the reporting of behavioural studies in the peer-reviewed literature, and thereby increase their usefulness to inform chemical regulation.

Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification.

Deep learning (DL) models for medical image classification frequently struggle to generalize to data from outside institutions. Additional clinical data are also rarely collected to comprehensively assess and understand model performance amongst subgroups. Following the development of a single-center model to identify the lung sliding artifact on lung ultrasound (LUS), we pursued a validation strategy using external LUS data. As annotated LUS data are relatively scarce-compared to other medical imaging data-we adopted a novel technique to optimize the use of limited external data to improve model generalizability. Externally acquired LUS data from three tertiary care centers, totaling 641 clips from 238 patients, were used to assess the baseline generalizability of our lung sliding model. We then employed our novel Threshold-Aware Accumulative Fine-Tuning (TAAFT) method to fine-tune the baseline model and determine the minimum amount of data required to achieve predefined performance goals. A subgroup analysis was also performed and Grad-CAM++ explanations were examined. The final model was fine-tuned on one-third of the external dataset to achieve 0.917 sensitivity, 0.817 specificity, and 0.920 area under the receiver operator characteristic curve (AUC) on the external validation dataset, exceeding our predefined performance goals. Subgroup analyses identified LUS characteristics that most greatly challenged the model's performance. Grad-CAM++ saliency maps highlighted clinically relevant regions on M-mode images. We report a multicenter study that exploits limited available external data to improve the generalizability and performance of our lung sliding model while identifying poorly performing subgroups to inform future iterative improvements. This approach may contribute to efficiencies for DL researchers working with smaller quantities of external validation data.

The effects of plastic additives on swimming activity and startle response in marine amphipod Echinogammarus marinus.

Plastic additives are widely used in plastic production and are found in the environment owing to their widespread applications. Among these additives, N-butyl benzenesulfonamide (NBBS) and triphenyl phosphate (TPHP) are under international watchlist for evaluation, with limited studies on amphipods. Di-ethylhexyl phthalate (DEHP) and dibutyl phthalate (DBP) are banned in some countries and categorised as substances of very high concern. This study aimed to investigate the effects of NBBS, TPHP, DEHP and DBP on the swimming activity of a coastal intertidal marine amphipod, Echinogammarus marinus. Furthermore, this study is the first to quantify startle response in E. marinus in response to light stimuli. Amphipods were exposed to 0, 0.5, 5, 50 and 500 µg/l concentrations of all test compounds. Swimming activity and startle responses were assessed by video tracking and analysis using an 8-min alternating dark and light protocol after exposure on days 7 and 14. We observed an overall compound and light effect on the swimming activity of E. marinus. A significant decrease in swimming distance was found in 500 µg/l NBBS and TPHP. We observed that the startle response in E. marinus had a latency period of >2 s and animals were assessed at 1 s and the sum of the first 5 s. There was a clear startle response in E. marinus during dark to light transition, evident with increased swimming distance. NBBS exposure significantly increased startle response at environmental concentrations, while significant effects were only seen in 500 µg/l TPHP at 5 s. We found no significant effects of DEHP and DBP on swimming behaviour at the concentrations assessed. The findings of this study affirm the necessity for a continuous review of plastic additives to combat adverse behavioural effects that may be transferable to the population levels.

Evaluation of precopulatory pairing behaviour and male fertility in a marine amphipod exposed to plastic additives.

Plastics contain a mixture of chemical additives that can leach into the environment and potentially cause harmful effects on reproduction and the endocrine system. Two of these chemicals, N-butyl benzenesulfonamide (NBBS) and triphenyl phosphate (TPHP), are among the top 30 organic chemicals detected in surface and groundwater and are currently placed on international watchlist for evaluation. Although bans have been placed on legacy pollutants such as diethylhexyl phthalate (DEHP) and dibutyl phthalate (DBP), their persistence remains a concern. This study aimed to examine the impact of plastic additives, including NBBS, TPHP, DBP, and DEHP, on the reproductive behaviour and male fertility of the marine amphipod Echinogammarus marinus. Twenty precopulatory pairs of E. marinus were exposed to varying concentrations of the four test chemicals to assess their pairing behaviour. A high-throughput methodology was developed and optimised to record the contact time and re-pair time within 15 min and additional point observations for 96 h. The study found that low levels of NBBS, TPHP, and DEHP prolonged the contact and re-pairing time of amphipods and the proportion of pairs reduced drastically with re-pairing success ranging from 75% to 100% in the control group and 0%-85% in the exposed groups at 96 h. Sperm count declined by 40% and 60% in the 50 µg/l and 500 µg/l DBP groups, respectively, whereas TPHP resulted in significantly lower sperms in 50 µg/l exposed group. Animals exposed to NBBS and DEHP showed high interindividual variability in all exposed groups. Overall, this study provides evidence that plastic additives can disrupt the reproductive mechanisms and sperm counts of amphipods at environmentally relevant concentrations. Our research also demonstrated the usefulness of the precopulatory pairing mechanism as a sensitive endpoint in ecotoxicity assessments to proactively mitigate population-level effects in the aquatic environment.

Conflicts of Interest in the Assessment of Chemicals, Waste, and Pollution.

Pollution by chemicals and waste impacts human and ecosystem health on regional, national, and global scales, resulting, together with climate change and biodiversity loss, in a triple planetary crisis. Consequently, in 2022, countries agreed to establish an intergovernmental science-policy panel (SPP) on chemicals, waste, and pollution prevention, complementary to the existing intergovernmental science-policy bodies on climate change and biodiversity. To ensure the SPP's success, it is imperative to protect it from conflicts of interest (COI). Here, we (i) define and review the implications of COI, and its relevance for the management of chemicals, waste, and pollution; (ii) summarize established tactics to manufacture doubt in favor of vested interests, i.e., to counter scientific evidence and/or to promote misleading narratives favorable to financial interests; and (iii) illustrate these with selected examples. This analysis leads to a review of arguments for and against chemical industry representation in the SPP's work. We further (iv) rebut an assertion voiced by some that the chemical industry should be directly involved in the panel's work because it possesses data on chemicals essential for the panel's activities. Finally, (v) we present steps that should be taken to prevent the detrimental impacts of COI in the work of the SPP. In particular, we propose to include an independent auditor's role in the SPP to ensure that participation and processes follow clear COI rules. Among others, the auditor should evaluate the content of the assessments produced to ensure unbiased representation of information that underpins the SPP's activities.

Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU.

OBJECTIVES: To evaluate the accuracy of a bedside, real-time deployment of a deep learning (DL) model capable of distinguishing between normal (A line pattern) and abnormal (B line pattern) lung parenchyma on lung ultrasound (LUS) in critically ill patients. DESIGN: Prospective, observational study evaluating the performance of a previously trained LUS DL model. Enrolled patients received a LUS examination with simultaneous DL model predictions using a portable device. Clip-level model predictions were analyzed and compared with blinded expert review for A versus B line pattern. Four prediction thresholding approaches were applied to maximize model sensitivity and specificity at bedside. SETTING: Academic ICU. PATIENTS: One-hundred critically ill patients admitted to ICU, receiving oxygen therapy, and eligible for respiratory imaging were included. Patients who were unstable or could not undergo an LUS examination were excluded. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 100 unique ICU patients (400 clips) were enrolled from two tertiary-care sites. Fifty-six patients were mechanically ventilated. When compared with gold standard expert annotation, the real-time inference yielded an accuracy of 95%, sensitivity of 93%, and specificity of 96% for identification of the B line pattern. Varying prediction thresholds showed that real-time modification of sensitivity and specificity according to clinical priorities is possible. CONCLUSIONS: A previously validated DL classification model performs equally well in real-time at the bedside when platformed on a portable device. As the first study to test the feasibility and performance of a DL classification model for LUS in a dedicated ICU environment, our results justify further inquiry into the impact of employing real-time automation of medical imaging into the care of the critically ill.

Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View.

BACKGROUND: Annotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency. METHODS: We trained a machine learning model to accurately distinguish between one of two classes of lung ultrasound (LUS) views using 2908 clips from a larger dataset. Partitioning the remaining dataset by view would reduce downstream labelling efforts by enabling annotators to focus on annotating pathological features specific to each view. RESULTS: In a sample view-specific annotation task, we found that automatically partitioning a 780-clip dataset by view saved 42 min of manual annotation time and resulted in 55±6 additional relevant labels per hour. CONCLUSIONS: Automatic partitioning of a LUS dataset by view significantly increases annotator efficiency, resulting in higher throughput relevant to the annotating task at hand. The strategy described in this work can be applied to other hierarchical annotation schemes.

Accurate assessment of the lung sliding artefact on lung ultrasonography using a deep learning approach.

Pneumothorax is a potentially life-threatening condition that can be rapidly and accurately assessed via the lung sliding artefact generated using lung ultrasound (LUS). Access to LUS is challenged by user dependence and shortage of training. Image classification using deep learning methods can automate interpretation in LUS and has not been thoroughly studied for lung sliding. Using a labelled LUS dataset from 2 academic hospitals, clinical B-mode (also known as brightness or two-dimensional mode) videos featuring both presence and absence of lung sliding were transformed into motion (M) mode images. These images were subsequently used to train a deep neural network binary classifier that was evaluated using a holdout set comprising 15% of the total data. Grad-CAM explanations were examined. Our binary classifier using the EfficientNetB0 architecture was trained using 2535 LUS clips from 614 patients. When evaluated on a test set of data uninvolved in training (540 clips from 124 patients), the model performed with a sensitivity of 93.5%, specificity of 87.3% and an area under the receiver operating characteristic curve (AUC) of 0.973. Grad-CAM explanations confirmed the model's focus on relevant regions on M-mode images. Our solution accurately distinguishes between the presence and absence of lung sliding artefacts on LUS.

Biomimetic generation of the strongest known biomaterial found in limpet tooth.

The biomaterial with the highest known tensile strength is a unique composite of chitin and goethite (α-FeO(OH)) present in teeth from the Common Limpet (Patella vulgata). A biomimetic based on limpet tooth, with corresponding high-performance mechanical properties is highly desirable. Here we report on the replication of limpet tooth developmental processes ex vivo, where isolated limpet tissue and cells in culture generate new biomimetic structures. Transcriptomic analysis of each developmental stage of the radula, the organ from which limpet teeth originate, identifies sequential changes in expression of genes related to chitin and iron processing. We quantify iron and chitin metabolic processes in the radula and grow isolated radula cells in vitro. Bioinspired material can be developed with electrospun chitin mineralised by conditioned media from cultured radula cells. Our results inform molecular processes behind the generation of limpet tooth and establish a platform for development of a novel biomimetic with comparable properties.

Priorities to inform research on marine plastic pollution in Southeast Asia.

Southeast Asia is considered to have some of the highest levels of marine plastic pollution in the world. It is therefore vitally important to increase our understanding of the impacts and risks of plastic pollution to marine ecosystems and the essential services they provide to support the development of mitigation measures in the region. An interdisciplinary, international network of experts (Australia, Indonesia, Ireland, Malaysia, the Philippines, Singapore, Thailand, the United Kingdom, and Vietnam) set a research agenda for marine plastic pollution in the region, synthesizing current knowledge and highlighting areas for further research in Southeast Asia. Using an inductive method, 21 research questions emerged under five non-predefined key themes, grouping them according to which: (1) characterise marine plastic pollution in Southeast Asia; (2) explore its movement and fate across the region; (3) describe the biological and chemical modifications marine plastic pollution undergoes; (4) detail its environmental, social, and economic impacts; and, finally, (5) target regional policies and possible solutions. Questions relating to these research priority areas highlight the importance of better understanding the fate of marine plastic pollution, its degradation, and the impacts and risks it can generate across communities and different ecosystem services. Knowledge of these aspects will help support actions which currently suffer from transboundary problems, lack of responsibility, and inaction to tackle the issue from its point source in the region. Being profoundly affected by marine plastic pollution, Southeast Asian countries provide an opportunity to test the effectiveness of innovative and socially inclusive changes in marine plastic governance, as well as both high and low-tech solutions, which can offer insights and actionable models to the rest of the world.

Shipping in the north-east Atlantic: Identifying spatial and temporal patterns of change.

Maritime traffic is increasing globally, with a four-fold increase in commercial vessel movements between 1992 and 2012. Vessels contribute to noise and air pollution, provide pathways for non-native species, and collide with marine wildlife. While knowledge of shipping trends and potential environmental impacts exists at both local and global levels, key information on vessel density for regional-scale management is lacking. This study presents the first in-depth spatio-temporal analysis of shipping in the north-east Atlantic region, over three years in a five-year period. Densities increased by 34%, including in 73% of Marine Protected Areas. Western Scotland and the Bay of Biscay experienced the largest increases in vessel density, predominantly from small and slow vessels. Given well-documented impacts that shipping can have on the marine environment, it is crucial that this situation continues to be monitored - particularly in areas designated to protect vulnerable species and ecosystems which may already be under pressure.

Frontiers in quantifying wildlife behavioural responses to chemical pollution.

Animal behaviour is remarkably sensitive to disruption by chemical pollution, with widespread implications for ecological and evolutionary processes in contaminated wildlife populations. However, conventional approaches applied to study the impacts of chemical pollutants on wildlife behaviour seldom address the complexity of natural environments in which contamination occurs. The aim of this review is to guide the rapidly developing field of behavioural ecotoxicology towards increased environmental realism, ecological complexity, and mechanistic understanding. We identify research areas in ecology that to date have been largely overlooked within behavioural ecotoxicology but which promise to yield valuable insights, including within- and among-individual variation, social networks and collective behaviour, and multi-stressor interactions. Further, we feature methodological and technological innovations that enable the collection of data on pollutant-induced behavioural changes at an unprecedented resolution and scale in the laboratory and the field. In an era of rapid environmental change, there is an urgent need to advance our understanding of the real-world impacts of chemical pollution on wildlife behaviour. This review therefore provides a roadmap of the major outstanding questions in behavioural ecotoxicology and highlights the need for increased cross-talk with other disciplines in order to find the answers.

Large-scale design and refinement of stable proteins using sequence-only models.

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we use a high-throughput, low-fidelity assay to experimentally evaluate the stability of approximately 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We build a neural network model that predicts protein stability given only sequences of amino acids, and compare its performance to the assayed values. We also report another network model that is able to generate the amino acid sequences of novel stable proteins given requested secondary sequences. Finally, we show that the predictive model-despite weaknesses including a noisy data set-can be used to substantially increase the stability of both expert-designed and model-generated proteins.

Impact of test chamber design on spontaneous behavioral responses of model crustacean zooplankton Artemia franciscana.

The use of small aquatic model organisms to investigate the behavioral effects of chemical exposure is becoming an integral component of aquatic ecotoxicology research and neuroactive drug discovery. Despite the increasing use of invertebrates for behavioral phenotyping in toxicological studies and chemical risk assessments, little is known regarding the potential for environmental factors-such as geometry, size, opacity and depth of test chambers-to modulate common behavioral responses. In this work, we demonstrate that test chamber geometry, size, opacity and depth can affect spontaneous, unstimulated behavioral responses of euryhaline crustacean Artemia franciscana first instar larval stages. We found that in the absence of any obvious directional cues, A. franciscana exhibited a strong innate wall preference behavior. Using different test chamber sizes and geometries, we found both increased wall preference and lowered overall distance traveled by the test shrimp in a smaller chamber with sharper-angled vertices. It was also determined through quantifiable changes in the chambers' color that the A. franciscana early larval stages can perceive, differentiate and react to differences in color or perhaps rather to light transmittance of the test chambers. The interaction between innate edge preference and positive phototaxis could be consistently altered with a novel photic stimulus system. We also observed a strong initial preference for depth in A. franciscana first instar larval stages, which diminished through the acclimatization. We postulate that the impact of test chamber designs on neurobehavioral baseline responses warrants further investigation, in particular considering the increased interest in behavioral eco-neurotoxicology applications.

Radiocaesium accumulation and fluctuating asymmetry in the Japanese mitten crab, Eriocheir japonica, along a gradient of radionuclide contamination at Fukushima.

The 2011 Tohoku earthquake-tsunami and the subsequent nuclear accident at the Fukushima Dai-ichi Nuclear Power Station (FDNPS) led to large-scale radionuclide contamination of the marine and freshwater environment. Monitoring studies of marine food products in the Fukushima region have generally demonstrated a declining trend in radiocaesium concentrations. However, the accumulation and elimination of radiocaesium and potential biological effects remain poorly understood for freshwater biota inhabiting highly contaminated areas at Fukushima. Consequently, the present study aimed to assess radiocaesium accumulation and developmental effects on the commercially important catadromous Japanese mitten crab, Eriocheir japonica. E. japonica were collected from four sites along a gradient of radionuclide contamination 4-44 km in distance from the FDNPS in 2017. To determine potential developmental effects, fluctuating asymmetry (FA) was used as a measure of developmental stability. Combined 134Cs and 137Cs values for whole E. japonica from highly contaminated sites 4 and 16 km in distance from the FDNPS were 3040 ± 521 and 2250 ± 908 Bq kg-1 wet weight respectively, 30 and 22 times greater than the Japanese standard limit of 100 Bq kg-1. Estimated total dose rates based on radiocaesium concentrations in whole crabs and sediment ranged from 0.016 to 37.7 µGy h-1. No significant relationship between radiocaesium accumulation and FA was recorded, suggesting that chronic radiation exposure at Fukushima is not inducing developmental effects in E. japonica as measured using fluctuating asymmetry. Furthermore, estimated dose rates were below proposed regulatory limits where significant deleterious effects are expected. The present study will aid in the understanding of the long-term consequences of radiation exposure for non-human biota and the management of radioactively contaminated environments.

Automation of Lung Ultrasound Interpretation via Deep Learning for the Classification of Normal versus Abnormal Lung Parenchyma: A Multicenter Study.

Lung ultrasound (LUS) is an accurate thoracic imaging technique distinguished by its handheld size, low-cost, and lack of radiation. User dependence and poor access to training have limited the impact and dissemination of LUS outside of acute care hospital environments. Automated interpretation of LUS using deep learning can overcome these barriers by increasing accuracy while allowing point-of-care use by non-experts. In this multicenter study, we seek to automate the clinically vital distinction between A line (normal parenchyma) and B line (abnormal parenchyma) on LUS by training a customized neural network using 272,891 labelled LUS images. After external validation on 23,393 frames, pragmatic clinical application at the clip level was performed on 1162 videos. The trained classifier demonstrated an area under the receiver operating curve (AUC) of 0.96 (±0.02) through 10-fold cross-validation on local frames and an AUC of 0.93 on the external validation dataset. Clip-level inference yielded sensitivities and specificities of 90% and 92% (local) and 83% and 82% (external), respectively, for detecting the B line pattern. This study demonstrates accurate deep-learning-enabled LUS interpretation between normal and abnormal lung parenchyma on ultrasound frames while rendering diagnostically important sensitivity and specificity at the video clip level.

Computational design of a synthetic PD-1 agonist.

Programmed cell death protein-1 (PD-1) expressed on activated T cells inhibits T cell function and proliferation to prevent an excessive immune response, and disease can result if this delicate balance is shifted in either direction. Tumor cells often take advantage of this pathway by overexpressing the PD-1 ligand PD-L1 to evade destruction by the immune system. Alternatively, if there is a decrease in function of the PD-1 pathway, unchecked activation of the immune system and autoimmunity can result. Using a combination of computation and experiment, we designed a hyperstable 40-residue miniprotein, PD-MP1, that specifically binds murine and human PD-1 at the PD-L1 interface with a Kd of ∼100 nM. The apo crystal structure shows that the binder folds as designed with a backbone RMSD of 1.3 Što the design model. Trimerization of PD-MP1 resulted in a PD-1 agonist that strongly inhibits murine T cell activation. This small, hyperstable PD-1 binding protein was computationally designed with an all-beta interface, and the trimeric agonist could contribute to treatments for autoimmune and inflammatory diseases.

The Role of Behavioral Ecotoxicology in Environmental Protection.

For decades, we have known that chemicals affect human and wildlife behavior. Moreover, due to recent technological and computational advances, scientists are now increasingly aware that a wide variety of contaminants and other environmental stressors adversely affect organismal behavior and subsequent ecological outcomes in terrestrial and aquatic ecosystems. There is also a groundswell of concern that regulatory ecotoxicology does not adequately consider behavior, primarily due to a lack of standardized toxicity methods. This has, in turn, led to the exclusion of many behavioral ecotoxicology studies from chemical risk assessments. To improve understanding of the challenges and opportunities for behavioral ecotoxicology within regulatory toxicology/risk assessment, a unique workshop with international representatives from the fields of behavioral ecology, ecotoxicology, regulatory (eco)toxicology, neurotoxicology, test standardization, and risk assessment resulted in the formation of consensus perspectives and recommendations, which promise to serve as a roadmap to advance interfaces among the basic and translational sciences, and regulatory practices.