An Update on Pediatric Environmental Health Specialty Units: Activities and Impacts, 2015-19.

OBJECTIVE: Pediatric Environmental Health Specialty Units (PEHSUs) address health concerns impacting children and their families related to environmental hazards by providing consultation and education to families, communities, and health care professionals. This analysis evaluated the productivity of the national PEHSU program. METHODS: PEHSUs reported data on services provided to US communities between October 1, 2014 and September 30, 2019. Descriptive statistics and qualitative analysis are presented. RESULTS: During this period, 6102 consultations and 4644 educational outreach activities were recorded. PEHSU faculty and staff published 462 articles, reviews, book chapters, fact sheets, commentaries, short informational pieces, and other materials between 2014 and 2019. These included 190 articles in scientific peer-reviewed journals and 29 textbook chapters to increase professional capacity in pediatric and reproductive environmental health. Lead, other metals, substances of abuse, pesticides, mold, and air pollution were frequently reported as agents of concern and educational topics. Requests for an overview of pediatric environmental health and outdoor pollutants were other frequently reported topics. CONCLUSIONS: PEHSUs work to decrease harmful exposures and improve children's health. They serve as expert resources for families, health care professionals, and communities on health effects related to environmental exposures. Data show the breadth and depth of concerns addressed and demonstrate the productivity and impact of this national program.

Addressing Emerging Needs Through the COVID-19 and Children With Medical Complexity ECHO.

The COVID-19 and Children with Medical Complexity (CMC) Extension for Community Healthcare Outcomes (ECHO) was developed as a "just in time" learning format to respond to the vast number of challenges faced by health care clinicians, public health professionals, and families/caregivers of CMC during the coronavirus disease 2019 (COVID-19) pandemic. A unique aspect of the ECHO was the meaningful integration of family leaders who participated as faculty, learners, case study presenters, and guest lecturers. Another distinguishing feature of this ECHO was its inclusion within a Collaborative Improvement and Innovation Network. A mixed methods analysis found that the COVID-19 and CMC ECHO was associated with significant gains in knowledge and confidence in caring for CMC and their families during the COVID-19 pandemic. This article provides an overview of the results of the COVID-19 and CMC ECHO and provides recommendations related to utilizing the ECHO model as a rapid response mechanism for systems improvement, clinical practice improvement, and education during a public health emergency.

Assessing the physiological effect of non-driving-related task performance and task modality in conditionally automated driving systems: A systematic review and meta-analysis.

In conditionally automated driving, the driver is free to disengage from controlling the vehicle, but they are expected to resume driving in response to certain situations or events that the system is not equipped to respond to. As the level of vehicle automation increases, drivers often engage in non-driving-related tasks (NDRTs), defined as any secondary task unrelated to the primary task of driving. This engagement can have a detrimental effect on the driver's situation awareness and attentional resources. NDRTs with resource demands that overlap with the driving task, such as visual or manual tasks, may be particularly deleterious. Therefore, monitoring the driver's state is an important safety feature for conditionally automated vehicles, and physiological measures constitute a promising means of doing this. The present systematic review and meta-analysis synthesises findings from 32 studies concerning the effect of NDRTs on drivers' physiological responses, in addition to the effect of NDRTs with a visual or a manual modality. Evidence was found that NDRT engagement led to higher physiological arousal, indicated by increased heart rate, electrodermal activity and a decrease in heart rate variability. There was mixed evidence for an effect of both visual and manual NDRT modalities on all physiological measures. Understanding the relationship between task performance and arousal during automated driving is of critical importance to the development of driver monitoring systems and improving the safety of this technology.

Assessing the physiological effect of non-driving-related task performance in conditionally automated driving systems: A systematic review and meta-analysis protocol.

Background: Level 3 automated driving systems involve the continuous performance of the driving task by artificial intelligence within set environmental conditions, such as a straight highway. The driver's role in Level 3 is to resume responsibility of the driving task in response to any departure from these conditions. As automation increases, a driver's attention may divert towards non-driving-related tasks (NDRTs), making transitions of control between the system and user more challenging. Safety features such as physiological monitoring thus become important with increasing vehicle automation. However, to date there has been no attempt to synthesise the evidence for the effect of NDRT engagement on drivers' physiological responses in Level 3 automation. Methods: A comprehensive search of the electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO, and IEEE Explore will be conducted. Empirical studies assessing the effect of NDRT engagement on at least one physiological parameter during Level 3 automation, in comparison with a control group or baseline condition will be included. Screening will take place in two stages, and the process will be outlined within a PRISMA flow diagram. Relevant physiological data will be extracted from studies and analysed using a series of meta-analyses by outcome. A risk of bias assessment will also be completed on the sample. Conclusion: This review will be the first to appraise the evidence for the physiological effect of NDRT engagement during Level 3 automation, and will have implications for future empirical research and the development of driver state monitoring systems.

C3I-SynFace: A synthetic head pose and facial depth dataset using seed virtual human models.

This article presents C3I-SynFace: a large-scale synthetic human face dataset with corresponding ground truth annotations of head pose and face depth generated using the iClone 7 Character Creator "Realistic Human 100" toolkit with variations in ethnicity, gender, race, age, and clothing. The data is generated from 15 female and 15 male synthetic 3D human models extracted from iClone software in FBX format. Five facial expressions - neutral, angry, sad, happy, and scared are added to the face models to add further variations. With the help of these models, an open-source data generation pipeline in Python is proposed to import these models into the 3D computer graphics tool Blender and render the facial images along with the ground truth annotations of head pose and face depth in raw format. The datasets contain more than 100k ground truth samples with their annotations. With the help of virtual human models, the proposed framework can generate extensive synthetic facial datasets (e.g., head pose or face depths datasets) with a high degree of control over facial and environmental variations such as pose, illumination, and background. Such large datasets can be used for the improved and targeted training of deep neural networks.

DroneAttention: Sparse weighted temporal attention for drone-camera based activity recognition.

Human activity recognition (HAR) using drone-mounted cameras has attracted considerable interest from the computer vision research community in recent years. A robust and efficient HAR system has a pivotal role in fields like video surveillance, crowd behavior analysis, sports analysis, and human-computer interaction. What makes it challenging are the complex poses, understanding different viewpoints, and the environmental scenarios where the action is taking place. To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Attention (SWTA) module to utilize sparsely sampled video frames for obtaining global weighted temporal attention. The proposed SWTA is comprised of two parts. First, temporal segment network that sparsely samples a given set of frames. Second, weighted temporal attention, which incorporates a fusion of attention maps derived from optical flow, with raw RGB images. This is followed by a basenet network, which comprises a convolutional neural network (CNN) module along with fully connected layers that provide us with activity recognition. The SWTA network can be used as a plug-in module to the existing deep CNN architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. It has been evaluated on three publicly available benchmark datasets, namely Okutama, MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets thereby surpassing the previous state-of-the-art performances by a margin of 25.26%, 18.56%, and 2.94%, respectively.

3D face-model reconstruction from a single image: A feature aggregation approach using hierarchical transformer with weak supervision.

Convolutional Neural Networks (CNN) have gained popularity as the de-facto model for any computer vision task. However, CNN have drawbacks, i.e. they fail to extract long-range perceptions in images. Due to their ability to capture long-range dependencies, transformer networks are adopted in computer vision applications, where they show state-of-the-art (SOTA) results in popular tasks like image classification, instance segmentation, and object detection. Although they gained ample attention, transformers have not been applied to 3D face reconstruction tasks. In this work, we propose a novel hierarchical transformer model, added to a feature pyramid aggregation structure, to extract the 3D face parameters from a single 2D image. More specifically, we use pre-trained Swin Transformer backbone networks in a hierarchical manner and add the feature fusion module to aggregate the features in multiple stages. We use a semi-supervised training approach and train our model in a supervised way with the 3DMM parameters from a publicly available dataset and unsupervised training with a differential renderer on other parameters like facial keypoints and facial features. We also train our network on a hybrid unsupervised loss and compare the results with other SOTA approaches. When evaluated across two public datasets on face reconstruction and dense 3D face alignment tasks, our method can achieve comparable results to the current SOTA performance and in some instances do better than the SOTA methods. A detailed subjective evaluation also shows that our method performs better than the previous works in realism and occlusion resistance.

An Innovative Approach to Increase Lead Testing by Pediatricians in Children, United States, 2019-2021.

Opportunities for lead exposure are common in the United States. The American Academy of Pediatrics, in collaboration with the Centers for Disease Control and Prevention, launched the Increasing Capacity for Blood Lead Testing Extension for Community Healthcare Outcomes (ECHO) project to educate pediatricians on the importance of testing children for lead exposure and to assess practice behavior change. We found that two weeks to one month after receiving training, more than 80% of participants reported increased lead testing and practice changes. Our results support use of the ECHO model as a mechanism for practice change. (Am J Public Health. 2022;112(S7):S647-S650. https://doi.org/10.2105/AJPH.2022.307084).

The Power of Connections: AAP COVID-19 ECHO Accelerates Responses During a Public Health Emergency.

CONTEXT: Pediatric providers across the United States have sought guidance on how to care for the nation's children during the uncertain historic times of the COVID-19 pandemic. The health care community has been challenged by the unprecedented burden of caring for patients when they have evolving guidelines and limited information about the effects of the virus on children. PROGRAM: In response, the American Academy of Pediatrics (AAP) rapidly launched a national initiative to increase child health professionals' knowledge, skills, and self-efficacy. This COVID-19 ECHO (Extension for Community Healthcare Outcomes) program created communities of learners among child health professionals and subject matter expert faculty using didactic and case-based presentations that foster an "all-teach, all-learn" approach. IMPLEMENTATION: The initial AAP COVID-19 ECHO program hosted more than 900 participants in 127 individual virtual sessions, with approximately 25 participants per session. The evolving nature of the pandemic necessitated dynamic and continuous bidirectional flow of concerns and information relevant to participants. Session topics were selected in a "just-in-time" fashion based on participant feedback from the prospective postsession surveys and faculty recommendations; speakers brought data and expert recommendations. EVALUATION: To assess impact, the AAP used a mixed-methods approach to evaluate the program's effectiveness in meeting its educational objectives. The 2-phase evaluation collected quantitative and qualitative data through an integrated feedback structure that utilized prospective postsession and retrospective postprogram surveys, along with postprogram focus groups. DISCUSSION: As the COVID-19 pandemic surges and another influenza season is upon us, the ECHO model is an effective strategy for facilitating bidirectional communication and education to build child health professionals' knowledge, skills, and self-efficacy during an unprecedented and ongoing public health emergency. KEY POINTS: The ECHO model is an effective strategy for health care organizations to facilitate bidirectional communication and education in building health professionals' clinical knowledge, skills, and self-efficacy during the unprecedented and ongoing public health emergency of the COVID-19 pandemic.

An efficient encoder-decoder model for portrait depth estimation from single images trained on pixel-accurate synthetic data.

Depth estimation from a single image frame is a fundamental challenge in computer vision, with many applications such as augmented reality, action recognition, image understanding, and autonomous driving. Large and diverse training sets are required for accurate depth estimation from a single image frame. Due to challenges in obtaining dense ground-truth depth, a new 3D pipeline of 100 synthetic virtual human models is presented to generate multiple 2D facial images and corresponding ground truth depth data, allowing complete control over image variations. To validate the synthetic facial depth data, we propose an evaluation of state-of-the-art depth estimation algorithms based on single image frames on the generated synthetic dataset. Furthermore, an improved encoder-decoder based neural network is presented. This network is computationally efficient and shows better performance than current state-of-the-art when tested and evaluated across 4 public datasets. Our training methodology relies on the use of synthetic data samples which provides a more reliable ground truth for depth estimation. Additionally, using a combination of appropriate loss functions leads to improved performance than the current state-of-the-art network performances. Our approach clearly outperforms competing methods across different test datasets, setting a new state-of-the-art for facial depth estimation from synthetic data.

Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets.

A data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network. Although of low complexity the resulting network achieves a high level of accuracy in iris region segmentation for challenging off-axis eye-patches. Interestingly, this network is also shown to achieve high levels of performance for regular, frontal, segmentation of iris regions, comparing favourably with state-of-the-art techniques of significantly higher complexity. Due to its lower complexity this network is well suited for deployment in embedded applications such as augmented and mixed reality headsets.

Development of Open-source Software and Gaze Data Repositories for Performance Evaluation of Eye Tracking Systems.

In this paper, a range of open-source tools, datasets, and software that have been developed for quantitative and in-depth evaluation of eye gaze data quality are presented. Eye tracking systems in contemporary vision research and applications face major challenges due to variable operating conditions such as user distance, head pose, and movements of the eye tracker platform. However, there is a lack of open-source tools and datasets that could be used for quantitatively evaluating an eye tracker's data quality, comparing performance of multiple trackers, or studying the impact of various operating conditions on a tracker's accuracy. To address these issues, an open-source code repository named GazeVisual-Lib is developed that contains a number of algorithms, visualizations, and software tools for detailed and quantitative analysis of an eye tracker's performance and data quality. In addition, a new labelled eye gaze dataset that is collected from multiple user platforms and operating conditions is presented in an open data repository for benchmark comparison of gaze data from different eye tracking systems. The paper presents the concept, development, and organization of these two repositories that are envisioned to improve the performance analysis and reliability of eye tracking systems.

Performance Evaluation Strategies for Eye Gaze Estimation Systems with Quantitative Metrics and Visualizations.

An eye tracker's accuracy and system behavior play critical roles in determining the reliability and usability of eye gaze data obtained from them. However, in contemporary eye gaze research, there exists a lot of ambiguity in the definitions of gaze estimation accuracy parameters and lack of well-defined methods for evaluating the performance of eye tracking systems. In this paper, a set of fully defined evaluation metrics are therefore developed and presented for complete performance characterization of generic commercial eye trackers, when they operate under varying conditions on desktop or mobile platforms. In addition, some useful visualization methods are implemented, which will help in studying the performance and data quality of eye trackers irrespective of their design principles and application areas. Also the concept of a graphical user interface software named GazeVisual v1.1 is proposed that would integrate all these methods and enable general users to effortlessly access the described metrics, generate visualizations and extract valuable information from their own gaze datasets. We intend to present these tools as open resources in future to the eye gaze research community for use and further advancement, as a contribution towards standardization of gaze research outputs and analysis.

An end to end Deep Neural Network for iris segmentation in unconstrained scenarios.

With the increasing imaging and processing capabilities of today's mobile devices, user authentication using iris biometrics has become feasible. However, as the acquisition conditions become more unconstrained and as image quality is typically lower than dedicated iris acquisition systems, the accurate segmentation of iris regions is crucial for these devices. In this work, an end to end Fully Convolutional Deep Neural Network (FCDNN) design is proposed to perform the iris segmentation task for lower-quality iris images. The network design process is explained in detail, and the resulting network is trained and tuned using several large public iris datasets. A set of methods to generate and augment suitable lower quality iris images from the high-quality public databases are provided. The network is trained on Near InfraRed (NIR) images initially and later tuned on additional datasets derived from visible images. Comprehensive inter-database comparisons are provided together with results from a selection of experiments detailing the effects of different tunings of the network. Finally, the proposed model is compared with SegNet-basic, and a near-optimal tuning of the network is compared to a selection of other state-of-art iris segmentation algorithms. The results show very promising performance from the optimized Deep Neural Networks design when compared with state-of-art techniques applied to the same lower quality datasets.

Dynamic Sensitivity of Corneal TRPM8 Receptors to Menthol Instillation in Dry Eye Versus Normal Subjects.

PURPOSE: To assess the sensitivity of corneal cold receptors to a known transient receptor potential melastatin 8 (TRPM8) agonist, menthol, in dry eye and normals, and to determine whether factors such as disease duration or age affect responses. METHODS: Dry eye disease (DED) (N = 33) and normal (N = 15) subjects were randomly assigned to receive Rohto® Hydra (0.01% menthol) or Systane® Ultra treatments (OU) in a prospective, double-blind, crossover study. DED subjects had documented disease and symptom response scores >2 on a 0- to 5-point scale. Normals had no history of DED and scores <2 on the same scale. Endpoints included mean cooling score (0 = not cool and 10 = very cool) evaluated at 0, 0.5, 1, 2, 3, and 4 min post-instillation, sum cooling scores (5 time points, range 0-60), and ocular signs and symptoms. RESULTS: Mean (±SD) ages were similar, 62.2 ± 8.6-year (DED) versus 53.5 ± 7.6-year (normal). Corneal sensitivity scores were not different between groups. Mean cooling scores at 0.5-4 min post-menthol instillation were significantly higher in DED subjects (P ≤ 0.03). Sum cooling scores were significantly higher (P = 0.04) in DED subjects with a disease duration <10 years (N = 18, 28.3 ± 2.58) versus ≥10 years (N = 15, 20.2 ± 2.76). Age did not affect cooling response in either group. CONCLUSION: DED subjects had greater sensitivity to cold than normal subjects. DED duration, and not age, was critical to cooling sensitivity. The finding that cooling scores were higher in subjects with DED for less than 10 years compared to more than 10 years suggests that corneal cold receptor sensitivity decreases as the duration of DED increases.

Variable Efficacy of Extended-release Mosquito Larvicides Observed in Catch Basins in the Northeast Chicago Metropolitan Area.

Since the mid-1990s, the North Shore Mosquito Abatement District (NSMAD) has applied extended-release formulations of mosquito larvicides to approximately 50,000 catch basins in the suburbs north of Chicago, IL, USA. This is performed as part of NSMAD's efforts to reduce local populations of the West Nile virus vector, Culex pipiens. Analyses from NSMAD's monitoring of larvicide-treated basins throughout the District over the 2014 and 2015 seasons suggest that larvicides intended to provide extended durations of control (30-180 days) failed to provide control for the maximum duration specified on the product label in approximately 25% of the District's basins. For larvicides designed to last up to 180 days (or about 26 weeks), failures were found at 1-15 weeks after treatment with most found at five weeks posttreatment. For larvicides formulated to last up to 30 days, failures were found at one to four weeks after applications with most found at three weeks posttreatment. The highest percentages of failing basins (ie, containing late-stage mosquito larvae or pupae during the specified product effectiveness period) were found in communities on the eastern side of the District, bordering Lake Michigan. As the larvicides appeared to function properly in the majority of monitored basins, it appears that the failures likely resulted from basin-specific physical factors (ie, basin volume, sediment content, and hydrology) that cause either product removal or a reduction in the concentration of the larvicide's active ingredient below the effective levels in these basins.

Small-Scale Trials Suggest Increasing Applications of Natular™ XRT and Natular™ T30 Larvicide Tablets May Not Improve Mosquito Reduction in Some Catch Basins.

Stormwater catch basins are commonly treated with larvicides by mosquito control agencies to reduce local populations of mosquito species capable of transmitting West Nile virus. Recent evidence suggests that extended-release larvicides formulated to last up to 180 days in catch basins may not be effective in some basins due to chronic flushing, rapid dissolution, or burying of treatment in sump debris. To investigate if increasing the number of applications could improve effectiveness, a small study was performed over 13 weeks in 2015 to evaluate two extended-release larvicides (Natular™ XRT 180-day tablets and Natular™ T30 30-day tablets) and a larvicide oil (CocoBear™). Over the course of 13 weeks, three groups of eight basins were monitored for mosquitoes, each group receiving Natular™ XRT, Natular™ T30, or CocoBear™ larvicides. All basins received a single application at the beginning of the study period. Once mosquitoes in a basin surpassed the treatment threshold during weekly monitoring, an additional application of the associated larvicide was given to that basin. The number of applications during the study period ranged from 1 to 10 for CocoBear™ basins, 1 to 7 for T30 basins, and 2 to 8 for XRT basins. Overall, the average number of applications and the cost of larvicide per basin were 4.4 applications at $0.66 per Coco-Bear™ basin, 4.4 applications at $6.26 per T30 basin, and 4 applications at $16.56 per XRT basin. Basins treated with XRT and T30 needed reapplications more often than expected, yet were no more effective than CocoBear™, suggesting that increasing the frequency of application of these larvicide formulations may not provide increased mosquito reduction in some basins.

Permissiveness of human hepatoma cell lines for HCV infection.

BACKGROUND: Although primary and established human hepatoma cell lines have been evaluated for hepatitis C virus (HCV) infection in vitro, thus far only Huh7 cells have been found to be highly permissive for infectious HCV. Since our understanding of the HCV lifecycle would benefit from the identification of additional permissive cell lines, we assembled a panel of hepatic and non-hepatic cell lines and assessed their ability to support HCV infection. Here we show infection of the human hepatoma cell lines PLC/PRF/5 and Hep3B with cell culture-derived HCV (HCVcc), albeit to lower levels than that achieved in Huh7 cells. To better understand the reduced permissiveness of PLC and Hep3B cells for HCVcc infection, we performed studies to evaluate the ability of each cell line to support specific steps of the viral lifecycle (i.e. entry, replication, egress and spread). RESULTS: We found that while the early events in HCV infection (i.e. entry plus replication initiation) are cumulatively equivalent or only marginally reduced in PLC and Hep3B cells, later steps of the viral life cycle such as steady-state replication, de novo virus production and/or spread are impaired to different degrees in PLC and Hep3B cultures compared to Huh7 cell cultures. Interestingly, we also observed that interferon stimulated gene (i.e. ISG56) expression was significantly and differentially up-regulated in PLC and Hep3B cells following viral infection. CONCLUSIONS: We conclude that the restrictions observed later during HCV infection in these cell lines could in part be attributed to HCV-induced innate signaling. Nevertheless, the identification of two new cell lines capable of supporting authentic HCVcc infection, even at reduced levels, expands the current repertoire of cell lines amendable for the study of HCV in vitro and should aid in further elucidating HCV biology and the cellular determinants that modulate HCV infection.

The rate of hepatitis C virus infection initiation in vitro is directly related to particle density.

To gain a more complete understanding of hepatitis C virus (HCV) entry, we initially assessed the rate at which HCV initiates productive attachment/infection in vitro and discovered it to be slower than most viruses. Since HCV, including cell culture-derived HCV (HCVcc), exhibits a broad-density profile (1.01-1.16 g/ml), we hypothesized that the varying densities of the HCVcc particles present in the inoculum may be responsible for this prolonged entry phenotype. To test this hypothesis, we show that during infection, particles of high density disappeared from the viral inoculum sooner and initiated productive infection faster than virions of low density. Moreover, we could alter the rate of attachment/infection initiation by increasing or decreasing the density of the cell culture medium. Together, these findings demonstrate that the relationship between the density of HCVcc and the density of the extracellular milieu can significantly impact the rate at which HCVcc productively interacts with target cells in vitro.