Tracking Tool Research Articles

Learning to SuRRF: Sustained Engagement and Increased Academic Productivity Over Two Years of a Resident-Led Virtual Laboratory

To determine if implementation of a resident-led virtual laboratory can sustain increased engagement and academic productivity in residents and faculty. We developed and introduced a multimodal virtual Surgery Resident Research Forum (SuRRF) in July 2019. SuRRF utilizes monthly virtual lab meetings, weekly newsletters, a centralized database of projects, project tracking tools, and a shared calendar of deadlines to facilitate research among surgical residents. Data on number of participating residents, faculty, and projects across SuRRF meetings at 1-year (7/2020) and 2-years post-implementation (9/2021) were collected to evaluate engagement. Institutional ACGME Resident Scholarly Activity and Faculty Scholarly Activity reports were evaluated for the pre-SuRRF implementation (2018-2019) and post-implementation (2020-2021) academic years to assess productivity pre- and post-implementation. Three tertiary academic hospitals of a single health system in New York. All residents in our general surgery program during the study period, including research residents, were eligible to participate in our study. At 1-year, there were 2 attendings, 13 residents, and 23 projects, compared to 12 attendings, 25 residents, and 42 projects at 2-years post-implementation. Post-SuRRF implementation, residents had significantly more publications (0.56 ± 0.15 vs. 1.10 ± 0.15, p = 0.005), textbook chapters (0.00 vs. 0.010 ± 0.044, p = 0.014), research participation (p < 0.01), and scholarly activity (p = 0.02). Post-SuRRF, faculty had significantly more publications (0.74 ± 0.15 vs. 2.20 ± 0.33, p < 0.001) and scholarly activity (p = 0.006). SuRRF promotes exposure to projects and resources and increases collaboration and peer-to-peer mentorship. Our experience with SuRRF suggests that resident-led virtual laboratories may increase peer-reviewed publications and improve resident and faculty engagement in scholarly activity, thus supporting academic growth.

The Influence of the Structure on the Attention of Organization Managers

We aim to understand the aspects of the structural distribution of attention that influence the attention of individuals in organizations. We performed two distinct and interlinked studies considered experiments using an eye tracking tool. In the first study, we evaluated the effect of training (as a proxy for the function) on managerial care and the individual characteristics of future managers (n=151). In the second study, we evaluated the effect of function/position on managerial care and the individual characteristics of professionals (n=140) working in organizations. We conclude that the professional profile and the organizational environment (internship) influence the choice of information in the areas of interest (AOI) of future managers. About managers, we concluded that the professional profile influences the choice of information (AOI) for the allocation of care. The initial training of decision-makers (managers) becomes less relevant than their professional profile, which is shaped based on their professional experiences and practices.

Assessing the Performance and Application of Operational Lagrangian Transport HAB Forecasting Systems

Availability of operational regional hydrodynamic models and near real time Harmful Algal Bloom (HAB) alerts from monitoring stations and remote sensing products have allowed the proliferation of short term advective HAB forecasts. However, their predictive ability in simulating HAB transport needs to be continuously evaluated in events of different HAB species to assess their applicability to different domains and the impacts of the choices made in model setup. Here we review the performance of three different modelling systems which were part of the PRIMROSE project against historical bloom events in different regions in the European Atlantic Area. The objectives are to understand their predictive ability and to demonstrate some aspects of Lagrangian model setup that are relevant to HAB early warning systems; in particular the use of advection-diffusion only models (without a biological component) and the effects of model configuration, especially model resolution. Hindcast and forecast simulations have been run in examples of high biomass blooms detected in satellite imagery; in the western English Channel, several events of potentially toxic species like Karenia mikimotoi and Prorocentrum cordatum (minimum) were simulated and in Western France a bloom of Mesodinium rubrum, prey of the toxic Dinophysis spp. Additionally, some simulations for studying the evolution of low biomass Dinophysis spp. blooms in Galicia-North Portugal were undertaken with models of different setup. Several metrics have been used to quantify the model performance and to compare the results of the different model configurations, showing that differences in hydrodynamical model configuration (initiation, resolution, forcing, and simulation domain) result in differences in the predicted transport of HABs. We find that advection only is a reasonable approximation but that it may do worse in an early (onset) phase than later on, and we find transport is generally increases with increasing resolution. Our results confirm that Lagrangian particle tracking tools can be integrated operationally in HAB early warning systems providing useful information on potential HAB evolution to users.

Adaptations to relational facilitation for two national care coordination programs during COVID-19.

Adaptations to implementation strategies are often necessary to support adoption and scale-up of evidence-based practices. Tracking adaptations to implementation strategies is critical for understanding any impacts on outcomes. However, these adaptations are infrequently collected. In this article we present a case study of how we used a new method during COVID-19 to systematically track and report adaptations to relational facilitation, a novel implementation strategy grounded in relational coordination theory. Relational facilitation aims to assess and improve communication and relationships in teams and is being implemented to support adoption of two Quadruple Aim Quality Enhancement Research Initiative (QA QUERI) initiatives: Care Coordination and Integrated Case Management (CC&ICM) and the Transitions Nurse Program for Home Health Care (TNP-HHC) in the Veterans Health Administration (VA). During 2021-2022, relational facilitation training, activities and support were designed as in-person and/or virtual sessions. These included a site group coaching session to create a social network map of care coordination roles and assessment of baseline relationships and communication between roles. Following this we administered the Relational Coordination Survey to assess the relational coordination strength within and between roles. COVID-19 caused challenges implementing relational facilitation, warranting adaptations. We tracked relational facilitation adaptations using a logic model, REDCap tracking tool based on the Framework for Reporting Adaptations and Modifications-Enhanced (FRAME) with expanded Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) dimensions, and member checking. Adaptations were analyzed descriptively and for themes using matrix content analysis. COVID-19's impact within the VA caused barriers for implementing relational facilitation, warranting eight unique adaptations to the implementation strategy. Most adaptations pertained to changing the format of relational facilitation activities (n = 6; 75%), were based on external factors (n = 8; 100%), were planned (n = 8; 100%) and initiated by the QA QUERI implementation team (n = 8; 100%). Most adaptations impacted adoption (n = 6; 75%) and some impacted implementation (n = 2; 25%) of the CC&ICM and TNP-HHC interventions. Systematically tracking and discussing adaptations to relational facilitation during the COVID-19 pandemic enhanced engagement and adoption of two VA care coordination interventions. The impact of these rapid, early course adaptations will be followed in subsequent years of CC&ICM and TNP-HHC implementation.

Role of pepper mild mottle virus as a tracking tool for fecal pollution in aquatic environments.

The plant pathogen pepper mild mottle virus (PMMoV) has recently been proposed as a water quality indicator, it is a RNA virus belonging to the genus Tobamovirus in the family Virgoviridae that causes harm to the pepper crops. After consuming processed food products containing infected peppers, such as hot sauces, PMMoV is excreted in high concentrations in feces; therefore, this is the most common RNA virus, constantly found in the feces of humans. The fecal–oral pathway is emerging as an environmental problem. The presence of high concentrations of pathogens associated with human excreta in environmental waters or water reuse supplies poses a threat to public health. Due to the difficulty in determining the presence of pathogens effectively in water, attempts to monitor microbial water quality often use surrogates or indicator organisms that can be easily detected; therefore, PMMoV is used as a viral surrogate in aquatic environment. This paper describes the incidence and persistence of PMMoV in aquatic environments and in waste treatment plants and its usefulness for quantifying virus reductions by advanced water treatment technologies. In recent research, SARS-CoV-2 was reported to be found in wastewater and utilized for the purpose of monitoring coronavirus illness outbreaks. Since PMMoV is readily identified in the human feces and can also serve as an indicator of human waste, the determined PMMoV concentrations may be utilized to give the normalized report of the SARS-CoV-2 concentration, so that, the amount of human waste found in the wastewater can be taken into consideration.

Clinical applications of augmented reality in orthopaedic surgery: a comprehensive narrative review.

The development of augmented reality (AR) technology allows orthopaedic surgeons to incorporate and visualize surgical data, assisting the execution of both routine and complex surgical operations. Uniquely, AR technology allows a surgeon to view the surgical field and superimpose peri-operative imaging, anatomical landmarks, navigation guidance, and more, all in one view without the need for conjugate gaze between multiple screens. The aim of this literature review was to introduce the fundamental requirements for an augmented reality system and to assess the current applications, outcomes, and potential limitations to this technology. A literature search was performed using MEDLINE and Embase databases, by two independent reviewers, who then collaboratively synthesized and collated the results of the literature search into a narrative review focused on the applications of augmented reality in major orthopaedic sub-specialties. Current technology requires that pre-operative patient data be acquired, and AR-compatible models constructed. Intra-operatively, to produce manipulatable virtual images into the user's view in real time, four major components are required including a camera, computer image processing technology, tracking tools, and an output screen. The user is provided with a heads-up display, which is a transparent display, enabling the user to look at both their natural view and the computer-generated images. Currently, high-quality evidence for clinical implementation of AR technology in the orthopaedic surgery operating room is lacking; however, growing in vitro literature highlights a multitude of potential applications, including increasing operative accuracy, improved biomechanical angular and alignment parameters, and potentially reduced operative time. While the application of AR systems in surgery is currently in its infancy, we anticipate rapid and widespread implementation of this technology in various orthopaedic sub-specialties.

Genomic surveillance of SARS-CoV-2 in US military compounds in Afghanistan reveals multiple introductions and outbreaks of Alpha and Delta variants

BackgroundWith the emergence and spread of SARS-CoV-2 variants, genomic epidemiology and surveillance have proven invaluable tools for variant tracking. Here, we analyzed SARS-CoV-2 samples collected from personnel located at the US/NATO bases across Afghanistan.ResultsSequencing and phylogenetic analyses revealed at least 16 independent introductions of SARS-CoV-2 into four of these relatively isolated compounds during April and May 2021, including multiple introductions of Alpha and Delta variants. Four of the introductions resulted in sustained spread of the virus within, and in two cases between, the compounds. Three of these outbreaks, one Delta and two Alpha, occurred simultaneously.ConclusionsEven in rigorously controlled and segregated environments, SARS-CoV-2 introduction and spread may occur frequently.

Boosting students' creativity through visual bricolage of experiences - how visual collage boards help students in expressing their knowledge during online classes

The objective of this study is to present various practical applications of collage-based methods (visual bricolage) as a way of boosting creative discussions with students and helping them to express their knowledge and experience using visual representation. This viewpoint paper is based on the experience of twenty classes conducted with the MA program and postgraduate students at Kozminski University between March 2019 and December 2021, when online whiteboards and visual metaphors were used. The method used was participative observation, as the author also acted as the workshop facilitator. To observe the user’s action, the contribution tracking tool that is available on the mural whiteboard (Mural, n.d.) was activated. The explorations during the workshops revealed two elements of online classes: Conceptualization vs Illustration, to determine whether photos and pictures serve as an instrumental illustration in the background of the story, or they are important elements that convey the sense of the students’ statements. Moreover, the paper contains various practical findings that help to facilitate creative educational workshops remotely.

Bireysel Dijital İmajın Gözetimi: Instagram Stalk Pratiği Üzerine Ampirik Bir Araştırma

With new media technologies, surveillance in human history has changed the dimension of social media, systematic surveillance has become globalized, and transformed into a superpanopticon structure that includes volunteering. Surveillance is now stripped of a government-associated closed circuit panopticon structure, and has gained a characteristic action for individuals to individuals, individuals to society, and individuals to society. In order to explain the "new" state of surveillance by linking it with the new media structure and to emphasize that its existing scope has expanded, a review has been made in the context of stalk / follow-up as a social media concept. In this study, Instagram, which is a social media channel, is examined especially as a stalk / tracking and surveillance tool. Instagram is one of the channels that individuals use extensively in digital identity and image building. In this context; Instagram is seen to have different patterns of use with respect to surveillance than other social media channels, however, a massively interactive surveillance practice has occurred.

Investigating visual expertise in sculpture: A methodological approach using eye tracking.

Research on visual expertise has progressed significantly due to the availability of eye tracking tools. However, attempts to bring together research on expertise and eye tracking methodology provoke several challenges, because visual information processes should be studied in authentic and domain-specific environments. Among the barriers to designing appropriate research are the proper definition of levels of expertise, the tension between internal (experimental control) and external (authentic environments) validity, and the appropriate methodology to study eye movements in a three-dimensional environment. This exploratory study aims to address these challenges and to provide an adequate research setting by investigating visual expertise in sculpting. Eye movements and gaze patterns of 20 participants were investigated while looking at two sculptures in a museum. The participants were assigned to four different groups based on their level of expertise (laypersons, novices, semi-experts, experts). Using mobile eye tracking, the following parameters were measured: number of fixations, duration of fixation, dwell time in relevant areas, and revisits in relevant areas. Moreover, scan paths were analysed using the eyenalysis approach. Conclusions are drawn on both the nature of visual expertise in sculpting and the potential (and limitations) of empirical designs that aim to investigate expertise in authentic environments.

Deep Transfer Learning-Based Fall Detection Approach Using IoMT-Enabled Thermal Imaging-Assisted Pervasive Surveillance and Big Health Data

People’s need for healthcare capacity has become increasingly critical as the elderly population continues to grow in most communities. Approximately 25–47% of seniors fall annually, and early detection of poor balance can significantly reduce their risk. Automated fall detection with big data analytics is key to maintaining the safety of the elderly in smart cities. Visible image systems (VIS) in smart buildings, on the other hand, visible image systems (VIS) in smart buildings may compromise the privacy of seniors by enabling technologies for intelligent big data analytics (IBDA). Thermal imaging (TI) is less obtrusive than visual imaging and can be used in combination with machine vision to perform a wide range of IBDAs. In this study, we present a novel two-step method for detecting falls in TI frames using deep learning (DL). As the first step, tracking tools are used to locate people’s locations. A novel modified deep transfer learning (TL) technique is used to classify the trajectory created by the tracking approach for people who are at risk of falling. Fall detection by the IBDA will be connected to the Internet of medical things (IoMT) and used as smart technology in the process of big data-assisted pervasive surveillance and health analytics. According to an analysis of the publicly available thermal fall dataset, our method outperforms traditional fall detection methods, with an average error of less than 3%. Additionally, IoMT platforms facilitate data processing, real-time monitoring and healthcare management. Our smart scheme for using big data analytics to enable intelligent decisions is compatible with the various spaces and provides a comfortable and safe environment for current and future elderly people.

Fertility tracking tool for visually impaired and non-visually impaired women: A qualitative study.

A natural family planning device, Fertility Tracking Tool (FTT), was developed to serve as a visual and tactile guide in monitoring the fertile and infertile days of the menstrual cycle. However, there is lack of preferences about the tool among visually impaired and non-visually impaired women. Therefore, understanding their viewpoints will provide additional input for the final design of the product. This study aimed to explore the opinions of potential users of FTT in terms of acceptability, price sensitivity, buying intention, product placement, branding, and packaging. The study employed a qualitative research design with data gathered from focus group discussions. Eleven FGD sessions were conducted participated by one group of visually impaired and ten groups of non-visually impaired women from Iligan City, Philippines, selected through purposive sampling technique. Semi-structured FGD guide was utilized, and the sessions were audio-recorded. The data were analyzed through thematic analysis. The themes that emerged in the study were "adaptable" with three sub-themes (easy to use, safe to use, and aesthetic), "cost-effective", "marketable", "accessibility convenience", "remarkable brand", and "complementary packaging". The study shows that the FTT is acceptable to both visually impaired and non-visually impaired women because it is user-friendly, has no side effects, and has a unique design. The accounts implying less price sensitivity, buying intention, accessibility preferences, notable branding, and auxiliary packaging are indications of the business potential of FTT. This study made a novel contribution to the nursing practice as the FTT is a newly invented device for natural family planning. The strong support from the government is necessary for FTT commercialization or even free distribution to the qualified users, not only in the research locale but extending the nationwide. The extensive FTT use may increase the natural contraceptive utilization in the Philippines, resulting in improved women's health and population control. The FTT utilization can also be adapted across the globe to result in a better impact.

PCR39 Patient and Caregiver Diversity in FDA Patient Engagement Activities and CBER-Regulated Therapeutics: A Review of the Literature

This review of literature is aim one of a multipronged pilot project to understand what is known about current research and efforts related to increasing diverse patient engagement in drug development and factors that contribute to variances in knowledge of- and participation in- patient engagement activities relevant to U.S. Food and Drug Administration (FDA) Center for Biologics Evaluation and Research (CBER) regulated products for food allergies. A two-fold approach was used to conduct this review: (1) a scoping systematic literature, and (2) a review of grey literature. Research abstracts were screened and reviewed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for articles published between years 2012-2021. All abstracts were screened by two trained researchers and reviewed by a third using predefined inclusion criteria. Grey literature was information identified from food allergy organization websites and screened for inclusion by two trained researchers following prespecified criteria. A Microsoft excel® tracking tool was built to organize extracted data. Finally, a framework developed by the National Health Council (NHC) was adapted and used to synthesize the extracted data for both approaches. Seven out of 1,370 research abstracts were included for full text data analysis. In most (86%), patients and caregivers were solely study participants, compared to one study (14%) in which they were actively involved in informing the research process. Sixteen organization websites were identified for screening—seven of which were included for analysis. 71.4% (5/7 organizations) reported engagement opportunities for patients/caregivers but did not explicitly share how diverse populations were encouraged to engage. The importance of diverse patient engagement in food allergy therapeutic development has been stressed by both patient organizations and in research. However, further exploration is needed in how to meaningfully engage diverse groups of patients and caregivers impacted by food allergies to help inform regulatory decision-making

Artificial Intelligence Based on Identifying Face Mask Wearing Detection Using Recurrent Neural Network

COVID-19 has established a brand new frequency, and people have started realizing that they are entering a new world. The society is currently undergoing rapid change and needs to respond rapidly to fresh norms which surround us all. Creating a risk-free environment is a priority for everyone as life has not been as conductive as before brand new plans are methodized daily to adapt to policies and controls. The facial mask detection platform uses artificial intelligence see if the user is wearing a mask. App users can also add faces and phone numbers to warn themselves if they are not wearing a mask. A notice is issued to the administrator if the face captured by the camera is not recognized. Several COVID tracking tools are acceptable and safe in many aspects of the society. The most important tool is mask inspection using the proposed method, the artificial intelligence-based recurrent neural network (RNN) algorithm is enabled to capture the input image from the image classification in the dataset. However, in the overlapping scene, a different size and localization face mask detector provide a plurality of face images of the detected face. Detected faces extracted from this are grouped based on the human face detection. The recurrent neural network (RNN) algorithm using classifier and detection the mask or unmasked faces classifier. If the result is decoded from the stage, the final output is detected using all face images in the image correctly and then a message is sent to the surface and e-mail send that is not either of the mask or unmask.

Implementation of Standardized Catheter Cleansing Cloths at an Inpatient Rehab Facility

<h3>Background</h3> A higher than expected number of catheter-associated urinary tract infections (CAUTIs) was occurring at an inpatient rehab facility (IRF). Patients at IRFs often have indwelling urinary catheters (IUCs) for medical conditions like neurogenic bladder or urinary retention conditions. It is essential to provide consistent care and maintenance of IUCs for patients with long-term or chronic urinary catheters to prevent infection. Variability exists in the frequency, procedure, and availability of products for catheter care. <h3>Methods</h3> The quality improvement project, using lean methodology, took place over a six-month period at a 38 bed IRF to include patients with an IUC. The catheter cleansing cloths were used daily and as needed on patients with an IUC. The improvement project assessed if the daily use of the catheter cleansing cloths reduced the incidence of CAUTI at an IRF. A daily tracking tool was developed to monitor catheter device utilization and record daily documentation of urinary catheter care with the designated cleansing cloths. <h3>Results</h3> In the 12 months prior to the implementation of the catheter cleansing cloths, the IRF had seven CAUTIs. In the six months since the start of using the cloths, the IRF had zero CAUTIs. The Standardized Utilization Ratio (SUR) for the IRF in the six months prior to implementing daily device monitoring was 1.458. In the first six months of daily device monitoring, the SUR was 1.014. <h3>Conclusions</h3> The quality improvement project revealed a standardized process for daily catheter care is associated with a reduction in CAUTI. This project also notes an association between the daily monitoring of IUC devices and a reduction in catheter device utilization. Using catheter cleansing cloths for daily catheter care may be a great resource to provide consistent and predicable care for patients with IUCs.

Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation.

BackgroundRecent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection, given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics and patterns of recovery could bring insights and lead to more timely treatment or treatment adjustment, as well as better resource management in health care systems.ObjectiveThe primary objective of this study is to explore the potential of longitudinal audio samples over time for COVID-19 progression prediction and, especially, recovery trend prediction using sequential deep learning techniques.MethodsCrowdsourced respiratory audio data, including breathing, cough, and voice samples, from 212 individuals over 5-385 days were analyzed, alongside their self-reported COVID-19 test results. We developed and validated a deep learning–enabled tracking tool using gated recurrent units (GRUs) to detect COVID-19 progression by exploring the audio dynamics of the individuals’ historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19 detection in terms of positive and negative (healthy) tests using sequential audio signals, which was primarily assessed in terms of the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with 95% CIs, and (2) longitudinal disease progression prediction over time in terms of probability of positive tests, which was evaluated using the correlation between the predicted probability trajectory and self-reported labels.ResultsWe first explored the benefits of capturing longitudinal dynamics of audio biomarkers for COVID-19 detection. The strong performance, yielding an AUROC of 0.79, a sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, which displayed high consistency with longitudinal test results with a correlation of 0.75 in the test cohort and 0.86 in a subset of the test cohort with 12 (57.1%) of 21 COVID-19–positive participants who reported disease recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals’ disease progression and recovery.ConclusionsAn audio-based COVID-19 progression monitoring system was developed using deep learning techniques, with strong performance showing high consistency between the predicted trajectory and the test results over time, especially for recovery trend predictions. This has good potential in the postpeak and postpandemic era that can help guide medical treatment and optimize hospital resource allocations. The changes in longitudinal audio samples, referred to as audio dynamics, are associated with COVID-19 progression; thus, modeling the audio dynamics can potentially capture the underlying disease progression process and further aid COVID-19 progression prediction. This framework provides a flexible, affordable, and timely tool for COVID-19 tracking, and more importantly, it also provides a proof of concept of how telemonitoring could be applicable to respiratory diseases monitoring, in general.

Development and preliminary validity of an Indonesian mobile application for a balanced and sustainable diet for obesity management

BackgroundMobile applications such as personalized tracking tools and food choice aids may enhance weight loss programs. We developed and assessed client preferences for the content, user interface, graphics, and logic flow of a mobile application, and evaluated its validity for tracking compliance with weight control and making healthy and sustainable food choices.MethodsOur four-stage study comprised formative research, application development, acceptance assessment, and validity. The formative research included literature reviews and six focus groups with 39 respondents aged 19–64 years at high risk for obesity. The development stage included programmer selection, defining application specifications, design, and user interface. Prototype acceptability was assessed with 53 respondents who graded 17 features of content, graphic design, and application flow (ranked as good, moderate, and poor). A feature was considered to have "good" acceptance if its mean response was higher than the mean of overall responses. The validity was assessed in 30 obese women using Bland–Altman plots to compare results from dietary intake assessment from the application to conventional paper-based methods.ResultsThe application was named as EatsUp®. The focus group participants defined the key requirements of this app as being informative, easy, and exciting to use. The EatsUp® core features consisted of simple menu recommendations, health news, notifications, a food database, estimated portion sizes, and food pictures. The prototype had a "good" overall acceptance regarding content, graphics, and flow. Fourteen out of 17 parameters were graded as "good" from > 70% of respondents. There was no significant difference between the rated proportions for content, graphics, and app flow (Kolmogorov–Smirnov Z-test, p > .05). The agreement using the Bland–Altman plots between EatsUp® and the paper-based method of measuring food intake was good, with a mean difference of energy intake of only 2.63 ± 28.4 kcal/day (p > 0.05), well within the 95% confidence interval for agreement.ConclusionsThe EatsUp® mobile application had good acceptance for graphics and app flow. This application can support the monitoring of balanced and sustainable dietary practice by providing nutritional data, and is comparable with conventional dietary assessment tools, and performed well in tracking energy, macronutrient, and selected micronutrients intakes.Trial registrationNCT03469869. The registration date was March 19, 2018.

Applying finite-time lyapunov exponent to study the tidal dispersion on oil spill trajectory in Burrard Inlet

This study used a combination of Finite-Time Lyapunov Exponent (FTLE) values, residual currents, and tidal excursion lengths to systematically investigate the effects of tidal dispersion on oil spill trajectories in Burrard Inlet, BC, Canada, which is a tidally dominated estuary. The FTLE analysis results showed that tidal type and tidal phase significantly influenced the FTLE fields because the flow structure and the location of saddle points varied as a function of the tidal type and tidal phase. Some transport barriers formed in the Inner Harbour, which blocked the water exchange between the western and eastern parts of the inlet. Moreover, tidal mixing in the wider regions of Burrard Inlet (i.e., the western Outer Harbour) was relatively weak than in the narrower areas (i.e., First Narrows and Second Narrows). The observations from the FTLE analysis agreed well with the residual currents and tidal excursion results. The drifter trajectories were consistent with the Lagrangian coherent structure lines extracted from the FTLE analysis. To verify the tidal dispersion impact on an oil spill trajectory in the inlet, the FTLE fields were compared with a real oil spill that occurred in Burrard Inlet in 2015 (the M/V Marathassa oil spill). The FTLE fields reasonably explained the spilled oil’s trajectories from the real event. In addition, a set of stochastic oil spill models were run in this study and found that the FTLE analysis was a reliable tool for oil spill tracking. Overall, the FTLE method would be a valuable addition to practical oil spill response planning.

Review of Method and a New Tool for Decline and Inactive SARS-CoV-2 in Wastewater Treatment

Following the recent outbreak of the COVID-19 pandemic caused by the SARS-CoV-2 virus, monitoring sewage has become crucial, according to reports that the virus was detected in sewage. Currently, various methods are discussed for understanding the SARS-CoV-2 using wastewater surveillance. This paper first introduces the fundamental knowledge of primary, secondary, and tertiary water treatment on SARS-CoV-2. Next, a thorough overview is presented to summarize the recent developments and breakthroughs in removing SARS-CoV-2 using solar water disinfection (SODIS) and UV (UVA (315–400 nm), UVB (280-315 nm), and UVC (100–280 nm)) process. In addition, Due to the fact that the distilled water can be exposed to sunlight if there is no heating source, it can be disinfected using solar water disinfection (SODIS). SODIS, on the other hand, is a well-known method of reducing pathogens in contaminated water; moreover, UVC can inactivate SARS-CoV-2 when the wavelength is between 100 to 280 nanometers. High temperatures (more than 56°C) and UVC are essential for eliminating SARS-CoV-2; however, the SODIS systems use UVA and work at lower temperatures (less than45°C). Therefore, using SODIS methods for wastewater treatment (or providing drinking water) is not appropriate during a situation like the ongoing pandemic. Finally, a wastewater-based epidemiology (WBE) tracking tool for SARS-CoV-2 can be used to detect its presence in wastewater.

Characterizing the chemical and microbial fingerprint of unsheltered homelessness in an urban watershed

Unsheltered homelessness is rapidly becoming a critical issue in many cities worldwide. The worsening situation not only highlights the socioeconomic plight, but it also raises awareness of ancillary issues such as the potential implications for urban water quality. The objective of this study was to simultaneously leverage diverse source tracking tools to develop a chemical and microbial fingerprint describing the relative contribution of direct human inputs into Las Vegas' tributary washes. By evaluating a wide range of urban water matrices using general water quality parameters, fecal indicator bacteria (FIB), human-associated microbial markers [e.g., HF183, crAssphage, and pepper mild mottle virus (PMMoV)], 16S rRNA gene sequencing data, and concentrations of 52 anthropogenic trace organic compounds (TOrCs), this study was able to differentiate principal sources of these constituents, including contributions from unsheltered homelessness. For example, HF183 (31% vs. 0%), crAssphage (61% vs. 5%), and PMMoV (72% vs. 55%) were more frequently detected in tributary washes with higher homeless census counts vs. ‘control’ tributary washes. Illicit drugs or their metabolites (e.g., heroin, acetylmorphine, amphetamine, and cocaine) and select TOrCs (e.g., acetaminophen, caffeine, ibuprofen, and naproxen) were also detected more frequently and at higher concentrations in the more anthropogenically-impacted washes. These data can be used to raise awareness of the shared interests between the broader community and those who are experiencing homelessness, notably the importance of protecting environmental health and water quality. Ultimately, this may lead to more rapid adoption of proven strategies for achieving functional zero homelessness, or at least additional resources for unsheltered individuals.