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OBJECTIVES/GOALS: Our goal is to determine whether microplastic fibers (MPFs) provide signals for dendritic cell-induced Th2 polarization via epithelial-cell-derived thymic stromal lymphopoietin (TSLP). We seek to highlight a potential mechanism for MPF-induced airway toxicity associated with asthma exacerbation. METHODS/STUDY POPULATION: Primary human bronchial epithelial cells (NHBEs) were grown and differentiated at an air-liquid interface. Dyed and undyed polyester MPFs (14x45 µm) generated using a cryomicrotome were delivered to NHBEs through a custom designed mesh-hopper system. After the exposure period (6, 12, 24 hrs), cell viability was assessed using alamarBlue, and RT-qPCR was performed to determine mRNA expression of asthma associated genes (i.e., TSLP, IL-13, IL-33, etc.,) in NHBEs. Bulk mRNA-sequencing followed by bioinformatics will be performed to observe other plausible pathways tweaked by lung cell exposure to MPFs. RESULTS/ANTICIPATED RESULTS: Through gravimetric analysis, it was determined that the mesh-hopper system can achieve delivery efficiencies of at least 85% for as low as 500 fibers. Following exposure, results show polyester MPFs (500 - 1,000 fibers) exposed to NHBEs at multiple time points (6, 12, 24 hrs) did not result in a statistically significant decrease in cell viability. Treatment with 500 undyed MPFs resulted in a slight increase in TSLP expression at 6 hrs that decreased over time, whereas all other treatment groups resulted in TSLP downregulation. Similarly, 500 undyed MPFs resulted in an increase in IL-13 expression at both 6 and 12 hrs with all other treatment groups leading to IL-13 downregulation. We anticipate the RNA-seq results will show pro-inflammatory pathways are highly targeted following NHBE exposure to MPFs. DISCUSSION/SIGNIFICANCE: This study is one of the first to mechanistically assess the impact of MPFs on lung cells while simultaneously addressing the need for a reliable system that delivers MPFs to ALI cultures to better mimic inhalation and avoid inadequate resuspension of particles in liquid medium.

This study aimed to provide environmental surveillance data for evaluating the risk of acquiring SARS-CoV-2 in public areas with high foot traffic in a university. Air and surface samples were collected at a university that had the second highest number of COVID-19 cases among public higher education institutions in the U.S. during Fall 2020. A total of 60 samples were collected in 16 sampling events performed during Fall 2020 and Spring 2021. Nearly 9800 students traversed the sites during the study period. SARS-CoV-2 was not detected in any air or surface samples. The university followed CDC guidance, including COVID-19 testing, case investigations, and contact tracing. Students, faculty, and staff were asked to maintain physical distancing and wear face coverings. Although COVID-19 cases were relatively high at the university, the possibility of acquiring SARS-CoV-2 infections at the sites tested was low.

CeO2 and CuO nanoparticles (NPs) are used as additives in petrodiesel to enhance engine performance leading to reduced diesel combustion emissions. Despite their benefits, the additive application poses human health concerns by releasing inhalable NPs into the ambient air. In this study, a bioinspired lung cell exposure system, Dosimetric Aerosol in Vitro Inhalation Device (DAVID), was employed for evaluating the toxicity of aerosolized CeO2 and CuO NPs with a short duration of exposure (≤10 min vs. hours in other systems) and without exerting toxicity from non-NP factors. Human epithelial A549 lung cells were cultured and maintained within DAVID at the air-liquid interface (ALI), onto which aerosolized NPs were deposited, and experiments in submerged cells were used for comparison. Exposure of the cells to the CeO2 NPs did not result in detectable IL-8 release, nor did it produce a significant reduction in cell viability based on lactate dehydrogenase (LDH) assay, with a marginal decrease (10%) at the dose of 388 μg/cm2 (273 cm2/cm2). In contrast, exposure to CuO NPs resulted in a concentration dependent reduction in LDH release based on LDH leakage, with 38% reduction in viability at the highest dose of 52 μg/cm2 (28.3 cm2/cm2). Cells exposed to CuO NPs resulted in a dose dependent cellular membrane toxicity and expressed IL-8 secretion at a global dose five times lower than cells exposed under submerged conditions. However, when comparing the ALI results at the local cellular dose of CuO NPs to the submerged results, the IL-8 secretion was similar. In this study, we demonstrated DAVID as a new exposure tool that helps evaluate aerosol toxicity in simulated lung environment. Our results also highlight the necessity in choosing the right assay endpoints for the given exposure scenario, e.g., LDH for ALI and Deep Blue for submerged conditions for cell viability.

Indoor air quality (IAQ) in schools has received attention over the past decades but still lacks specific standards and regulations. This study aimed to review the impact of bioaerosol activity in indoor environments on acute respiratory diseases and explore whether carbon dioxide can be used as an indicator of bioaerosol and respiratory diseases in indoor environments in K-12 school systems. Findings suggest a lack of a consensual approach to evaluate bioaerosols impacting IAQ in indoor infrastructures, particularly in school environments; an elevated CO _2 concentration inside the school classrooms was not uncommon, and the evidence of unsatisfactory and degraded IAQ (surpassing ASHRAE standards) at public schools in rural and urban settings in one of the North Central County, Florida. It was found that CO _2 levels can be associated with bioaerosol activity, and sufficient ventilation within the space substantially reduces the airborne time of respiratory droplets and CO _2 levels. CO _2 monitoring can act as an effective, low-cost alternative to surveying or detecting the prevalence of respiratory diseases, which may hold strength through establishing critical CO _2 thresholds and, thereafter associating it with the infectious doses of pathogen activity.

Objective: To present the detailed history of three cervical cancer patients with rectovaginal fistula, who had undergone radiotherapy. Cases report: A 74-year-old patient with end-stage renal disease undergoing hemodialysis had radiotherapy for her advanced cervical cancer. Colonoscopic biopsy showed radiation sigmoid colitis and ulcers. Laparotomy revealed colon perforation and rectovaginal fistula. The second case is a 54-year-old cervical cancer patient, who had received concurrent chemoradiation therapy and further systemic therapy with cisplatin, paclitaxel, and bevacizumab. She suffered from bloody stool and abdominal pain. Rectovaginal fistula was found during exploratory laparotomy. The third case is a 35-year-old cervical cancer patient, who had received concurrent chemoradiation therapy. Systemic therapy was then prescribed with platinum, paclitaxel, and bevacizumab for her lung metastasis, and a rectovaginal fistula was found later. All three patients did not survive later. Conclusions: Fatal rectovaginal fistula may occur in post-radiation advanced cervical cancer patients. Unnecessary colonoscopic biopsy may cause significant sequelae. In patients with high risk for rectovaginal fistulas, chemotherapy without adding bevacizumab might be suggested in patients with low risk of poor response to chemotherapy. In addition, three-dimensional conformal radiation therapy or intensity-modulated radiation therapy should be used for patients with high risk for fistulas.

ABSTRACT The rapid and accurate identification of the causing agents during bacterial infections would greatly improve pathogen transmission, prevention, patient care, and medical treatments in clinical settings. Although many conventional and molecular methods have been proven to be efficient and reliable, some of them suffer technical biases and limitations that require the development and application of novel and advanced techniques. Recently, due to its cost affordability, noninvasiveness, and label-free feature, Raman spectroscopy (RS) is emerging as a potential technique for fast bacterial detection. However, the method is still hampered by many technical issues, such as low signal intensity, poor reproducibility, and standard data set insufficiency, among others. Thus, it should be cautiously claimed that Raman spectroscopy could provide practical applications in real-world settings. In order to evaluate the implementation potentials of Raman spectroscopy in the identification of bacterial pathogens, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS). A total of 17,149 SERS spectra were harvested from a Raman spectrometer and were further analyzed via machine learning approaches, which showed that a convolutional neural network (CNN) deep learning algorithm achieved the highest prediction accuracy for recognizing pathogenic bacteria at both the genus and species levels. In summary, the SERS technique holds a promising potential for fast bacterial pathogen identification in clinical laboratories with the integration of machine learning algorithms, which might be further developed and sharpened for the direct identification and prediction of bacterial pathogens from clinical samples. IMPORTANCE In this study, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS). A total of 17,149 SERS spectra were harvested from a Raman spectrometer and were further analyzed via machine learning approaches, the results of which showed that the convolutional neural network (CNN) deep learning algorithm could achieve the highest prediction accuracy for recognizing pathogenic bacteria at both the genus and species levels. Taken together, we concluded that the SERS technique held a promising potential for fast bacterial pathogen diagnosis in clinical laboratories with the integration of deep learning algorithms, which might be further developed and sharpened for the direct identification and prediction of bacterial pathogens from clinical samples.

Over the past decades, conventional methods and molecular assays have been developed for the detection of tuberculosis (TB). However, these techniques suffer limitations in the identification of Mycobacterium tuberculosis (Mtb), such as long turnaround time and low detection sensitivity, etc., not even mentioning the difficulty in discriminating antibiotics-resistant Mtb strains that cause great challenges in TB treatment and prevention. Thus, techniques with easy implementation for rapid diagnosis of Mtb infection are in high demand for routine TB diagnosis. Due to the label-free, low-cost and non-invasive features, surface enhanced Raman spectroscopy (SERS) has been extensively investigated for its potential in bacterial pathogen identification. However, at current stage, few studies have recruited handheld Raman spectrometer to discriminate sputum samples with or without Mtb, separate pulmonary Mtb strains from extra-pulmonary Mtb strains, or profile Mtb strains with different antibiotic resistance characteristics. In this study, we recruited a set of supervised machine learning algorithms to dissect different SERS spectra generated via a handheld Raman spectrometer with a focus on deep learning algorithms, through which sputum samples with or without Mtb strains were successfully differentiated (5-fold cross-validation accuracy = 94.32%). Meanwhile, Mtb strains isolated from pulmonary and extra-pulmonary samples were effectively separated (5-fold cross-validation accuracy = 99.86%). Moreover, Mtb strains with different drug-resistant profiles were also competently distinguished (5-fold cross-validation accuracy = 99.59%). Taken together, we concluded that, with the assistance of deep learning algorithms, handheld Raman spectrometer has a high application potential for rapid point-of-care diagnosis of Mtb infections in future.

OBJECTIVES/GOALS: In vitro models that mimic the human respiratory system are needed to assess the toxicity of inhaled contaminants. Therefore, our goal is to establish a Dosimetric Aerosol in-Vitro Inhalation Device (DAVID) that delivers aerosols in different patterns to human lung cells cultured at an air-liquid interface (ALI). METHODS/STUDY POPULATION: The collection unit of DAVID was modified in this study to accommodate different deposition patterns (spots, annular ring, rectangle & circle). CuO aerosols of varying concentrations were generated using a 6-jet Collison nebulizer for varying time periods to achieve different doses. To quantify the doses that were delivered to cells, the samples were digested with nitric acid & analyzed by Inductively Coupled Plasma-Optical Emission Spectrometry. Following the exposure of A549 cells to CuO aerosols, cytotoxicity and mRNA expression (i.e., HMOX1 & IL-8) will be assessed via LDH and RT-qPCR to determine the effect of regional (mass deposited/area of the pattern) and global (mass deposited/area of the cell culture insert) doses in cells. RESULTS/ANTICIPATED RESULTS: The deposition areas covered by rectangular, spot, annular ring, and circular patterns are estimated to be 6, 17, 27 and 85% of the insert’s surface area, onto which cells are cultured. Results for the patterns tested (spots and annular ring) show that both the regional and global doses were greater for spots than annular ring. Also, the regional doses were higher than global doses. Irrespective of the patterns, the global doses were the same for nebulizer suspensions of 0.1-1 mg/mL. Statistical analysis by ANOVA revealed there was no significant difference in doses between replicate inserts used in the same trial. We anticipate that regional doses with aerosol deposition to a larger surface area of the cell culture insert will correspond with higher cytotoxicity and mRNA expression of HMOX1 and IL-8 in cells. DISCUSSION/SIGNIFICANCE: There are limited in vitro exposure systems that can efficiently deliver aerosols to lung cells, while also mimicking inhalation by humans. In addition to addressing this knowledge gap, we will show the role of regional & global doses in studying cellular response & the ability of DAVID to deliver aerosols in different deposition patterns.

Abstract Pathways of transmission of coronavirus (COVID‐19) disease in the human population are still emerging. However, empirical observations suggest that dense human settlements are the most adversely impacted, corroborating a broad consensus that human‐to‐human transmission is a key mechanism for the rapid spread of this disease. Here, using logistic regression techniques, estimates of threshold levels of population density were computed corresponding to the incidence (case counts) in the human population. Regions with population densities greater than 3,000 person per square mile in the United States have about 95% likelihood to report 43,380 number of average cumulative cases of COVID‐19. Since case numbers of COVID‐19 dynamically changed each day until 30 November 2020, ca. 4% of US counties were at 50% or higher probability to 38,232 number of COVID‐19 cases. While threshold on population density is not the sole indicator for predictability of coronavirus in human population, yet it is one of the key variables on understanding and rethinking human settlement in urban landscapes.

Rodent infestations are a common problem that can result in several issues, including diseases, damage to property, and crop loss. Conventional methods of controlling rodent infestations often involve using mousetraps and applying rodenticides manually, leading to high manpower expenses and environmental pollution. To address this issue, we introduce a system for remotely monitoring rodent infestations using Internet of Things (IoT) nodes equipped with Long Range (LoRa) modules. The sensing nodes wirelessly transmit data related to rodent activity to a cloud server, enabling the server to provide real-time information. Additionally, this approach involves using images to auxiliary detect rodent activity in various buildings. By capturing images of rodents and analyzing their behavior, we can gain insight into their movement patterns and activity levels. By visualizing the recorded information from multiple nodes, rodent control personnel can analyze and address infestations more efficiently. Through the digital and quantitative sensing technology proposed at this stage, it can serve as a new objective indicator before and after the implementation of medication or other prevention and control methods. The hardware cost for the proposed system is approximately USD 43 for one sensor module and USD 17 for one data collection gateway (DCG). We also evaluated the power consumption of the sensor module and found that the 3.7 V 18,650 Li-ion batteries in series can provide a battery life of two weeks. The proposed system can be combined with rodent control strategies and applied in real-world scenarios such as restaurants and factories to evaluate its performance.

The occurrence of cases of COVID-19 suggests that it will likely become seasonally endemic in human populations. We seek to provide a quantification of the seasonality of the occurrence and severity of COVID-19 cases in human populations. Using global data, we show that the spatiotemporal distribution of COVID-19 cases is a function of distinct seasons and climates. We investigated this at the county and the country scale using a comparison of seasonal means, correlation analyses using ambient air temperatures and dew point temperatures, and multiple linear regression techniques. We found that most locations had the highest incidence of COVID-19 during winter compared to other seasons. Regions closer to the equator had a higher incidence of COVID-19 during the summer than regions further from the equator. Regions close to the equator, where mean annual temperatures have less variance compared to those further from the equator, had smaller differences between seasonal COVID-19 incidence. Correlation and regression analyses showed that ambient air and dew point temperatures were significantly associated with COVID-19 incidence. Our results suggest that temperature and the environment are influential factors to understand the transmission of COVID-19 within the human population. This research provides empirical evidence that temperature changes are a strong indicator of seasonal COVID-19 outbreaks, and as such it will aid in planning for future outbreaks and for mitigating their impacts.

Objectives: Because the detection of SARS-CoV-2 RNA in aerosols but failure to isolate viable (infectious) virus are commonly reported, there is substantial controversy whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be transmitted through aerosols. This conundrum occurs because common air samplers can inactivate virions through their harsh collection processes. We sought to resolve the question whether viable SARS-CoV-2 can occur in aerosols using VIVAS air samplers that operate on a gentle water vapor condensation principle. Methods: Air samples collected in the hospital room of two coronavirus disease-2019 (COVID-19) patients, one ready for discharge and the other newly admitted, were subjected to RT-qPCR and virus culture. The genomes of the SARS-CoV-2 collected from the air and isolated in cell culture were sequenced. Results: Viable SARS-CoV-2 was isolated from air samples collected 2 to 4.8 m away from the patients. The genome sequence of the SARS-CoV-2 strain isolated from the material collected by the air samplers was identical to that isolated from the newly admitted patient. Estimates of viable viral concentrations ranged from 6 to 74 TCID50 units/L of air. Conclusions: Patients with respiratory manifestations of COVID-19 produce aerosols in the absence of aerosol-generating procedures that contain viable SARS-CoV-2, and these aerosols may serve as a source of transmission of the virus.

Learning disabilities are highly prevalent on college campuses, yet students with learning disabilities graduate at lower rates than those without disabilities. Academic and psychosocial supports are essential for overcoming challenges and for improving postsecondary educational opportunities for students with learning disabilities. A holistic, multi-level model of campus-based supports was established to facilitate culture and practice changes at the institutional level, while concurrently bolstering mentors’ abilities to provide learning disability-knowledgeable support, and simultaneously creating opportunities for students’ personal and interpersonal development. Mixed methods were used to investigate implementation of coordinated personal, interpersonal, and institutional level supports for undergraduate science, technology, engineering, and math (STEM) students with learning disabilities. A one-group pre-test post-test strategy was used to examine undergraduate outcomes. Participants included 52 STEM undergraduates with learning disabilities, 57 STEM graduate student mentors, 34 STEM faculty mentors, and 34 university administrators and personnel as members of a university-wide council. Enrolled for 2 years, undergraduates were engaged in group meetings involving psychoeducation and reflective discussions, development of self-advocacy projects, and individual mentorship. Undergraduates reported improved self-efficacy (p = 0.001), campus connection (p < 0.001), professional development (p ≤ 0.002), and self-advocacy (p < 0.001) after two academic years. Graduate student mentors increased their understanding about learning disabilities and used their understanding to support both their mentees and other students they worked with. Council members identified and created opportunities for delivering learning disability-related trainings to faculty, mentors and advisors on campus, and for enhancing coordination of student services related to learning and related disorders. Disability-focused activities became integrated in broader campus activities regarding diversity. This research explicates a role that college campuses can play in fostering the wellbeing and the academic and career development of its students with developmental learning and related disorders. It offers an empirically tested campus-based model that is multilevel, holistic, and strengths-based for supporting positive outcomes of young people with learning disabilities in STEM. Moreover, findings advance the knowledge of supports and skills that are important for self-regulating and navigating complex and multi-faceted disability-related challenges within both the postsecondary educational environment and the young adults’ sociocultural context.

ABSTRACT The dynamics and significance of aerosol transmission of respiratory viruses are still controversial, for the major reasons that virus aerosols are inefficiently collected by commonly used air samplers and that the collected viruses are inactivated by the collection method. Without knowledge of virus viability, infection risk analyses lack accuracy. This pilot study was performed to (i) determine whether infectious (viable) respiratory viruses in aerosols could be collected from air in a real world environment by the viable virus aerosol sampler (VIVAS), (ii) compare and contrast the efficacy of the standard bioaerosol sampler, the BioSampler, with that of the VIVAS for the collection of airborne viruses in a real world environment, and (iii) gain insights for the use of the VIVAS for respiratory virus sampling. The VIVAS operates via a water vapor condensation process to enlarge aerosolized virus particles to facilitate their capture. A variety of viable human respiratory viruses, including influenza A H1N1 and H3N2 viruses and influenza B viruses, were collected by the VIVAS located at least 2 m from seated patients, during a late-onset 2016 influenza virus outbreak. Whereas the BioSampler when operated following our optimized parameters also collected virus aerosols, it was nevertheless overall less successful based on a lower frequency of virus isolation in most cases. This side-by-side comparison highlights some limitations of past studies based on impingement-based sampling, which may have generated false-negative results due to either poor collection efficiency and/or virus inactivation due to the collection process. IMPORTANCE The significance of virus aerosols in the natural transmission of respiratory diseases has been a contentious issue, primarily because it is difficult to collect or sample virus aerosols using currently available air sampling devices. We tested a new air sampler based on water vapor condensation for efficient sampling of viable airborne respiratory viruses in a student health care center as a model of a real world environment. The new sampler outperformed the industry standard device (the SKC BioSampler) in the collection of natural virus aerosols and in maintaining virus viability. These results using the VIVAS indicate that respiratory virus aerosols are more prevalent and potentially pose a greater inhalation biohazard than previously thought. The VIVAS thus appears to be a useful apparatus for microbiology air quality tests related to the detection of viable airborne viruses.

A novel particle processor was designed and built for the production of flake-shaped powders. 300 μm magnesium and 140 μm iron particles processed for 1 and 2 minutes were analyzed for dimensional, ductile, and morphological characteristics. Particle diameter distributions tended to broaden towards higher size ranges after 2 minutes of processing; the mean particle size was in the range of 400 μm for magnesium and 300 μm for iron. The flake thickness decreased over time, leading to a mean-thickness of 12 μm for the magnesium particle sample processed with 2 × 6.0 mm milling ball media after 2 minutes. The effect of particle medium showed that the milling operation had greater influences on the more ductile material. Surface morphology also became smoother as the milling time increased. Larger ball media tended to produce samples with larger particle sizes with wider size distributions, while smaller ball media produced smaller particles with narrower size distributions. Loading weights also tended to have similar trends. The novel process was demonstrated as an effective and efficient method for the production of flake-shaped metal particles which greatly reduced the amount of milling time and energy required for flake particle production.

On-board hydrogen storage is an important obstacle to the development of a sustainable, ultra-low emission transportation system. A dry particle coating technique was used to coat micron-sized magnesium powders with Ni nanoparticles for hydrogen storage. Three parameters were explored in this study: powder size, nickel loading, and processing time. The composite materials were evaluated based upon a number of criteria, including the degree to which the nanoparticles were distributed over the Mg surface, the improvement in kinetics for hydrogen absorption, and the increased amount of hydrogen absorbed and desorbed. Comparisons were made between the bulk Mg powders and those coated with Ni. Due to the high shear forces it created, the dry particle coating system effectively distributed Ni nanoparticles onto the Mg powder surface. A coating process that required 48 hours using traditional ball milling was reduced to 90 minutes with the dry particle coating system. Magnesium powder with a mean diameter of 44 microns and a nickel loading of 2 atomic weight percent was the most kinetically active for hydrogen absorption under the conditions studied. Hydrogen absorption began at 150°C, and desorption started at 250°C. The dry particle coating system, however, did not alter the magnesium microstructure during 90 minutes of processing and did not produce the large surface areas generated by ball milling.

Owing to the complexity of geo-engineering seepage problems influenced by different random factors, three-dimensional simulation and analysis of the stochastic seepage field plays an important role in engineering applications. A three-dimensional anisotropic heterogeneous steady random seepage model was developed on the basis of the finite element method. A statistical analysis of the distribution characteristics of soil parameters sampled from the main embankment of the Yangtze River in the Southern Jingzhou zone of China was conducted. The Kolomogorov-Smirnov test verified the statistical hypothesis that the permeability coefficient tensor has a Gaussian distribution. With the help of numerical analysis of the stochastic seepage field using the developed model, various statistical and random characteristics of the stochastic seepage field of the main embankment of the Yangtze River in the Southern Jingzhou zone of China were investigated. The model was also examined with statistical testing. Through the introduction of random variation of the upstream and downstream water levels into the model, the effects of the boundary randomness due to variation of the downstream and upstream water levels on the variation of simulated results presented with a vector series of the random seepage field were analyzed. Furthermore, the combined influence of the variation of the soil permeability coefficient and such seepage resistance measures as the cut-off wall and relief ditch on the hydraulic head distribution was analyzed and compared with the results obtained by determinate analysis. Meanwhile, sensitivities of the hydraulic gradient and downstream exit height to the variation of boundary water level were studied. The validity of the simulated results was verified by stochastic testing and measured data. The developed model provides more detail and a full stochastic algorithm to characterize and analyze three-dimensional stochastic seepage field problems.

Bats are natural reservoirs of coronaviruses and other viruses with zoonotic potential. Florida has indigenous non-migratory populations of Brazilian free-tailed bats (Tadarida brasiliensis) that mostly roost in colonies in artificial structures. Unlike their counterparts in Brazil and Mexico, the viruses harbored by the Florida bats have been underexplored. We report the detection of an alphacoronavirus RNA-dependent RNA polymerase (RdRp) gene sequence in the feces of two of 19 different T. brasiliensis that were capture/release bats that had been evaluated for overall health. The RdRp sequence is similar but not identical to previously detected sequences in the feces of two different species of bats (T. brasiliensis and Molossus molossus) in Brazil. In common with the experience of others doing similar work, attempts to isolate the virus in cell cultures were unsuccessful. We surmise that this and highly related alphacoronavirus are carried by Brazilian free-tailed bats living in a wide eco-spatial region. As various coronaviruses (CoVs) that affect humans emerged from bats, our study raises the question whether CoVs such as the one detected in our work are yet-to-be-detected pathogens of humans and animals other than bats.

As young adults transition into adulthood they must develop effective self-regulation techniques to help them navigate the heightened expectations of independence placed upon them. During this challenging developmental stage, mentors and other supportive individuals can facilitate co-regulation processes that help young adults reach self-regulation. This qualitative research identifies and characterizes supportive processes of regulation that graduate student mentors engaged in as part of their mentorship interactions with undergraduate mentees with learning disabilities and attention-deficit hyperactivity disorder (LD/ADHD). Participants were part of a larger campus-based study with 52 undergraduate mentees with LD/ADHD and 57 graduate student mentors. Data were transcripts from mentor group meetings (N = 20) discussing LD/ ADHD and the mentorship experiences, undergraduate group meetings (N = 13) discussing LD/ADHD experiences and supports, and an instrumental mentor case study. Structural coding was used to identify content related to mentorship experiences; process coding was used to describe the actions and roles undertaken by mentors; content analysis was used to examine relative salience of topics discussed during mentor group meetings. Four themes emerged describing the ways in which mentors acted as co-regulators for mentees including: Fostering Positive Relationships, Guidance Based on a Similar Path, Supporting Strategy Generation, and Supporting Mentees by Setting Limits. Findings highlight key actions and processes for effective co-regulation techniques used by disability-informed mentors, that support the self-regulation practices of undergraduate students with LD/ADHD to reach their educational, career, and personal goals. [ABSTRACT FROM AUTHOR]