Understanding the bio-physical characteristics of a fen ecosystem to inform management and conserve the rare habitat

Fen-wetland ecosystems are rare nationwide. Their unique groundwater regime and chemistry, along with a floating, vegetated peat mat that may occur, support diverse and rare plant and wildlife communities. A fen’s ecological benefits are considered even greater within an urbanized setting through its natural attenuation of runoff and pollutants, though these ecosystem services may diminish should the urban-sourced impacts eventually alter the fen’s bio-physical condition. Here we will present how determining a fen’s key bio-physical factors and understanding their combined sensitivity to external processes is necessary to define and address potential threats to a fen’s conservation. Located within a 100-acre Metro-owned natural area along the lower Willamette River, the last known remaining fen composed of a groundwater-fed lake with a densely vegetated floating peat mat in the region is vulnerable to threats that could alter its fragile biochemistry. Threats include stormwater runoff, groundwater reductions from local pumping, nutrient input from septic tanks, and invasive species. To inform conservation measures, Metro’s goal was to assess the fen’s watershed inputs and bio-physical condition by studying the site hydrology, water and soil quality, and vegetation. Initial results reveal a unique ecosystem with counter groundwater and surface-water flow directions due to the unique geologic setting, eutrophic lake conditions from high nutrient loading and concentration, acidic water chemistry and soils from parent bedrock materials, and a diverse plant community consisting of 27 taxa of rare plants. Ongoing monitoring of the fen is helping to assess its condition, help detect future trends, and inform preservation of this unique habitat and potential recommendations for restoring disturbed fens elsewhere in the region

Using a neural network approach to accelerate disequilibrium chemistry calculations in exoplanet atmospheres

In this era of exoplanet characterisation with JWST, the need for a fast implementation of classical forward models to understand the chemical and physical processes in exoplanet atmospheres is more important than ever. Notably, the time-dependent ordinary differential equations to be solved by chemical kinetics codes are very time-consuming to compute. In this study, we focus on the implementation of neural networks to replace mathematical frameworks in one-dimensional chemical kinetics codes. Using the gravity profile, temperature-pressure profiles, initial mixing ratios, and stellar flux of a sample of hot-Jupiters atmospheres as free parameters, the neural network is built to predict the mixing ratio outputs in steady state. The architecture of the network is composed of individual autoencoders for each input variable to reduce the input dimensionality, which is then used as the input training data for an LSTM-like neural network. Results show that the autoencoders for the mixing ratios, stellar spectra, and pressure profiles are exceedingly successful in encoding and decoding the data. Our results show that in 90% of the cases, the fully trained model is able to predict the evolved mixing ratios of the species in the hot-Jupiter atmosphere simulations. The fully trained model is ~1000 times faster than the simulations done with the forward, chemical kinetics model while making accurate predictions.Comment: 13 pages, 9 figures, accepted for publication at MNRA

Factors Influencing Physician Treatment Strategies in Crotaline Snake Envenomation

BACKGROUND: Crotaline snake envenomation is a potentially serious medical condition that affects thousands of Americans each year. There continues to be variation in treatment practices by physicians in the United States despite guidelines establishing the use of antivenom and supportive care as the mainstays for treating crotaline snake envenomation. METHODS: This study sought to determine associations between physician treatment strategies, snake identification (ID), venom effects, bite location and patient presentation. A cross-sectional review of electronic medical records (EMR) for patients diagnosed with venomous snake bites from July 1, 2014 to August 31, 2019 was completed. Data collected from the EMR included: patient demographics, transfer information, length of hospital and ICU stays, snake ID, bite site, progression of local tissue effects, additional clinical and lab results, patient comorbidities and complications, and provider treatment strategy. RESULTS: Of the 83 patients who met inclusion criteria, 68 patients (81.9%) received antivenom. None of the 15 patients who were under observation (no antivenom) for treatment went to the ICU. These patients experienced the shortest hospital stays (H(2)=16.76, p<0.001). Hospital stays were longest for patients envenomated by an identified rattlesnake or cottonmouth compared to patients envenomated by an unknown snake or copperhead (H(2)=14.32, p<0.05). Rattlesnake envenomations used more vials of antivenom than copperhead envenomations (H(2)=8.76, p=0.01). In a regression model of treatment strategy, progression of local tissue effects was the only statistically significant predictor of receiving antivenom while other independent variables including snake ID, patient age, hemotoxicity, systemic symptoms, site of the snakebite, and patient comorbidities were not significant predictors. Lastly, there was a statistically significant association between treatment strategy and opioid prescription, with 77.9% of patients who received antivenom also receiving an opioid for pain management vs. 33.3% of patients under observation (no antivenom) who received opioids (Fisher Exact Probability Test, p=0.001). CONCLUSION: Envenomated patients are likely to be treated with antivenom if there was progression of local tissue effects. For patients in this study who were bitten by copperheads and unknown snakes, close observation without antivenom administration had favorable outcomes including shorter hospital stays and likely decreased hospital costs.N

Associations of social determinants of health and childhood obesity. A cross-sectional analysis of the 2021 National Survey of Children’s Health

Background: Childhood obesity is a growing health problem in the United States, with those affected having an increased likelihood of developing chronic diseases at a younger age. Social determinants of health (SDOH) are known to influence overall health. Children with low socioeconomic status (SES) have been shown to be overweight and have poor health outcomes. Therefore, our primary objective was to use the National Survey of Children’s Health (NSCH) 2021 data to determine current associations between childhood obesity and social determinants of health (SDOH).Methods: We conducted a cross-sectional analysis of 2021 NSCH to extract data from questions related to the SDOH domains. We extracted sociodemographic variables to use as controls and constructed bivariate and multivariable logistic regression models to determine associations, via odds ratios, between SDOH and child obesity.Results: Within the binary regression models, we found that children identified as having obesity were more likely than non-obese children to experience SDOH in all domains. After controlling for race/ethnicity, household income (%FPL), parental education, and child sex, children identified as having obesity were significantly more likely to experience food insecurity when compared to non-obese children (AOR = 1.39; 95% CI: 1.13-1.17).Conclusion: Our study found that the food insecurity domain of SDOH was significantly associated with childhood obesity. Improving policies for programs such as SNAP as well as addressing lack of access to nutritious foods, especially within food deserts, may help alleviate some food insecurity. Improving access to adequate amounts of nutritious foods is critical in addressing childhood obesity and thus, decreasing risk of chronic disease and poor long-term health outcomes

Connecting Online Graduate Students to the University Community

The purpose of this study was to understand how to facilitate a sense of belonging among online graduate students. Sense of community theory was the basis for examining students’ desires to be a part of a community and feel connected to the institution. Findings from a survey using the Sense of Community Index 2 (SCI-2) of online graduate students provided insight into the sense of community, the importance of belonging to a community, and activities that could strengthen their connection with the institution. Overall students showed a slightly stronger sense of belonging to their program than to the institution. Students were also asked how they prefer to connect to others in the community with mentoring and inperson social events being the most requested

Association and disparities of food insecurity and child abuse: Analysis of the National Survey of Children’s Health

Background: Child abuse is a major public health issue and is a significant risk factor for compromised development, health morbidities, and the development of mental and behavioral disorders in children. Many factors contribute to child abuse, especially family stressors. Food insecurity, a significant family stressor, likely increases the rate of child abuse while also contributing directly and indirectly to the consequences on child development and lifespan. Given the adverse effects of child abuse and food insecurity, investigating their relationship is crucial to developing mitigation strategies.Purpose: Our primary objective was to assess the relationship between child abuse and food insecurity using data from the National Survey of Children’s Health (NSCH). Given that these disproportionately affect children of different demographic groups, our study aims to identify associations amongst varying demographic factors.Methods: We conducted an observational study assessing the National Survey of Children’s Health (2016-2021) to investigate the relationship between food security and child abuse. Using survey weights provided by the NSCH, we determined population estimates and rates of children experiencing food insecurity and child abuse. We then constructed logistic regression models to assess associations, via odds ratio, between food security groups and whether the child experienced child abuse. Finally, we constructed logistic regression models, via odds ratios, to assess food security and child abuse by demographic factors.Results: While rates of food security were similar across age groups, households with lower income had higher rates of marginal or low food security, as well as homes with Black, Indigenous, multi-racial, and Hispanic children. Compared to those with high food security, the odds of children with marginal or low food security were significantly more likely to experience child abuse (AORs: 2.36, 95% CI: 2.17-2.57 and 5.24, 95% CI: 4.59-6.00, respectively). Compared to White children with high food security, Indigenous, Black, and White children were significantly more likely to experience child abuse as household food security decreased.Conclusion: Child abuse and food insecurity have a significant association, including overlapping contributory factors and disparities. Efforts to improve food insecurity through policy, community food banks, and school-based programs may secondarily reduce child abuse. To address racial/ethnic disparities, the expansion of culturally-competent, evidence-based programs to reduce food insecurity should be established, which may also reduce risk factors for child abuse

A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential

The current deluge of newly identified RNA transcripts presents a singular opportunity for improved assessment of coding potential, a cornerstone of genome annotation, and for machine-driven discovery of biological knowledge. While traditional, feature-based methods for RNA classification are limited by current scientific knowledge, deep learning methods can independently discover complex biological rules in the data de novo. We trained a gated recurrent neural network (RNN) on human messenger RNA (mRNA) and long noncoding RNA (lncRNA) sequences. Our model, mRNA RNN (mRNN), surpasses state-of-the-art methods at predicting protein-coding potential despite being trained with less data and with no prior concept of what features define mRNAs. To understand what mRNN learned, we probed the network and uncovered several context-sensitive codons highly predictive of coding potential. Our results suggest that gated RNNs can learn complex and long-range patterns in full-length human transcripts, making them ideal for performing a wide range of difficult classification tasks and, most importantly, for harvesting new biological insights from the rising flood of sequencing data

Astro2020 Science White Paper: Triggered High-Priority Observations of Dynamic Solar System Phenomena

Unexpected dynamic phenomena have surprised solar system observers in the past and have led to important discoveries about solar system workings. Observations at the initial stages of these events provide crucial information on the physical processes at work. We advocate for long-term/permanent programs on ground-based and space-based telescopes of all sizes - including Extremely Large Telescopes (ELTs) - to conduct observations of high-priority dynamic phenomena, based on a predefined set of triggering conditions. These programs will ensure that the best initial dataset of the triggering event are taken; separate additional observing programs will be required to study the temporal evolution of these phenomena. While not a comprehensive list, the following are notional examples of phenomena that are rare, that cannot be anticipated, and that provide high-impact advances to our understandings of planetary processes. Examples include: new cryovolcanic eruptions or plumes on ocean worlds; impacts on Jupiter, Saturn, Uranus, or Neptune; extreme eruptions on Io; convective superstorms on Saturn, Uranus, or Neptune; collisions within the asteroid belt or other small-body populations; discovery of an interstellar object passing through our solar system (e.g. 'Oumuamua); and responses of planetary atmospheres to major solar flares or coronal mass ejections.Comment: Astro2020 white pape

High Crime Neighborhoods as a Driver for Toxic Stress Leading to Asthma

BACKGROUND: Social determinants of health and allostatic load theory suggest social environment can drive asthma diagnoses via the mechanism of toxic stress, the prolonged activation of stress response systems. While research has linked neighborhood crime to asthma, multivariate causal modeling has not been used to test toxic stress as the mechanism that links the two. The current study investigates neighborhood crime as a driver of pediatric asthma diagnoses via toxic stress. METHODS: A retrospective geospatial analysis of health and crime data was conducted. Health data was collected from the OU-Tulsa General Pediatric Clinic’s Electronic Medical Record while crime data was collected from the Tulsa Police Department. All variables were mapped geospatially using census tract as the unit of analysis. Structural equation modeling was used to test the causal model. Neighborhood crime indicators included homicide, rape, and narcotic-related offenses. Diagnoses of conduct, attention deficit, and other anxiety disorders were used in the analysis as toxic stress indicators. Asthma diagnoses were the outcome variable. To further test the model, data from 2016 was used as a calibration sample while data from 2017 was used as a validation sample. RESULTS: A full mediation model of high crime neighborhoods (n = 134) as a driver of toxic stress resulting in increased asthma diagnoses fit the 2016 data well (Χ2 = 15.6, p =.27; df = 13; RMSEA = .04 [90% CI: .00, .10]; CFI: .99; SRMR = .04). The results indicated the model accounted for 78% (R2 = .78) of the variance in asthma diagnoses. The model also provided a good fit to the 2017 data (X2= 23.6, p<.001; df= 13; RMSEA = .08 [90% CI: .02, .13]; CFI: .96; SRMR=.06). CONCLUSION: The results of the current study have important practice and research implications. While clinicians and researchers have become increasingly aware of the impact of social determinants of health, there has been little focus on improving clinical practices. Physicians interested in alleviated the burden of toxic stress and asthma should explore ways to reduce neighborhood crime at the policy level while also being aware of each of their patients’ unique circumstances in relation to where they live.N

Patterns of High-Dose and Long-Term Proton Pump Inhibitor Use: A Cross-Sectional Study in Six South Australian Residential Aged Care Services

Aim: While proton pump inhibitors (PPIs) are generally considered safe and well tolerated, frail older people who take PPIs long term may be susceptible to adverse events. This study characterized PPI use and determined factors associated with high-dose use among older adults in residential aged care services (RACSs).Methods: A cross-sectional study of 383 residents of six South Australian RACSs within the same organization was conducted. Clinical, diagnostic, and medication data were collected by study nurses. The proportions of residents who took a PPI for > 8 weeks and without documented indications were calculated. Factors associated with high-dose PPI use compared to standard/low doses were identified using age- and sex-adjusted logistic regression models.Results: 196 (51%) residents received a PPI, with 45 (23%) prescribed a high dose. Overall, 173 (88%) PPI users had documented clinical indications or received medications that can increase bleeding risk. Three-quarters of PPI users with gastroesophageal reflux disease or dyspepsia had received a PPI for > 8 weeks. High-dose PPI use was associated with increasing medication regimen complexity [odds ratio (OR) 1.02; 95% confidence interval (CI) 1.01–1.04 per one-point increase in Medication Regimen Complexity Index score] and a greater number of medications prescribed for regular use (OR 1.11; 95% CI 1.01–1.21 per additional medication).Conclusions: Half of all residents received a PPI, of whom the majority had documented clinical indications or received medications that may increase bleeding risk. There remains an opportunity to review the continuing need for treatment and consider “step-down” approaches for high-dose PPI users.</p