Modeling Cost Effectiveness of Green Infrastructure at Stormwater Runoff Critical Points in Maunalua Bay Watershed, Oʻahu

Like many urbanized areas, the watersheds surrounding Maunalua Bay are highly developed with impervious surfaces and channelized waterways. This can cause issues with stormwater. Stormwater is water that flows over impermeable surfaces (roads, roofs, etc.) after heavy rain events. Stormwater can pick up pollutants as is flows down slope, negatively impacting the health of water bodies. It can also cause flood events impacting infrastructure and lives Green Infrastructure (G.I.) techniques can be implemented to improve conventional infrastructure and stormwater management. Green infrastructure is an approach to stormwater management that tries to mimic the natural water cycle. Most green infrastructure traps and treats water from a storm event and then slowly releases it back into the environment allowing for more control on the quantity of water being released. We created a map that identifies areas in Maunalua that have the highest potential for stormwater mitigation via G.I. Using existing maps on land cover, slope , soil permeability and storm drain density, we created a model that ranks each map attribute in terms of stormwater risk. This map can assist regional stakeholders in prioritizing and evaluating the costs and benefits of adopting G.I. techniques.Our model identified two stormwater "hotspots" within the Kamilo Iki watershed. One "hotspot" validated the model with existing green infrastructure already present. The other "hotspot" lacked green infrastructure. Using the EPA stormwater calculator we identified the most cost effective green infrastructure for a residential neighborhood.Malama Maunalu

Variability in Functional Traits along an Environmental Gradient in the South African Resurrection Plant Myrothamnus flabellifolia

Many desiccation-tolerant plants are widely distributed and exposed to substantial environmental variation across their native range. These environmental differences generate site-specific selective pressures that could drive natural variation in desiccation tolerance across populations. If identified, such natural variation can be used to target tolerance-enhancing characteristics and identify trait associations within a common genetic background. Here, we tested for natural variation in desiccation tolerance across wild populations of the South African resurrection plant Myrothamnus flabellifolia. We surveyed a suite of functional traits related to desiccation tolerance, leaf economics, and reproductive allocation in M. flabellifolia to test for trait associations and tradeoffs. Despite considerable environmental variation across the study area, M. flabellifolia plants were extremely desiccation tolerant at all sites, suggesting that tolerance is either maintained by selection or fixed in these populations. However, we detected notable associations between environmental variation, population characteristics, and fitness traits. Relative to mesic sites, plants in xeric sites were more abundant and larger, but were slower growing and less reproductive. The negative association between growth and reproduction with plant size and abundance pointed towards a potential growth–abundance tradeoff. The finding that M. flabellifolia is more common in xeric sites despite reductions in growth rate and reproduction suggests that these plants thrive in extreme aridity

Phenogrouping heart failure with preserved or mildly reduced ejection fraction using electronic health record data

Background: Heart failure (HF) with preserved or mildly reduced ejection fraction includes a heterogenous group of patients. Reclassification into distinct phenogroups to enable targeted interventions is a priority. This study aimed to identify distinct phenogroups, and compare phenogroup characteristics and outcomes, from electronic health record data. Methods: 2,187 patients admitted to five UK hospitals with a diagnosis of HF and a left ventricular ejection fraction ≥ 40% were identified from the NIHR Health Informatics Collaborative database. Partition-based, model-based, and density-based machine learning clustering techniques were applied. Cox Proportional Hazards and Fine-Gray competing risks models were used to compare outcomes (all-cause mortality and hospitalisation for HF) across phenogroups. Results: Three phenogroups were identified: (1) Younger, predominantly female patients with high prevalence of cardiometabolic and coronary disease; (2) More frail patients, with higher rates of lung disease and atrial fibrillation; (3) Patients characterised by systemic inflammation and high rates of diabetes and renal dysfunction. Survival profiles were distinct, with an increasing risk of all-cause mortality from phenogroups 1 to 3 (p < 0.001). Phenogroup membership significantly improved survival prediction compared to conventional factors. Phenogroups were not predictive of hospitalisation for HF. Conclusions: Applying unsupervised machine learning to routinely collected electronic health record data identified phenogroups with distinct clinical characteristics and unique survival profiles

The Re-Establishment of Desiccation Tolerance in Germinated Arabidopsis thaliana Seeds and Its Associated Transcriptome

The combination of robust physiological models with “omics” studies holds promise for the discovery of genes and pathways linked to how organisms deal with drying. Here we used a transcriptomics approach in combination with an in vivo physiological model of re-establishment of desiccation tolerance (DT) in Arabidopsis thaliana seeds. We show that the incubation of desiccation sensitive (DS) germinated Arabidopsis seeds in a polyethylene glycol (PEG) solution re-induces the mechanisms necessary for expression of DT. Based on a SNP-tile array gene expression profile, our data indicates that the re-establishment of DT, in this system, is related to a programmed reversion from a metabolic active to a quiescent state similar to prior to germination. Our findings show that transcripts of germinated seeds after the PEG-treatment are dominated by those encoding LEA, seed storage and dormancy related proteins. On the other hand, a massive repression of genes belonging to many other classes such as photosynthesis, cell wall modification and energy metabolism occurs in parallel. Furthermore, comparison with a similar system for Medicago truncatula reveals a significant overlap between the two transcriptomes. Such overlap may highlight core mechanisms and key regulators of the trait DT. Taking into account the availability of the many genetic and molecular resources for Arabidopsis, the described system may prove useful for unraveling DT in higher plants

Phase-amplitude coupled persistent theta and gamma oscillations in rat primary motor cortex in vitro

In vivo, theta (4-7 Hz) and gamma (30-80 Hz) neuronal network oscillations are known to coexist and display phase-amplitude coupling (PAC). However, in vitro, these oscillations have for many years been studied in isolation. Using an improved brain slice preparation technique we have, using co-application of carbachol (10 μM) and kainic acid (150 nM), elicited simultaneous theta (6.6 ± 0.1 Hz) and gamma (36.6 ± 0.4 Hz) oscillations in rodent primary motor cortex (M1). Each oscillation showed greatest power in layer V. Using a variety of time series analyses we detected significant cross-frequency coupling 74% of slice preparations. Differences were observed in the pharmacological profile of each oscillation. Thus, gamma oscillations were reduced by the GABAA receptor antagonists, gabazine (250 nM and 2 μM), and picrotoxin (50 μM) and augmented by AMPA receptor antagonism with SYM2206 (20 μM). In contrast, theta oscillatory power was increased by gabazine, picrotoxin and SYM2206. GABAB receptor blockade with CGP55845 (5 μM) increased both theta and gamma power, and similar effects were seen with diazepam, zolpidem, MK801 and a series of metabotropic glutamate receptor antagonists. Oscillatory activity at both frequencies was reduced by the gap junction blocker carbenoxolone (200 μM) and by atropine (5 μM). These data show theta and gamma oscillations in layer V of rat M1 in vitro are cross-frequency coupled, and are mechanistically distinct. The development of an in vitro model of phase-amplitude coupled oscillations will facilitate further mechanistic investigation of the generation and modulation of coupled activity in mammalian cortex

Polymorphisms near TBX5 and GDF7 are associated with increased risk for Barrett's esophagus.

BACKGROUND & AIMS: Barrett's esophagus (BE) increases the risk of esophageal adenocarcinoma (EAC). We found the risk to be BE has been associated with single nucleotide polymorphisms (SNPs) on chromosome 6p21 (within the HLA region) and on 16q23, where the closest protein-coding gene is FOXF1. Subsequently, the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON) identified risk loci for BE and esophageal adenocarcinoma near CRTC1 and BARX1, and within 100 kb of FOXP1. We aimed to identify further SNPs that increased BE risk and to validate previously reported associations. METHODS: We performed a genome-wide association study (GWAS) to identify variants associated with BE and further analyzed promising variants identified by BEACON by genotyping 10,158 patients with BE and 21,062 controls. RESULTS: We identified 2 SNPs not previously associated with BE: rs3072 (2p24.1; odds ratio [OR] = 1.14; 95% CI: 1.09-1.18; P = 1.8 × 10(-11)) and rs2701108 (12q24.21; OR = 0.90; 95% CI: 0.86-0.93; P = 7.5 × 10(-9)). The closest protein-coding genes were respectively GDF7 (rs3072), which encodes a ligand in the bone morphogenetic protein pathway, and TBX5 (rs2701108), which encodes a transcription factor that regulates esophageal and cardiac development. Our data also supported in BE cases 3 risk SNPs identified by BEACON (rs2687201, rs11789015, and rs10423674). Meta-analysis of all data identified another SNP associated with BE and esophageal adenocarcinoma: rs3784262, within ALDH1A2 (OR = 0.90; 95% CI: 0.87-0.93; P = 3.72 × 10(-9)). CONCLUSIONS: We identified 2 loci associated with risk of BE and provided data to support a further locus. The genes we found to be associated with risk for BE encode transcription factors involved in thoracic, diaphragmatic, and esophageal development or proteins involved in the inflammatory response

Immunoglobulin A Antibodies against Ricin A and B Subunits Protect Epithelial Cells from Ricin Intoxication

Epithelial cells of the respiratory and gastrointestinal tracts are extremely vulnerable to the cytotoxic effects of ricin, a Shiga-like toxin with ribosome-inactivating properties. While mucosal immunity to ricin correlates with secretory immunoglobulin A (IgA) antibody levels in vivo, the potential of IgA to protect epithelial cells from ricin in vitro has not been examined due to the unavailability of well-defined antitoxin IgA antibodies. Here we report the characterization of four monoclonal IgA antibodies (IgA MAbs) produced from the Peyer's patches and mesenteric lymph nodes of BALB/c mice immunized intragastrically with ricin toxoid. Two IgA MAbs (33G2 and 35H6) were active against ricin's lectin subunit (RTB), and two (23D7 and 25A4) reacted with the toxin's enzymatic subunit (RTA). All four IgA MAbs neutralized ricin in a Vero cell cytotoxicity assay, blocked toxin-induced interleukin-8 release by the human monocyte/macrophage cell line 28SC, and protected polarized epithelial cell monolayers from ricin-mediated protein synthesis inhibition. 33G2 and 35H6 reduced ricin binding to the luminal surfaces of human intestinal epithelial cells to undetectable levels in tissue section overlay assays, whereas 23D7 had no effect on toxin attachment. 23D7 and 25A4 did, however, reduce ricin transcytosis across MDCK II cell monolayers, possibly by interfering with intracellular toxin transport. We conclude that IgA antibodies against RTA and RTB can protect mucosal epithelial cells from ricin intoxication

Variability in Functional Traits along an Environmental Gradient in the South African Resurrection Plant Myrothamnus flabellifolia

Many desiccation-tolerant plants are widely distributed and exposed to substantial environmental variation across their native range. These environmental differences generate site-specific selective pressures that could drive natural variation in desiccation tolerance across populations. If identified, such natural variation can be used to target tolerance-enhancing characteristics and identify trait associations within a common genetic background. Here, we tested for natural variation in desiccation tolerance across wild populations of the South African resurrection plant Myrothamnus flabellifolia. We surveyed a suite of functional traits related to desiccation tolerance, leaf economics, and reproductive allocation in M. flabellifolia to test for trait associations and tradeoffs. Despite considerable environmental variation across the study area, M. flabellifolia plants were extremely desiccation tolerant at all sites, suggesting that tolerance is either maintained by selection or fixed in these populations. However, we detected notable associations between environmental variation, population characteristics, and fitness traits. Relative to mesic sites, plants in xeric sites were more abundant and larger, but were slower growing and less reproductive. The negative association between growth and reproduction with plant size and abundance pointed towards a potential growth&ndash;abundance tradeoff. The finding that M. flabellifolia is more common in xeric sites despite reductions in growth rate and reproduction suggests that these plants thrive in extreme aridity

Phenogrouping heart failure with preserved or mildly reduced ejection fraction using electronic health record data

Background: Heart failure with preserved or mildly reduced ejection fraction includes a heterogenous group of patients. Reclassification into distinct phenogroups to enable targeted interventions is a priority. This study aimed to identify distinct phenogroups, compare phenogroup characteristics and outcomes, and identify factors to straightforwardly predict phenogroup membership, from electronic health record data.Methods: 2,187 patients admitted to five UK hospitals with a diagnosis of HF and a left ventricular ejection fraction &gt; 40% were identified from the NIHR Health Informatics Collaborative database. Partition-based, model-based, and density-based machine learning clustering techniques were applied. Cox Proportional Hazards and Fine-Gray competing risks models were used to compare outcomes (all-cause mortality and hospitalisation for HF) across phenogroups. Penalised multinomial logistic regression was applied to predict phenogroup membership.Results: Three phenogroups were identified: 1. Younger, predominantly female patients with high prevalence of cardiometabolic and coronary disease; 2. More frail patients, with higher rates of lung disease and atrial fibrillation; 3. Patients characterised by systemic inflammation and high rates of diabetes and renal dysfunction. Survival profiles were distinct, with an increasing risk of all-cause mortality from phenogroups 1 to 3 (p &lt; 0.001). Phenogroup membership significantly improved survival prediction compared to conventional factors. Phenogroups were not predictive of hospitalisation for HF. A combination of ten variables assigned patients to phenogroups with 90% accuracy.Conclusions: Applying unsupervised machine learning to routinely collected electronic health record data identified phenogroups with distinct clinical characteristics and unique survival profiles.<br/

Identification of heart failure hospitalisation from NHS Digital data:comparison with expert adjudication

Background and Aims: Population-wide, person-level, linked electronic health record data are increasingly used to estimate epidemiology, guide resource allocation, and identify events in clinical trials. The accuracy of data from NHS Digital (now part of NHS England) for identifying hospitalisation for heart failure (HHF), a key HF standard, is not clear. This study aimed to evaluate the accuracy of NHS Digital data for identifying HHF.Methods: Patients experiencing at least one HHF, as determined by NHS Digital data, and age and sex matched patients not experiencing HHF, were identified from a prospective cohort study and underwent expert adjudication. Three code sets commonly used to identify HHF were applied to the data and compared with expert adjudication (I50: International Classification of Diseases (ICD)-10 codes beginning I50; OIS: Clinical Commissioning Groups Outcomes Indicator Set; NICOR: National Institute for Cardiovascular Outcomes Research, used as the basis for the National Heart Failure Audit in England and Wales). Results: 504 patients underwent expert adjudication, of which 10 (2%) were adjudicated to have experienced HHF. Specificity was high across all three code sets in the first diagnosis position (I50: 96·2% [95% confidence interval, CI: 94·1 – 97·7%]; NICOR: 93·3% [CI 90·8 – 95·4%]; OIS: 95·6% [CI 93·3 – 97·2%]), but decreased substantially as the number of diagnosis positions expanded. Sensitivity (40·0% [CI 12·2 – 73·8%]) and positive predictive value (PPV) (highest with I50: 17·4% [CI 8·1 – 33·6%]) were low in the first diagnosis position for all coding sets. PPV was higher for the National Heart Failure Audit criteria, albeit modestly (36·4%; [16·6 – 62·2%]).Conclusions: NHS Digital data were not able to accurately identify HHF, and should not be used in isolation for this purpose. <br/