High-Frequency in Situ Optical Measurements During a Storm Event: Assessing Relationships Between Dissolved Organic Matter, Sediment Concentrations, and Hydrologic Processes

Dissolved organic matter (DOM) dynamics during storm events has received considerable attention in forested watersheds, but the extent to which storms impart rapid changes in DOM concentration and composition in highly disturbed agricultural watersheds remains poorly understood. In this study, we used identical in situ optical sensors for DOM fluorescence (FDOM) with and without filtration to continuously evaluate surface water DOM dynamics in a 415 km(2) agricultural watershed over a 4 week period containing a short-duration rainfall event. Peak turbidity preceded peak discharge by 4 h and increased by over 2 orders of magnitude, while the peak filtered FDOM lagged behind peak turbidity by 15 h. FDOM values reported using the filtered in situ fluorometer increased nearly fourfold and were highly correlated with dissolved organic carbon (DOC) concentrations (r(2) = 0.97), providing a highly resolved proxy for DOC throughout the study period. Discrete optical properties including specific UV absorbance (SUVA(254)), spectral slope (S(290-350)), and fluorescence index (FI) were also strongly correlated with in situ FDOM and indicate a shift toward aromatic, high molecular weight DOM from terrestrially derived sources during the storm. The lag of the peak in FDOM behind peak discharge presumably reflects the draining of watershed soils from natural and agricultural landscapes. Field and experimental evidence showed that unfiltered FDOM measurements underestimated filtered FDOM concentrations by up to similar to 60% at particle concentrations typical of many riverine systems during hydrologic events. Together, laboratory and in situ data provide insights into the timing and magnitude of changes in DOM quantity and quality during storm events in an agricultural watershed, and indicate the need for sample filtration in systems with moderate to high suspended sediment loads

Polymorphisms in Cyclooxygenase, Lipoxygenase and TP53 genes predict colorectal polyp risk reduction by aspirin in the seAFOod polyp prevention trial

YesAspirin and eicosapentaenoic acid (EPA) reduce colorectal adenomatous polyp risk and affect synthesis of oxylipins including prostaglandin E2. We investigated whether 35 single nucleotide polymorphisms (SNPs) in oxylipin metabolism genes such as cyclooxygenase [PTGS] and lipoxygenase [ALOX], as well as 7 SNPs already associated with colorectal cancer (CRC) risk reduction by aspirin (eg. TP53; rs104522), modified the effects of aspirin and EPA on colorectal polyp recurrence in the randomised 2x2 factorial seAFOod trial. Treatment effects were reported as the incidence rate ratio (IRR) and 95% confidence interval (CI) by stratifying negative binomial and Poisson regression analyses of colorectal polyp risk on SNP genotype. Statistical significance was reported with adjustment for the false discovery rate as the P and q value. Five hundred and forty-two (of 707) trial participants had both genotype and colonoscopy outcome data. Reduction in colorectal polyp risk in aspirin users compared with non-aspirin users was restricted to rs4837960 (PTGS1) common homozygotes (IRR 0.69 [95%CI 0.53,0.90]; q=0.06), rs2745557 (PTGS2) compound heterozygote-rare homozygotes (IRR 0.60 [0.41,0.88]; q=0.06), rs7090328 (ALOX5) rare homozygotes (IRR 0.27 [0.11,0.64]; q=0.05), rs2073438 (ALOX12) common homozygotes (IRR 0.57 [0.41,0.80]; q=0.05), and rs104522 (TP53) rare homozygotes (IRR 0.37 [0.17,0.79]; q=0.06). No modification of colorectal polyp risk in EPA users was observed. In conclusion, genetic variants relevant to the proposed mechanism of action on oxylipins are associated with differential colorectal polyp risk reduction by aspirin in individuals who develop multiple colorectal polyps. SNP genotypes should be considered during development of personalised, predictive models of CRC chemoprevention by aspirin.Funder(s): Efficacy and Mechanism Evaluation Programme (EME) Award Id(s): NIHR128210. Funder(s): NIHR Senior Investigator grant. Funder(s): Cancer Research UK (CRUK) Award Id(s): C23434/A24939. Funder(s): European Union-BBSRC (UK) Award Id(s): BB/P028233/1

A review of inclusive business models and their application in aquaculture development

Open Access Journal; Published online: 30 Jan 2020For aquaculture to continue along its current growth trajectory and contribute towards achieving the Sustainable Development Goals, value chains must become more inclusive. Smallholders and other local value chain actors are often constrained by circumstances and market failures in the global aquaculture industry. Integrating these actors into aquaculture value chains through inclusive business models (IBMs) is often touted as a solution to sustainable and ethical trade and business that can generate development outcomes. We reviewed 36 papers under seven business models commonly used in agriculture development to assess their application in aquaculture value chains in lower‐income countries. A global value chain (GVC) analysis is used to unpack the economic and social upgrading objectives of the different IBMs, as well as the types of relational coordination used between actors in the chain to achieve development outcomes. The extent to which these IBMs helped poor actors overcome certain barriers is evaluated with a focus on how they may ensure or be a risk to inclusiveness through the relations and upgrading opportunities evident in their make‐up. The analysis found that the majority of the models focused on economic upgrading over social upgrading. Providing opportunities for the latter is key to achieving the inclusive objectives of IBMs. Greater horizontal coordination between actors can create further opportunities for economic upgrading established under vertical coordination with other nodes upstream and downstream in a value chain. There is a need to further contextualize these models to aquaculture systems and develop clear indicators of inclusiveness

A review of techniques for parameter sensitivity analysis of environmental models

Mathematical models are utilized to approximate various highly complex engineering, physical, environmental, social, and economic phenomena. Model parameters exerting the most influence on model results are identified through a ‘sensitivity analysis’. A comprehensive review is presented of more than a dozen sensitivity analysis methods. This review is intended for those not intimately familiar with statistics or the techniques utilized for sensitivity analysis of computer models. The most fundamental of sensitivity techniques utilizes partial differentiation whereas the simplest approach requires varying parameter values one-at-a-time. Correlation analysis is used to determine relationships between independent and dependent variables. Regression analysis provides the most comprehensive sensitivity measure and is commonly utilized to build response surfaces that approximate complex models.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42691/1/10661_2004_Article_BF00547132.pd

Latest results of dark matter detection with the DarkSide experiment

In this contribution the latest results of dark matter direct detection obtained by the DarkSide Collaboration are discussed. New limits on the scattering cross-section between dark matter particles and baryonic matter have been set. The results have been reached using the DarkSide-50 detector, a double-phase Time Projection Chamber (TPC) filled with 40Ar and installed at Laboratori Nazionali del Gran Sasso (LNGS). In 2018, the DarkSide Collaboration has performed three different types of analysis. The so-called high-mass analysis into the range between ∼ 10 GeV and ∼ 1000 GeV is discussed under the hypothesis of scattering between dark matter and Ar nuclei. The low-mass analysis, performed using the same hypothesis, extends the limit down to ∼1.8 GeV. Through a different hypothesis, that predicts dark matter scattering off the electrons inside of the Ar atom, it has been possible to set limits for sub-GeV dark matter masses

The Cholecystectomy As A Day Case (CAAD) Score: A Validated Score of Preoperative Predictors of Successful Day-Case Cholecystectomy Using the CholeS Data Set

Background Day-case surgery is associated with significant patient and cost benefits. However, only 43% of cholecystectomy patients are discharged home the same day. One hypothesis is day-case cholecystectomy rates, defined as patients discharged the same day as their operation, may be improved by better assessment of patients using standard preoperative variables. Methods Data were extracted from a prospectively collected data set of cholecystectomy patients from 166 UK and Irish hospitals (CholeS). Cholecystectomies performed as elective procedures were divided into main (75%) and validation (25%) data sets. Preoperative predictors were identified, and a risk score of failed day case was devised using multivariate logistic regression. Receiver operating curve analysis was used to validate the score in the validation data set. Results Of the 7426 elective cholecystectomies performed, 49% of these were discharged home the same day. Same-day discharge following cholecystectomy was less likely with older patients (OR 0.18, 95% CI 0.15–0.23), higher ASA scores (OR 0.19, 95% CI 0.15–0.23), complicated cholelithiasis (OR 0.38, 95% CI 0.31 to 0.48), male gender (OR 0.66, 95% CI 0.58–0.74), previous acute gallstone-related admissions (OR 0.54, 95% CI 0.48–0.60) and preoperative endoscopic intervention (OR 0.40, 95% CI 0.34–0.47). The CAAD score was developed using these variables. When applied to the validation subgroup, a CAAD score of ≤5 was associated with 80.8% successful day-case cholecystectomy compared with 19.2% associated with a CAAD score >5 (p < 0.001). Conclusions The CAAD score which utilises data readily available from clinic letters and electronic sources can predict same-day discharges following cholecystectomy