Application of the Aquifer Impact Model to support decisions at a CO2 sequestration site

The National Risk Assessment Partnership (NRAP) has developed a suite of tools to assess and manage risk at CO sequestration sites. The NRAP tool suite includes the Aquifer Impact Model (AIM), which evaluates the potential for groundwater impacts from leaks of CO and brine through abandoned wellbores. There are two aquifer reduced-order models (ROMs) included with the AIM tool, a confined alluvium aquifer, and an unconfined carbonate aquifer. The models accept aquifer parameters as a range of variable inputs so they may have broad applicability. The generic aquifer models may be used at the early stages of site selection, when site-specific data is not available. Guidelines have been developed for determining when the generic ROMs might be applicable to a new site. This paper considers the application of the AIM to predicting the impact of CO or brine leakage were it to occur at the Illinois Basin Decatur Project (IBDP). Results of the model sensitivity analysis can help guide characterization efforts; the hydraulic parameters and leakage source term magnitude are more sensitive than clay fraction or cation exchange capacity. Sand permeability was the only hydraulic parameter measured at the IBDP site. More information on the other hydraulic parameters could reduce uncertainty in risk estimates. Some non-adjustable parameters are significantly different for the ROM than for the observations at the IBDP site. The generic ROMs could be made more useful to a wider range of sites if the initial conditions and no-impact threshold values were adjustable parameters. © 2017 Society of Chemical Industry and John Wiley & Sons, Ltd. 2 2

The Merging of Two Dynasties—Identification of an African Cotton Leaf Curl Disease-Associated Begomovirus with Cotton in Pakistan

Cotton leaf curl disease (CLCuD) is a severe disease of cotton that occurs in Africa and Pakistan/northwestern India. The disease is caused by begomoviruses in association with specific betasatellites that differ between Africa and Asia. During survey of symptomatic cotton in Sindh (southern Pakistan) Cotton leaf curl Gezira virus (CLCuGV), the begomovirus associated with CLCuD in Africa, was identified. However, the cognate African betasatellite (Cotton leaf curl Gezira betasatellite) was not found. Instead, two Asian betasatellites, the CLCuD-associated Cotton leaf curl Multan betasatellite (CLCuMB) and Chilli leaf curl betasatellite (ChLCB) were identified. Inoculation of the experimental plant species Nicotiana benthamiana showed that CLCuGV was competent to maintain both CLCuMB and ChLCB. Interestingly, the enations typical of CLCuD were only induced by CLCuGV in the presence of CLCuMB. Also in infections involving both CLCuMB and ChLCB the enations typical of CLCuMB were less evident. This is the first time an African begomovirus has been identified on the Indian sub-continent, highlight the growing threat of begomoviruses and particularly the threat of CLCuD causing viruses to cotton cultivation in the rest of the world

Molecular diversity of Cotton leaf curl Gezira virus isolates and their satellite DNAs associated with okra leaf curl disease in Burkina Faso

Okra leaf curl disease (OLCD) is a major constraint on okra (Abelmoschus esculentus) production and is widespread in Africa. Using a large number of samples representative of the major growing regions in Burkina Faso (BF), we show that the disease is associated with a monopartite begomovirus and satellite DNA complexes. Twenty-three complete genomic sequences of Cotton leaf curl Gezira virus (CLCuGV) isolates associated with OLCD, sharing 95 to 99% nucleotide identity, were cloned and sequenced. Six betasatellite and four alphasatellite (DNA-1) molecules were also characterized. The six isolates of betasatellite associated with CLCuGV isolates correspond to Cotton leaf curl Gezira betasatellite (CLCuGB) (88 to 98% nucleotide identity). One isolate of alphasatellite is a variant of Cotton leaf curl Gezira alphasatellite (CLCuGA) (89% nucleotide identity), whereas the three others isolates appear to correspond to a new species of alphasatellite (CLCuGA most similar sequence present 52 to 60% nucleotide identity), provisionally named Okra leaf curl Burkina Faso alphasatellite (OLCBFA). Recombination analysis of the viruses demonstrated the interspecies recombinant origin of all CLCuGV isolates, with parents being close to Hollyhock leaf crumple virus (AY036009) and Tomato leaf curl Diana virus (AM701765). Combined with the presence of satellites DNA, these results highlight the complexity of begomoviruses associated with OLCD

A Live-Attenuated HSV-2 ICP0− Virus Elicits 10 to 100 Times Greater Protection against Genital Herpes than a Glycoprotein D Subunit Vaccine

Glycoprotein D (gD-2) is the entry receptor of herpes simplex virus 2 (HSV-2), and is the immunogen in the pharmaceutical industry's lead HSV-2 vaccine candidate. Efforts to prevent genital herpes using gD-2 subunit vaccines have been ongoing for 20 years at a cost in excess of $100 million. To date, gD-2 vaccines have yielded equivocal protection in clinical trials. Therefore, using a small animal model, we sought to determine if a live-attenuated HSV-2 ICP0− virus would elicit better protection against genital herpes than a gD-2 subunit vaccine. Mice immunized with gD-2 and a potent adjuvant (alum+monophosphoryl lipid A) produced high titers of gD-2 antibody. While gD-2-immunized mice possessed significant resistance to HSV-2, only 3 of 45 gD-2-immunized mice survived an overwhelming challenge of the vagina or eyes with wild-type HSV-2 (MS strain). In contrast, 114 of 115 mice immunized with a live HSV-2 ICP0− virus, 0ΔNLS, survived the same HSV-2 MS challenges. Likewise, 0ΔNLS-immunized mice shed an average 125-fold less HSV-2 MS challenge virus per vagina relative to gD-2-immunized mice. In vivo imaging demonstrated that a luciferase-expressing HSV-2 challenge virus failed to establish a detectable infection in 0ΔNLS-immunized mice, whereas the same virus readily infected naïve and gD-2-immunized mice. Collectively, these results suggest that a HSV-2 vaccine might be more likely to prevent genital herpes if it contained a live-attenuated HSV-2 virus rather than a single HSV-2 protein

Adherence in the CAPRISA 004 tenofovir gel microbicide trial.

CAPRISA, 2014.High adherence is key to microbicide effectiveness. Here we provide a description of adherence interventions and the adherence rates achieved in the CAPRISA 004 Tenofovir Gel Trial. Adherence support for the before-and-after dosing strategy (BAT 24) was provided at enrolment and at each monthly study visit. This initially comprised individual counselling and was replaced midway by a structured theory-based adherence support program (ASP) based on motivational interviewing. The 889 women were followed for an average of 18 months and attended a total of 17031 monthly visits. On average women reported 5 sex acts and returned 5.9 empty applicators per month. The adherence rate based on applicator count in relation to all reported sex acts was 72.2% compared to the 82.0% self-reported adherence during the last sex act. Adherence support activities, which achieve levels of adherence similar to or better than those achieved by the CAPRISA 004 ASP, will be critical to the success of future microbicide trials

Women with pregnancies had lower adherence to 1% Tenofovir vaginal gel as HIV preexposure prophylaxis in CAPRISA 004, a phase IIB randomized-controlled trial.

Background: Antiretroviral prophylaxis may be a critical strategy to reduce periconception HIV transmission. Maximizing the benefit of periconception pharmacologic HIV risk-reduction requires an understanding of the links between pregnancy and adherence to this prevention strategy. Methods: We assessed study gel adherence among women with pregnancies compared to women without pregnancies enrolled in the CAPRISA 004 phase IIB trial of 1% vaginal tenofovir gel. Pregnancy was assessed with monthly urine tests. Adherence was measured monthly and defined as proportion of sex acts covered by two returned, used applicators based on pre- and post-coital dosing. High adherence was defined as a median adherence score of >80%, that is, more than 80% of sex acts were covered by two applications of study gel. A multivariate generalized estimating equations (GEE) model with a binomial distribution was used to assess covariates associated with high adherence (>80%) over time. Median adherence before and after pregnancy was compared using Wilcoxon signed rank test. Results: Among 868 women, 53 had at least 1 pregnancy (4.06 per 100 woman years, 95% CI: 3.04, 5.31). Women with pregnancies had lower median adherence compared to women without pregnancies (50% [IQR: 45–83] vs. 60% [IQR: 50–100], p = 0.02). Women with pregnancies also had a 48% lower odds of high adherence compared to women without pregnancies when adjusting for confounders (aOR 0.52, 95%CI: 0.41–0.66, p<0.0001). Among women with pregnancies, adherence before and after pregnancy was not different (50% [IQR: 46–83] vs. 55% [IQR: 20–100], p = 0.68). Conclusions: Women with pregnancies were less likely to have high adherence to study gel compared to women without pregnancies. Understanding these differences may inform findings from HIV prevention trials and future implementation of antiretroviral prophylaxis for at-risk women who choose to conceive. The protocol for the parent trial is registered on ClinicalTrials.gov, NCT00441298, http://www.clinicaltrials.gov/ct2/show/NCT00441298

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Outlier detection in indoor localization and Internet of Things (IoT) using machine learning

In Internet of things (IoT) millions of devices are intel- ligently connected for providing smart services. Especially in in- door localization environment, that is one of the most concerning topic of smart cities, internet of things and wireless sensor net- works. Many technologies are being used for localization purpose in indoor environment and Wi-Fi using received signal strengths (RSSs) is one of them. Wi-Fi RSSs are sensitive to reflection, re- fraction, interference and channel noise that cause irregularity in signal strengths. The irregular and anomalous RSS values, used in a Wi-Fi indoor localization environment, cannot define the location of any unknown node correctly. Therefore, this research has de- veloped an outlier detection technique named as iF_Ensemble for Wi-Fi indoor localization environment by analyzing RSSs us- ing the combination of supervised, unsupervised and ensemble ma- chine learning methods. In this research isolation forest (iForest) is used as an unsupervised learning method. Supervised learning method includes support vector machine (SVM), K-nearest neigh- bor (KNN) and random forest (RF) classifiers with stacking that is an ensemble learning method. For the evaluation purpose accu- racy, precision, recall, F-score and ROC-AUC curve are used. The evaluation of used machine learning method provides high accu- racy of 97.8 percent with proposed outlier detection methods and almost 2 percent improvement in the accuracy of localization pro- cess in indoor environment after eliminating outliers