The Comparison of GEV, Log-Pearson Type 3 and Gumbel Distributions in the Upper Thames River Watershed under Global Climate Models

The increase in greenhouse gas emissions has had a severe impact on global temperature, and is affecting weather patterns worldwide. With this global climate change, precipitation levels are changing, and in many places are drastically increasing. The need to be able to accurately predict extreme precipitation events is imperative in designing for not only the safety of infrastructure, but also people’s lives. To predict these events, the use of historical data is necessary, along with statistical distributions that are used to fit the data. In this study, historical data from the London International Airport station has been used, along with 11 different Atmosphere Ocean Global Climate Models (AOGCMs), which are used to predict future climate variables. These models produced a total of 27 different data sets of annual maximum precipitation over a period of 117 years, for storm durations of 1, 2, 6, 12 and 24 hours. The current Environment Canada recommended distribution is the Gumbel (EV1) distribution, and the current United States distribution is the Log-Pearson type 3 (LP3). This report investigates a third distribution, the Generalized Extreme Value (GEV) distribution, in the context of the Upper Thames River Watershed. The historical data set and the data sets derived from AOGCMs were used with the GEV, LP3 and EV1 distributions, and the goodness of fit tests were performed to select which was most appropriate distribution. L-Moment Ratio diagrams were also constructed to help establish the most suitable distribution. All results showed that GEV was very appropriate to the Upper Thames River Watershed data, and it was often the favored distribution. This report shows the need for more studies to be carried out on the GEV distribution, to ensure we are using the most appropriate methods for predicting these extreme precipitation events.https://ir.lib.uwo.ca/wrrr/1039/thumbnail.jp

Methods to decrease variability in histological scoring in placentas from a cohort of preterm infants

OBJECTIVE: Reliable semiquantitative assessment of histological placental acute inflammation is problematic, even among experts. Tissue samples in histology slides often show variability in the extent and location of neutrophil infiltrates. We sought to determine whether the variability in pathologists\u27 scoring of neutrophil infiltrates in the placenta could be reduced by the use of \u27regions of interest\u27 (ROIs) that break the sample into smaller components. DESIGN: ROIs were identified within stained H&E slides from a cohort of 56 women. ROIs were scored using a semiquantitative scale (0-4) for the average number of neutrophils by at least two independent raters. SETTING: Preterm singleton births at Yale New Haven Hospital. PARTICIPANTS: This study used stained H&E placental slides from a cohort of 56 women with singleton pregnancies who had a clinically indicated amniocentesis within 24 hours of delivery. PRIMARY AND SECONDARY OUTCOME MEASURES: Interrater agreement was assessed with the intraclass correlation coefficient (ICC) and log-linear regression. Predictive validity was assessed using amniotic fluid protein profile scores (neutrophil defensin-2, neutrophil defensin-1, calgranulin C and calgranulin A). RESULTS: Excellent agreement by the ICC was found for the average neutrophil scores within a region of interest. Log-linear analyses suggest that even where there is disagreement, responses are positively associated along the diagonal. There was also strong evidence of predictive validity comparing pathologists\u27 scores with amniotic fluid protein profile scores. CONCLUSIONS: Agreement among observers of semiquantitative neutrophil scoring through the use of digitised ROIs was demonstrated to be feasible with high reliability and validity

A semiparametric modeling framework for potential biomarker discovery and the development of metabonomic profiles

<p>Abstract</p> <p>Background</p> <p>The discovery of biomarkers is an important step towards the development of criteria for early diagnosis of disease status. Recently electrospray ionization (ESI) and matrix assisted laser desorption (MALDI) time-of-flight (TOF) mass spectrometry have been used to identify biomarkers both in proteomics and metabonomics studies. Data sets generated from such studies are generally very large in size and thus require the use of sophisticated statistical techniques to glean useful information. Most recent attempts to process these types of data model each compound's intensity either discretely by positional (mass to charge ratio) clustering or through each compounds' own intensity distribution. Traditionally data processing steps such as noise removal, background elimination and m/z alignment, are generally carried out separately resulting in unsatisfactory propagation of signals in the final model.</p> <p>Results</p> <p>In the present study a novel semi-parametric approach has been developed to distinguish urinary metabolic profiles in a group of traumatic patients from those of a control group consisting of normal individuals. Data sets obtained from the replicates of a single subject were used to develop a functional profile through Dirichlet mixture of beta distribution. This functional profile is flexible enough to accommodate variability of the instrument and the inherent variability of each individual, thus simultaneously addressing different sources of systematic error. To address instrument variability, all data sets were analyzed in replicate, an important issue ignored by most studies in the past. Different model comparisons were performed to select the best model for each subject. The m/z values in the window of the irregular pattern are then further recommended for possible biomarker discovery.</p> <p>Conclusion</p> <p>To the best of our knowledge this is the very first attempt to model the physical process behind the time-of flight mass spectrometry. Most of the state of the art techniques does not take these physical principles in consideration while modeling such data. The proposed modeling process will apply as long as the basic physical principle presented in this paper is valid. Notably we have confined our present work mostly within the modeling aspect. Nevertheless clinical validation of our recommended list of potential biomarkers will be required. Hence, we have termed our modeling approach as a "framework" for further work.</p

Molecular Characterization of Methicillin-Resistant Staphylococci from the Dairy Value Chain in Two Indian States

Bovine milk and milk products may contain pathogens, antimicrobial resistant bacteria, and antibiotic residues that could harm consumers. We analyzed 282 gram-positive isolates from milk samples from dairy farmers and vendors in Haryana and Assam, India, to assess the prevalence of methicillin-resistant staphylococci using microbiological tests, antibiotic susceptibility testing, and genotyping by PCR. The prevalence of genotypic methicillin resistance in isolates from raw milk samples was 5% [95% confidence interval, CI (3–8)], with 7% [CI (3–10)] in Haryana, in contrast to 2% [CI (0.2–6)] in Assam. The prevalence was the same in isolates from milk samples collected from farmers [5% (n = 6), CI (2–11)] and vendors [5% (n = 7), CI (2–10)]. Methicillin resistance was also observed in 15% of the isolates from pasteurized milk [(n = 3), CI (3–38)]. Two staphylococci harboring a novel mecC gene were identified for the first time in Indian dairy products. The only SCCmec type identified was Type V. The staphylococci with the mecA (n = 11) gene in raw milk were commonly resistant to oxacillin [92%, CI (59–100)] and cefoxitin [74%, CI (39–94)], while the isolates with mecC (n = 2) were resistant to oxacillin (100%) only. All the staphylococci with the mecA (n = 3) gene in pasteurized milk were resistant to both oxacillin and cefoxitin. Our results provided evidence that methicillin-resistant staphylococci occur in dairy products in India with potential public health implications. The state with more intensive dairy systems (Haryana) had higher levels of methicillin-resistant bacteria in milk

Too Hot to Handle: A Survey of Attitudes towards Fever of 462 Pediatric Intensive Care Unit staff

The role played by fever in the outcome of critical illness in children is unclear. This survey of medical and nursing staff in 35 paediatric intensive care units and transport teams in the United Kingdom and Ireland established attitudes towards the management of children with fever. Four hundred sixty-two medical and nursing staff responded to a web-based survey request. Respondents answered eight questions regarding thresholds for temperature control in usual clinical practice, indications for paracetamol use, and readiness to participate in a clinical trial of permissive temperature control. The median reported threshold for treating fever in clinical practice was 38 °C (IQR 38–38.5 °C). Paracetamol was reported to be used as an analgesic and antipyretic but also for non-specific comfort indications. There was a widespread support for a clinical trial of a permissive versus a conservative approach to fever in paediatric intensive care units. Within a trial, 58% of the respondents considered a temperature of 39 °C acceptable without treatment. Conclusions: Staff on paediatric intensive care units in the United Kingdom and Ireland tends to treat temperatures within the febrile range. There was a willingness to conduct a randomized controlled trial of treatment of fever

Seropersistence of SII-ChAdOx1 nCoV-19 (COVID-19 vaccine): 6-month follow-up of a randomized,controlled, observer-blind, phase 2/3 immuno-bridging study in Indian adults

AZD1222 (ChAdOx1 nCoV-19) is a replication-deficient adenoviral vectored coronavirus disease-19 (COVID-19) vaccine that is manufactured as SII-ChAdOx1 nCoV-19 by the Serum Institute of India Pvt Ltd following technology transfer from Oxford University/AstraZeneca. The non-inferiority of SII-ChAdOx1 nCoV-19 with AZD1222 was previously demonstrated in an observer-blind, phase 2/3 immuno-bridging study (trial registration: CTRI/2020/08/027170). In this analysis of immunogenicity and safety data 6 months post first vaccination (Day 180), 1,601 participants were randomized 3:1 to SII-ChAdOx1 nCoV-19 or AZD1222 (immunogenicity/reactogenicity cohort n = 401) and 3:1 to SII-ChAdOx1 nCoV-19 or placebo (safety cohort n = 1,200). Immunogenicity was measured by anti-severe acute respiratory syndrome coronavirus 2 spike (anti-S) binding immunoglobulin G and neutralizing antibody (nAb) titers. A decline in anti-S titers was observed in both vaccine groups, albeit with a greater decline in SII-ChAdOx1 nCoV-19 vaccinees (geometric mean titer [GMT] ratio [95% confidence interval (CI) of SII-ChAdOx1 nCoV-19 to AZD1222]: 0.60 [0.41-0.87]). Consistent similar decreases in nAb titers were observed between vaccine groups (GMT ratio [95% CI]: 0.88 [0.44-1.73]). No cases of severe COVID-19 were reported following vaccination, while one case was observed in the placebo group. No causally related serious adverse events were reported through 180 days. No thromboembolic or autoimmune adverse events of special interest were reported. Collectively, these data illustrate that SII-ChAdOx1 nCoV-19 maintained a high level of immunogenicity 6 months post-vaccination. SII-ChAdOx1 nCoV-19 was safe and well tolerated