Internal iron loading and warm temperatures are pre-conditions for cyanobacterial dominance in embayments along Georgian Bay, Great Lakes

Previous work suggests that a high rate of internal ferrous iron (Fe2+) loading from anoxic sediments into overlying waters favours cyanobacteria dominance (> 50% of the phytoplankton biomass) over eukaryotic algae. This Cyanobacteria-Ferrous conceptual model was assessed along the Georgian Bay coastline of Lake Huron, Ontario in one meso-eutrophic and three oligotrophic embayments which experience natural hypolimnetic anoxia. Cyanobacteria dominated all embayments in the relatively warmer summer of 2012 but not in much cooler 2014 although hypolimnetic anoxia and internal Fe2+ loading were observed in both summers in all embayments. A cyanobacteria bloom large enough to turn the lake visibly green was observed only in warmer 2012 in the meso-eutrophic embayment. Results show that warm summer temperatures and internal Fe2+ loading are necessary pre-conditions for cyanobacteria dominance while high nutrient levels are needed to form large blooms. There were no consistent patterns between dominance and total and dissolved phosphorus (P), total nitrogen, ammonium and nitrate. Internal P loading was not a necessary pre-condition for dominance. While P removal programs will decrease phytoplankton biomass in eutrophic waters, oxidized surficial sediments must be maintained throughout an aquatic system to prevent cyanobacteria dominance.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

An integrated tool to assess the role of new planting in PM10 capture and the human health benefits: a case study in London.

The role of vegetation in mitigating the effects of PM(10) pollution has been highlighted as one potential benefit of urban greenspace. An integrated modelling approach is presented which utilises air dispersion (ADMS-Urban) and particulate interception (UFORE) to predict the PM(10) concentrations both before and after greenspace establishment, using a 10 x 10 km area of East London Green Grid (ELGG) as a case study. The corresponding health benefits, in terms of premature mortality and respiratory hospital admissions, as a result of the reduced exposure of the local population are also modelled. PM(10) capture from the scenario comprising 75% grassland, 20% sycamore maple (Acer pseudoplatanus L.) and 5% Douglas fir (Pseudotsuga menziesii (Mirb.) Franco) was estimated to be 90.41 t yr(-1), equating to 0.009 t ha(-1) yr(-1) over the whole study area. The human health modelling estimated that 2 deaths and 2 hospital admissions would be averted per year

The effects of a nurse case manager and a community health worker team on diabetic control, emergency department visits, and hospitalizations among urban African Americans with type 2 diabetes mellitus: a randomized controlled trial.

BACKGROUND: Although African American adults bear a disproportionate burden from diabetes mellitus (DM), few randomized controlled trials have tested culturally appropriate interventions to improve DM care. METHODS: We randomly assigned 542 African Americans with type 2 DM enrolled in an urban managed care organization to either an intensive or minimal intervention group. The intensive intervention group consisted of all components of the minimal intervention plus individualized, culturally tailored care provided by a nurse case manager (NCM) and a community health worker (CHW), using evidence-based clinical algorithms with feedback to primary care providers (eg, physicians, nurse practitioners, or physician assistants). The minimal intervention consisted of mailings and telephone calls every 6 months to remind participants about preventive screenings. Data on diabetic control were collected at baseline and at 24 months by blind observers; data emergency department (ER) visits and hospitalizations were assessed using administrative data. RESULTS: At baseline, participants had a mean age of 58 years, 73% were women, and 50% were living in poverty. At 24 months, compared with the minimal intervention group, those in the intensive intervention group were 23% less likely to have ER visits (rate difference [RD], -14.5; adjusted rate ratio [RR], 0.77; 95% confidence interval [CI], 0.59-1.00). In on-treatment analyses, the rate reduction was strongest for patients who received the most NCM and CHW visits (RD, -31.0; adjusted RR, 0.66; 95% CI, 0.43-1.00; rate reduction downward arrow 34%). CONCLUSION: These data suggest that a culturally tailored intervention conducted by an NCM/CHW team reduced ER visits in urban African Americans with type 2 DM. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00022750

Estimating Glycemia from HbA1c and CGM: Analysis of Accuracy and Sources of Discrepancy

Objective. To examine the accuracy of different periods of continuous glucose monitoring (CGM), hemoglobin A1c (HbA1c), and their combination for estimating mean glycemia over 90 days (AG90).Research Design and Methods. We retrospectively studied 985 90-day CGM periods with Results. Using 14 days of CGM at the end of the 90-day period resulted in a mean absolute error [95th percentile] of 14 [34] mg/dL compared with eAG90. Using two non-overlapping 14-day periods reduced the error to 6 [15] mg/dl. Non-glycemic effects on HbA1c led to a mean absolute error for average glucose calculated from HbA1c of 12 [29] mg/dL. Combining 14 days of CGM with HbA1c reduced the error to 10 [26] mg/dL. Mismatches between CGM and HbA1c greater than 40 mg/dL occurred more than 5% of the time.Conclusions. The accuracy of estimates of eAG90 from limited periods of CGM can be improved by averaging with HbA1c or extending the monitoring period beyond 26 days. Large mismatches between eAG90 estimated from CGM and HbA1c are not unusual and may persist due to stable non-glycemic factors.</p

A quantifier-based fuzzy classification system for breast cancer patients

Objectives:Recent studies of breast cancer data have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a range of different clustering techniques. Consensus between unsupervised classification algorithms has been successfully used to categorise patients into these specific groups, but often at the expenses of not classifying the whole set. It is known that fuzzy methodologies can provide linguistic based classification rules. The objective of this study was to investigate the use of fuzzy methodologies to create an easy to interpret set of classification rules, capable of placing the large majority of patients into one of the specified groups. Materials and methods: In this paper, we extend a data-driven fuzzy rule-based system for classification purposes (called ‘fuzzy quantification subsethood-based algorithm’) and combine it with a novel class assignment procedure. The whole approach is then applied to a well characterised breast cancer dataset consisting of ten protein markers for over 1000 patients to refine previously identified groups and to present clinicians with a linguistic ruleset. A range of statistical approaches was used to compare the obtained classes to previously obtained groupings and to assess the proportion of unclassified patients. Results: A rule set was obtained from the algorithm which features one classification rule per class, using labels of High, Low or Omit for each biomarker, to determine the most appropriate class for each patient. When applied to the whole set of patients, the distribution of the obtained classes had an agreement of 0.9 when assessed using Kendall's Tau with the original reference class distribution. In doing so, only 38 patients out of 1073 remain unclassified, representing a more clinically usable class assignment algorithm. Conclusion: The fuzzy algorithm provides a simple to interpret, linguistic rule set which classifies over 95% of breast cancer patients into one of seven clinical groups