Hydrologic Analysis of Two Headwater Lake Basins of Differing Lake pH in the West-central Adirondack Mountains of New York

Hydrologic analysis of two headwater lake basins in the Adirondack Mountains, New York, during 1980-81 indicates that the degree of neutralization of acid precipitation is controlled by the groundwater contribution to the lake. According to flow-duration analyses, daily mean outflow/unit area from the neutral lake (Panther Lake, pH 5-7) was more sustained and contained a higher percentage of groundwater than that of the acidic lake (Woods Lake, pH 4-5). Outflow recession rates and maximum base-flow rates, derived from individual recession curves, were 3.9 times and 1.5 times greater, respectively, in the neutral-lake basin than in the acidic-lake basin. Groundwater contribution to lake outflow was also calculated from a lake-water budget; the groundwater contribution to the neutral lake was about 10 times greater than that to the acidic lake. Thick sandy till forms the groundwater reservoir and the major recharge area in both basins but covers 8.5 times more area in the neutral-lake basin than in the acidic-lake basin. More groundwater storage within the neutral basin provides longer contact time with neutralizing minerals and more groundwater discharge. As a result, the neutral lake has relatively high pH and alkalinity, and more net cation transport. (USGS

Comparing the emergence of Echinochloa crus-galli populations in different locations. Part II: similarities and threshold parameters

The variability in the emergence process of different populations was confirmed for two Echinochloa crus-galli populations, one from Italy (IT) and the second from Norway (NO). Seeds were sown in 12 localities over Europe and the Middle East, and the emergence patterns of IT and NO were compared with those of several local populations at each location. Seeds of each population were sown in pots buried to the ground level. The base temperature (Tb) for emergence was estimated by (1) analysing logistic models applied to the field emergence of IT and NO, and (2) a germination assay set in winter 2020 at constant temperatures (8, 11, 14, 17, 20, 26, 29°C) with newly collected seeds in 2019 from the same fields where IT and NO had previously been harvested in 2015. The logistic models developed for IT and NO in each location showed that the emergence pattern of IT was similar to that of the local populations in Poland, Italy, Spain, Turkey South and Iran, while NO fitted better to those in Sweden and Latvia. No germination was obtained for IT in a germination chamber, but the estimated Tb with the logistic model was 11.2°C. For NO, the estimated Tb was 8.8°C in the germination chamber and 8.1°C in the field. Results suggest that adaptation to local environmental conditions has led to inter-population differences in Tb and parameter estimates of thermal-time models to predict the emergence of E. crus-galli should only be used for populations with similar climatic and habitat conditions.The authors thank all the technicians, students and institutions that have contributed to establishing and maintaining the field experiment. We also thank Dr. Frank Forcella and James Eklund, from the USDA‐ARS in Morris (MN), for providing the dataloggers and facilitating the collection of soil temperature data in each location. Our thanks also to the Spanish Ministry of Economy and Competitiveness for funding to Royo‐Esnal through the AGL2017‐83325‐C4‐2‐R; Duzce Üniversitesi, Turkey, for funding to Uludag (Project No: 2015.11.02.375); and the Norwegian Research Funding for Agriculture and the Food Industry and project partners in Research Council of Norway Project no. 267700 for supporting Tørresen in the experiment. Uludag thank his two graduate students, Miss Buyukkurt and Zambak, and Murdoch thank MSc student, Mr Guangxing Xie, who carried out some of the germination assays. Royo‐Esnal thank Jordi Izquierdo for providing the seeds of L3 population for the experiment in Lleida. Finally, the authors are also grateful to the EWRS, for providing funds to enable the working group participants to meet and discuss the collaborative experiment

Asthma, Airflow Obstruction, and Eosinophilic Airway Inflammation Prevalence in Western Kenya: A Population-Based Cross-Sectional Study

Objectives: Determine the prevalence of airway disease (e.g., asthma, airflow obstruction, and eosinophilic airway inflammation) in Kenya, as well as related correlates of airway disease and health-related quality of life.Methods: A three-stage, cluster-randomized cross-sectional study in Uasin Gishu County, Kenya was conducted. Individuals 12 years and older completed questionnaires (including St. George’s Respiratory Questionnaire for COPD, SGRQ-C), spirometry, and fractional exhaled nitric oxide (FeNO) testing. Prevalence ratios with 95% confidence intervals (CIs) were calculated. Multivariable models were used to assess correlates of airflow obstruction and high FeNO.Results: Three hundred ninety-two participants completed questionnaires, 369 completed FeNO testing, and 305 completed spirometry. Mean age was 37.5 years; 64% were women. The prevalence of asthma, airflow obstruction on spirometry, and eosinophilic airway inflammation was 21.7%, 12.3% and 15.7% respectively in the population. Women had significantly higher SGRQ-C scores compared to men (15.0 vs. 7.7). Wheezing or whistling in the last year and SGRQ-C scores were strongly associated with FeNO levels >50 ppb after adjusting for age, gender, BMI, and tobacco use.Conclusion: Airway disease is a significant health problem in Kenya affecting a young population who lack a significant tobacco use history

Tiered Approach to Resilience Assessment

Regulatory agencies have long adopted a three-tier framework for risk assessment. We build on this structure to propose a tiered approach for resilience assessment that can be integrated into the existing regulatory processes. Comprehensive approaches to assessing resilience at appropriate and operational scales, reconciling analytical complexity as needed with stakeholder needs and resources available, and ultimately creating actionable recommendations to enhance resilience are still lacking. Our proposed framework consists of tiers by which analysts can select resilience assessment and decision support tools to inform associated management actions relative to the scope and urgency of the risk and the capacity of resource managers to improve system resilience. The resilience management framework proposed is not intended to supplant either risk management or the many existing efforts of resilience quantification method development, but instead provide a guide to selecting tools that are appropriate for the given analytic need. The goal of this tiered approach is to intentionally parallel the tiered approach used in regulatory contexts so that resilience assessment might be more easily and quickly integrated into existing structures and with existing policies

NETIMIS: Dynamic Simulation of Health Economics Outcomes Using Big Data

Many healthcare organizations are now making good use of electronic health record (EHR) systems to record clinical information about their patients and the details of their healthcare. Electronic data in EHRs is generated by people engaged in complex processes within complex environments, and their human input, albeit shaped by computer systems, is compromised by many human factors. These data are potentially valuable to health economists and outcomes researchers but are sufficiently large and complex enough to be considered part of the new frontier of ‘big data’. This paper describes emerging methods that draw together data mining, process modelling, activity-based costing and dynamic simulation models. Our research infrastructure includes safe links to Leeds hospital’s EHRs with 3 million secondary and tertiary care patients. We created a multidisciplinary team of health economists, clinical specialists, and data and computer scientists, and developed a dynamic simulation tool called NETIMIS (Network Tools for Intervention Modelling with Intelligent Simulation; http://www.netimis.com) suitable for visualization of both human-designed and data-mined processes which can then be used for ‘what-if’ analysis by stakeholders interested in costing, designing and evaluating healthcare interventions. We present two examples of model development to illustrate how dynamic simulation can be informed by big data from an EHR. We found the tool provided a focal point for multidisciplinary team work to help them iteratively and collaboratively ‘deep dive’ into big data

A standardized approach to treat complex aortic valve endocarditis: a case series

Background Surgical treatment of complicated aortic valve endocarditis often is challenging, even for experienced surgeons. We aim at demonstrating a standardized surgical approach by stentless bioprostheses for the treatment of aortic valve endocarditis complicated by paravalvular abscess formation. MethodsSixteen patients presenting with aortic valve endocarditis (4 native and 12 prosthetic valves) and paravalvular abscess formation at various localizations and to different extents were treated by a standardized approach using stentless bioprostheses. The procedure consisted of thorough debridement, root replacement with reimplantation of the coronary arteries and correction of accompanying pathologies (aortoventricular and aortomitral dehiscence, septum derangements, Gerbode defect, total atrioventricular conduction block, mitral and tricuspid valve involvement).ResultsAll highly complex patients included (14 males and 2 females; median age 63 years [range 31–77]) could be successfully treated with stentless bioprostheses as aortic root replacement. Radical surgical debridement of infected tissue with anatomical recontruction was feasible. Although predicted operative mortality was high (median logarithmic EuroSCORE I of 40.7 [range 12.8–68.3]), in-hospital and 30-day mortality rates were favorable (18.8 and 12.5% respectively). ConclusionsRepair of active aortic valve endocarditis complicated by paravalvular abscess formation and destruction of the left ventricular outflow tract with stentless bioprosthesis is a valuable option for both native and prosthetic valves. It presents a standardized approach with a high success rate for complete debridement, is readily available, and yields comparable clinical outcomes to the historical gold standard, repair by homografts. Additionally, use of one type of prosthesis reduces logistical issues and purchasing costs

Selective Depletion of Eosinophils or Neutrophils in Mice Impacts the Efficiency of Apoptotic Cell Clearance in the Thymus

Developing thymocytes undergo a rigorous selection process to ensure that the mature T cell population expresses a T cell receptor (TCR) repertoire that can functionally interact with major histocompatibility complexes (MHC). Over 90% of thymocytes fail this selection process and die. A small number of macrophages within the thymus are responsible for clearing the large number of dying thymocytes that must be continuously cleared. We studied the capacity of thymic macrophages to clear apoptotic cells under acute circumstances. This was done by synchronously inducing cell death in the thymus and then monitoring the clearance of apoptotic thymocytes. Interestingly, acute cell death was shown to recruit large numbers of CD11b+ cells into the thymus. In the absence of a minor CSF-1 dependent population of macrophages, the recruitment of these CD11b+ cells into the thymus was greatly reduced and the clearance of apoptotic cells was disrupted. To assess a possible role for the CD11b+ cells in the clearance of apoptotic cells, we analyzed mice deficient for eosinophils and mice with defective trafficking of neutrophils. Failure to attract either eosinophils or neutrophils to the thymus resulted in the impaired clearance of apoptotic cells. These results suggested that there is crosstalk between cells of the innate immune system that is necessary for maximizing the efficiency of apoptotic cell removal

Cross-species efficacy of enzyme replacement therapy for CLN1 disease in mice and sheep

CLN1 disease, also called infantile neuronal ceroid lipofuscinosis (NCL) or infantile Batten disease, is a fatal neurodegenerative lysosomal storage disorder resulting from mutations in the CLN1 gene encoding the soluble lysosomal enzyme palmitoyl-protein thioesterase 1 (PPT1). Therapies for CLN1 disease have proven challenging because of the aggressive disease course and the need to treat widespread areas of the brain and spinal cord. Indeed, gene therapy has proven less effective for CLN1 disease than for other similar lysosomal enzyme deficiencies. We therefore tested the efficacy of enzyme replacement therapy (ERT) by administering monthly infusions of recombinant human PPT1 (rhPPT1) to PPT1-deficient mice (Cln1(–/–)) and CLN1(R151X) sheep to assess how to potentially scale up for translation. In Cln1(–/–) mice, intracerebrovascular (i.c.v.) rhPPT1 delivery was the most effective route of administration, resulting in therapeutically relevant CNS levels of PPT1 activity. rhPPT1-treated mice had improved motor function, reduced disease-associated pathology, and diminished neuronal loss. In CLN1(R151X) sheep, i.c.v. infusions resulted in widespread rhPPT1 distribution and positive treatment effects measured by quantitative structural MRI and neuropathology. This study demonstrates the feasibility and therapeutic efficacy of i.c.v. rhPPT1 ERT. These findings represent a key step toward clinical testing of ERT in children with CLN1 disease and highlight the importance of a cross-species approach to developing a successful treatment strategy

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Background: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) “black-box” approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Methods: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients’ attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/ pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Results: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD. C Conclusions: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support