Stochastic generation of annual, monthly and daily climate data: A review

International audienceThe generation of rainfall and other climate data needs a range of models depending on the time and spatial scales involved. Most of the models used previously do not take into account year to year variations in the model parameters. Long periods of wet and dry years were observed in the past but were not taken into account. Recently, Thyer and Kuczera (1999) developed a hidden state Markov model to account for the wet and dry spells explicitly in annual rainfall. This review looks firstly at traditional time series models and then at the more complex models which take account of the pseudo-cycles in the data. Monthly rainfall data have been generated successfully by using the method of fragments. The main criticism of this approach is the repetitions of the same yearly pattern when only a limited number of years of historical data are available. This deficiency has been overcome by using synthetic fragments but this brings an additional problem of generating the right number of months with zero rainfall. Disaggregation schemes are effective in obtaining monthly data but the main problem is the large number of parameters to be estimated when dealing with many sites. Several simplifications have been proposed to overcome this problem. Models for generating daily rainfall are well developed. The transition probability matrix method preserves most of the characteristics of daily, monthly and annual characteristics and is shown to be the best performing model. The two-part model has been shown by many researchers to perform well across a range of climates at the daily level but has not been tested adequately at monthly or annual levels. A shortcoming of the existing models is the consistent underestimation of the variances of the simulated monthly and annual totals. As an alternative, conditioning model parameters on monthly amounts or perturbing the model parameters with the Southern Oscillation Index (SOI) result in better agreement between the variance of the simulated and observed annual rainfall and these approaches should be investigated further. As climate data are less variable than rainfall, but are correlated among themselves and with rainfall, multisite-type models have been used successfully to generate annual data. The monthly climate data can be obtained by disaggregating these annual data. On a daily time step at a site, climate data have been generated using a multisite type model conditional on the state of the present and previous days. The generation of daily climate data at a number of sites remains a challenging problem. If daily rainfall can be modelled successfully by a censored power of normal distribution then the model can be extended easily to generate daily climate data at several sites simultaneously. Most of the early work on the impacts of climate change used historical data adjusted for the climate change. In recent studies, stochastic daily weather generation models are used to compute climate data by adjusting the parameters appropriately for the future climates assumed

A real time correlator architecture using distributed arithmetic principles

A real time correlator design based on the principles of Distributed Arithmetic (DA) is described. This design is shown to be more efficient in terms of memory requirement than the direct DA implementation, especially when the number of coefficients is large. Since the proposed architecture implements the sum of product evaluation, it can be easily extended to finite and infinite response filters. Methods to further reduce the memory requirements are also discussed. A brief comparison is made between the proposed method and different DA implementations

Continuous rainfall simulation: 1. A regionalized subdaily disaggregation approach

This paper is the first of two in the current issue that presents a framework for generating continuous (uninterrupted) rainfall sequences at both gaged and ungaged point locations. The ultimate objective is to present a methodology for stochastically generating continuous subdaily rainfall sequences at any location such that the statistics at a range of aggregation scales are preserved. This first paper presents a regionalized nonparametric daily disaggregation model in which, conditional on a daily rainfall amount and previous and next-day wetness states at the location of interest, subdaily fragments are resampled using continuous records at nearby locations. The second paper then focuses on a regionalized daily rainfall generation model.To enable the substitution of subdaily rainfall at nearby locations for subdaily rainfall at the location of interest, it is necessary to identify locations with ‘‘similar’’ daily to subdaily scaling characteristics. We use a two-sample, two-dimensional Kolmogorov-Smirnov (K-S) test to identify whether the daily to subdaily scaling relationships are statistically similar between all possible station pairs sampled from 232 gages located throughout Australia. This step is followed by a logistic regression to determine the influence of the covariates of latitude, longitude, elevation, and distance to the coast on the probability that the scaling at any two locations will be similar. The model is tested at five locations, where recorded subdaily data was available for comparison, and results indicate good model performance, particularly in preserving the probability distribution of extremes and the antecedent rainfall prior to the storm event.Seth Westra, Rajeshwar Mehrotra, Ashish Sharma and Ratnasingham Srikantha

Bronchoscopic Targeted Lung Denervation in Patients with Severe Asthma:Preliminary Findings

Treatment options for severe asthma are limited, particularly in those patients who do not meet criteria for biologicals. Targeted lung denervation (TLD) is the bronchoscopic ablation of the peribronchial vagal nerve trunks to reduce cholinergic stimulation of airway smooth muscle and submucosal glands. This report describes the experience of the first 2 asthma patients treated with TLD worldwide. The participants were 54 and 51 years of age, and both had severe asthma (GINA 5) (FEV1: 53% and 113% of predicted; AQLQ scores: 5.3 and 4.4). Both participants were treated with TLD in a single day-case procedure under general anaesthesia. Lung function, health status, and adverse event data were collected at baseline and 12 months after TLD. No treatment-related serious adverse events were reported up to 12 months. Cough symptoms improved in both participants, and 1 participant reported a marked reduction in rescue medication use at 6 months. There were no significant changes in spirometry, lung volumes, or health status. In conclusion, TLD was performed safely in both participants, but more evidence is needed to clarify safety and efficacy of TLD in severe asthma. Therefore, further investigation of the treatment in severe asthma patients would be useful

Existence of a Strong Correlation of Biomarkers and Mirna in Females With Metabolic Syndrome and Obesity in a Population of West Virginia

Objectives: Metabolic syndrome causes complications like cardiovascular disease and type 2 diabetes mellitus (T2DM). As metabolic syndrome develops, altered levels of cytokines and microRNAs (miRNA) are measurable in the circulation. We aimed to construct a panel detecting abnormal levels of cytokines and miRNAs in patients at risk for metabolic syndrome. Methods: Participants included 54 patients from a Family Medicine Clinic at Marshall University School of Medicine, in groups of: Control, Obese, and Metabolic Syndrome (MetS). Results: Serum levels of leptin, adiponectin, leptin: adiponectin ratio, IL-6, six miRNAs (320a, 197-3p, 23-3p, 221-3p, 27a-3p, and 130a-3p), were measured. Among the three groups, leptin, and leptin: adiponectin ratio, and IL-6 levels were highest in MetS, and levels in Obese were greater than Control (p\u3e0.05). Adiponectin levels were lower in Obese compared to Control, but lowest in MetS (p0.05). Conclusion: Our results support the clinical application of biomarkers in diagnosing early stage MetS, which will enable attenuation of disease progression before onset of irreversible complications. Since West Virginians are high-risk for developing MetS, our biomarker panel could reduce the disease burden on our population

pNaKtide Attenuates Steatohepatitis and Atherosclerosis by Blocking Na/K-ATPase/ROS Amplification in C57BI6 and ApoE Knockout Mice Fed a Western Diet

We have previously reported that the alpha1 subunit of sodium potassium adenosine triphosphatase (Na/K-ATPase), acts as a receptor and an amplifier for reactive oxygen species, in addition to its distinct pumping function. On this background, we speculated that blockade of Na/K-ATPase-induced ROS amplification with a specific peptide, pNaKtide, might attenuate the development of steatohepatitis. To test this hypothesis, pNaKtide was administered to a murine model of NASH: the C57Bl6 mouse fed a western diet containing high amounts of fat and fructose. The administration of pNaKtide reduced obesity as well as hepatic steatosis, inflammation and fibrosis. Of interest, we also noted marked improvement in mitochondrial fatty acid oxidation, insulin sensitivity, dyslipidemia and aortic streaking in this mouse model. To further elucidate the effects of pNaKtide on atherosclerosis, similar studies were performed in ApoE knockout mice also exposed to the western diet. In these mice, pNaKtide not only improved steatohepatitis, dyslipidemia, and insulin sensitivity, but also ameliorated significant aortic atherosclerosis. Collectively, this study demonstrates that the Na/K-ATPase/ROS amplification loop contributes significantly to the development and progression of steatohepatitis and atherosclerosis. And furthermore, this study presents a potential treatment, the pNaKtide, for the metabolic syndrome phenotype

Intra-alveolar neutrophil-derived microvesicles are associated with disease severity in COPD

Despite advances in the pathophysiology of Chronic Obstructive Pulmonary Disease (COPD), there is a distinct lack of biochemical markers to aid clinical management. Microvesicles (MVs) have been implicated in the pathophysiology of inflammatory diseases including COPD but their association to COPD disease severity remains unknown. We analysed different MV populations in plasma and bronchoalveolar lavage fluid (BALF) taken from sixty-two patients with mild to very severe COPD (51% male; mean age: 65.9 years). These patients underwent comprehensive clinical evaluation (symptom scores, lung function, exercise testing) and the capacity of MVs to be clinical markers of disease severity was assessed. We successfully identified various MV subtype populations within BALF (leukocyte, PMN (polymorphonuclear leukocyte i.e. neutrophil), monocyte, epithelial and platelet MVs) and plasma (leukocyte, PMN, monocyte and endothelial MVs), and compared each MV population to disease severity. BALF neutrophil MVs were the only population to significantly correlate with the clinical evaluation scores including FEV1, mMRC dyspnoea score, 6-minute walk test, hyperinflation and gas transfer. BALF neutrophil MVs, but not neutrophil cell numbers, also strongly correlated with BODE index. We have undertaken, for the first time, a comprehensive evaluation of MV profiles within BALF/plasma of COPD patients. We demonstrate that BALF levels of neutrophil-derived MVs are unique in correlating with a number of key functional and clinically-relevant disease severity indices. Our results show the potential of BALF neutrophil MVs for a COPD biomarker that tightly links a key pathophysiological mechanism of COPD (intra-alveolar neutrophil activation) with clinical severity/outcome

Sarcopenic obesity and risk of new onset depressive symptoms in older adults: English Longitudinal Study of Ageing

We examined the role of sarcopenic obesity as a risk factor for new-onset depressive symptoms over 6-year follow-up in a large sample of older adults

Symptomatic, biochemical and radiographic recovery in patients with Covid-19

Background: The symptoms, radiography, biochemistry and healthcare utilisation of patients with COVID-19 following discharge from hospital have not been well described. Methods: Retrospective analysis of 401 adult patients attending a clinic following an index hospital admission or emergency department attendance with COVID-19. Regression models were used to assess the association between characteristics and persistent abnormal chest radiographs or breathlessness. Results: 75.1% of patients were symptomatic at a median of 53 days post discharge and 72 days after symptom onset and chest radiographs were abnormal in 47.4%. Symptoms and radiographic abnormalities were similar in PCR-positive and PCR-negative patients. Severity of COVID-19 was significantly associated with persistent radiographic abnormalities and breathlessness. 18.5% of patients had unscheduled healthcare visits in the 30 days post discharge. Conclusions: Patients with COVID-19 experience persistent symptoms and abnormal blood biomarkers with a gradual resolution of radiological abnormalities over time. These findings can inform patients and clinicians about expected recovery times and plan services for follow-up of patients with COVID-19