Past alcohol consumption and incident atrial fibrillation: The Atherosclerosis Risk in Communities (ARIC) Study.

BackgroundAlthough current alcohol consumption is a risk factor for incident atrial fibrillation (AF), the more clinically relevant question may be whether alcohol cessation is associated with a reduced risk.Methods and resultsWe studied participants enrolled in the Atherosclerosis Risk in Communities Study (ARIC) between 1987 and 1989 without prevalent AF. Past and current alcohol consumption were ascertained at baseline and at 3 subsequent visits. Incident AF was ascertained via study ECGs, hospital discharge ICD-9 codes, and death certificates. Of 15,222 participants, 2,886 (19.0%) were former drinkers. During a median follow-up of 19.7 years, there were 1,631 cases of incident AF, 370 occurring in former consumers. Former drinkers had a higher rate of AF compared to lifetime abstainers and current drinkers. After adjustment for potential confounders, every decade abstinent from alcohol was associated with an approximate 20% (95% CI 11-28%) lower rate of incident AF; every additional decade of past alcohol consumption was associated with a 13% (95% CI 3-25%) higher rate of AF; and every additional drink per day during former drinking was associated with a 4% (95% CI 0-8%) higher rate of AF.ConclusionsAmong former drinkers, the number of years of drinking and the amount of alcohol consumed may each confer an increased risk of AF. Given that a longer duration of abstinence was associated with a decreased risk of AF, earlier modification of alcohol use may have a greater influence on AF prevention

On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow

Abundant data is the key to successful machine learning. However, supervised learning requires annotated data that are often hard to obtain. In a classification task with limited resources, Active Learning (AL) promises to guide annotators to examples that bring the most value for a classifier. AL can be successfully combined with self-training, i.e., extending a training set with the unlabelled examples for which a classifier is the most certain. We report our experiences on using AL in a systematic manner to train an SVM classifier for Stack Overflow posts discussing performance of software components. We show that the training examples deemed as the most valuable to the classifier are also the most difficult for humans to annotate. Despite carefully evolved annotation criteria, we report low inter-rater agreement, but we also propose mitigation strategies. Finally, based on one annotator's work, we show that self-training can improve the classification accuracy. We conclude the paper by discussing implication for future text miners aspiring to use AL and self-training.Comment: Preprint of paper accepted for the Proc. of the 21st International Conference on Evaluation and Assessment in Software Engineering, 201

VISUALIZATION-BASED DECISION SUPPORT FOR OPTIMIZING SITE SELECTION:QUARRIES IN LEBANON; WHERE TO?

Traditionally the term visualization has been used to describe the process of graphically conveying or presenting end results. This paper argues that the utility of visualization approaches extends beyond these limits as it plays key role in fields of exploration, analysis and presentation, which enhances planner\u27s capabilities to solve complex planning problems. It proposes a transdisciplinary method that combines visualization approaches to site selection, integrated with spatial scenario planning, and stakeholder participation. However, it focuses on visualization as it relates to spatial data, to be applied to all the stages of problem-solving in geographical analysis, from development of initial hypotheses, through knowledge discovery, analysis, presentation and evaluation. It uses three different spatial scenarios – nature conservation, residential expansion, and sustainable development- to investigate the potentials of GIS based visualization to develop maps of a range of plausible future for possible quarrying locations in Lebano

Ectopy on a single 12‐lead ECG, incident cardiac myopathy, and death in the community

BackgroundAtrial fibrillation and heart failure are 2 of the most common diseases, yet ready means to identify individuals at risk are lacking. The 12-lead ECG is one of the most accessible tests in medicine. Our objective was to determine whether a premature atrial contraction observed on a standard 12-lead ECG would predict atrial fibrillation and mortality and whether a premature ventricular contraction would predict heart failure and mortality.Methods and resultsWe utilized the CHS (Cardiovascular Health) Study, which followed 5577 participants for a median of 12 years, as the primary cohort. The ARIC (Atherosclerosis Risk in Communities Study), the replication cohort, captured data from 15 792 participants over a median of 22 years. In the CHS, multivariable analyses revealed that a baseline 12-lead ECG premature atrial contraction predicted a 60% increased risk of atrial fibrillation (hazard ratio, 1.6; 95% CI, 1.3-2.0; P<0.001) and a premature ventricular contraction predicted a 30% increased risk of heart failure (hazard ratio, 1.3; 95% CI, 1.0-1.6; P=0.021). In the negative control analyses, neither predicted incident myocardial infarction. A premature atrial contraction was associated with a 30% increased risk of death (hazard ratio, 1.3; 95% CI, 1.1-1.5; P=0.008) and a premature ventricular contraction was associated with a 20% increased risk of death (hazard ratio, 1.2; 95% CI, 1.0-1.3; P=0.044). Similarly statistically significant results for each analysis were also observed in ARIC.ConclusionsBased on a single standard ECG, a premature atrial contraction predicted incident atrial fibrillation and death and a premature ventricular contraction predicted incident heart failure and death, suggesting that this commonly used test may predict future disease

Influence of Plant Growth Regulators on Somatic Embryogenesis Induction in Seriphidium herba-album

Seriphidium herba-album (syn. Artemisia herba-alba) is a medicinal, aromatic, greenish-silver herb. It is used widely in folk medicine for treatment of diarrhea, abdominal cramps and in the healing of external wounds. It's also used for the treatment of diabetes mellitus, neurological disorders as epilepsy, Alzheimer's disease, depression and jaundice. In this study we assessed the protocol for callus induction, maturation of somatic embryogenesis, frequency of germination and conversion into plantlets for leaf explants of Seriphidium herba-album using different concentrations of PGRs. Highest induction frequencies of embryogenic calli occurred after 35 days on MS medium supplemented with 1.5 mg L-1 2,4-D and 0.5 mg L-1 BAP. Optimum MS medium for higher frequency of matured somatic embryos was recorded using 5.0 mg L-1 BAP and 0.5 mg L-1 NAA and somatic embryos also induced young in vitro grown plantlets when cultured in the medium containing GA3 and kinetin. Hence, attempts to induce direct somatic embryogenesis have been achieved up to embryo regeneration and maturation

The role of tool geometry in process damped milling

The complex interaction between machining structural systems and the cutting process results in machining instability, so called chatter. In some milling scenarios, process damping is a useful phenomenon that can be exploited to mitigate chatter and hence improve productivity. In the present study, experiments are performed to evaluate the performance of process damped milling considering different tool geometries (edge radius, rake and relief angles and variable helix/pitch). The results clearly indicate that variable helix/pitch angles most significantly increase process damping performance. Additionally, increased cutting edge radius moderately improves process damping performance, while rake and relief angles have a smaller and closely coupled effect

Biological-Based Produced Water Treatment Using Microalgae: Challenges and Efficiency

Produced water (PW) is the most significant waste stream generated in the oil and gas industries. The generated PW has the potential to be a useful water source rather than waste. While a variety of technologies can be used for the treatment of PW for reuse, biological-based technologies are an effective and sustainable remediation method. Specifically, microalgae, which are a costeffective and sustainable process that use nutrients to eliminate organic pollutants from PW during the bioremediation process. In these treatment processes, microalgae grow in PW free of charge, eliminate pollutants, and generate clean water that can be recycled and reused. This helps to reduce CO2 levels in the atmosphere while simultaneously producing biofuels, other useful chemicals, and added-value products. As such, this review focuses on PW generation in the oil and gas industry, PW characteristics, and examines the available technologies that can be used for PW remediation, with specific attention to algal-based technologies. In addition, the various aspects of algae growth and cultivation in PW, the effect of growth conditions, water quality parameters, and the corresponding treatment performance are presented. Lastly, this review emphasizes the bioremediation of PW using algae and highlights how to harvest algae that can be processed to generate biofuels for added-value products as a sustainable approach.Scopu