The Montgomery County law prohibiting smoking in all sections of Eating and Drinking Establishments became effective on October 9, 2003. The law is designed to protect both staff and patrons from the harmful effects of tobacco smoke.Chapter 24-9 prohibits smoking in certain public places, such as elevators; health care facilities; schools and other education facilities; County government buildings; theaters; movie theaters; rail transit stations; businesses or organizations open to the public; retail stores; banks; offices; factories; eating and drinking establishments; restrooms; enclosed auditoriums; concert halls; and lecture halls. Signs prohibiting smoking must be posted conspicuously at each entrance to a public place covered by this section.
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Usually, people don't like smoking or chewing tobacco at first. Your body is smart, and it knows when it's being poisoned. When people try smoking for the first time, they often cough a lot and feel pain or burning in their throat and lungs. This is your lungs' way of trying to protect you and tell you to keep them smoke free.
Smoking bans, or smoke-free laws, are public policies, including criminal laws and occupational safety and health regulations, that prohibit tobacco smoking in certain areas, usually in enclosed workplaces and other public spaces. Such policies are usually enacted to protect people from the negative health effects of passive smoking or second-hand smoke (SHS) exposure.
High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright 2017 Elsevier Inc. All rights reserved.
Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through www.biosoft.hacettepe.edu.tr/MLViS/. PMID:25928885 2ff7e9595c
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