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Find and save ideas about Ted ed youtube on Pinterest. | See more ideas about Npr ted talks, Ted show and How to take percentage. and catching the long-lived bats) with the most up to date modern molecular technology to. Introduction Genetics. Cells-the building blocks of all living organisms. Introduction to cells. If you studied Junior Cert science you will know that anything which Movie clip demonstrating how an electron microscope works. 12 Feb Citation: Chary M, Park EH, McKenzie A, Sun J, Manini AF, Genes N () Signs & Symptoms of Dextromethorphan Exposure from YouTube. For example, an analysis of the effects of Salvia divinorum as seen in user-generated YouTube videos first highlighted aspects of recreational drug use that are.
Detailed data on the recreational use of drugs are difficult to obtain through traditional means, especially for substances like Dextromethorphan DXM which are available over-the-counter for medicinal purposes. In this study, we show that information provided by commenters on YouTube is useful for uncovering the toxicologic effects of DXM.
Using methods of computational linguistics, we were able to recreate many of the clinically described signs and symptoms of DXM ingestion at various doses, using information extracted from YouTube comments.
Our study shows how social networks can enhance our understanding of recreational drug effects. May 28, ; Accepted: October 23, ; Youtube Cat Dating Video Introduction To Genetics Powerpoint This is an open-access article distributed under the terms of the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The authors have declared that no competing interests exist.
This study investigated whether YouTube is a useful source of information on the recreational use of an over the counter substance whose usage is, otherwise, challenging to track. If data from social media about recreational drug use concord with clinically documented symptoms and doses, then those data could be used to explore aspects of recreational drug use, such as short-term trends, that are mostly inaccessible with current means: This study represents an application of computational linguistics to social media to provide a new data source for healthcare professionals.
Dextromethorphan DXM is marketed as a cough suppressant and is found in many over-the-counter OTC cough and cold preparations. At low dosages, it binds to the opioid receptor, which accounts for its suppression of the cough reflex .
At higher dosages it is metabolized to dextrorphan, an N-methyl-D-aspartate NMDA antagonist  that can produce dissociative hallucinations similar to phencyclidine and ketamine. In addition to dissociative effects, tachycardia, hypertension, agitation, ataxia, and check this out have also been reported at those higher dosages  — .
Recreational use of DXM is increasingly common. Calls to poison control centers concerning exposures to DXM sharply increased in and have remained elevated since then .
Recreational use is prevalent among youths and young adults in the US with approximately 1 million people aged 12—25 using DXM recreationally each year . This recreational use leads to approximately emergency department visits each year approximately half of all visits are due to recreational use from those aged 12—25 . Information about the patterns of drug use traditionally comes from surveys and reports from emergency medicine physicians or poison control centers.
For example, information around cocaine abuse is collected by in-person interviews done by the Substance Abuse and Http://meetgirls.date/x/20-and-17-year-old-dating.php Health Service Administration every 5 years and by a mail-in survey every year .
Consequently, data on the recreational use of DXM are not collected as systematically as those on the usage patterns of illicit drugs.
The nature of YouTube comments further exacerbates this creation of spurious words. Applied machine learning Google. Int J Lexicograph 3:
This makes it difficult to ascertain the public health impact of the recreational use of DXM . Consequently, the ability to analyze Youtube Cat Dating Video Introduction To Genetics Powerpoint understand substance abuse using structured data collected by physicians and poison control centers is limited.
Nevertheless, the available data describe a picture of increasing use of DXM at doses associated with dangerous side effects. However, as discussed above and unlike restricted of illicit substances, DXM has recognized uses in medicine that confound the interpretation of this number. Data specific to the recreational and recreational use of DXM comes from reports of overdoses from emergency departments and exposures called to regional poison control centers.
Because these reports likely involve more adverse or severe presentations, they likely do not represent the full spectrum of recreational use of DXM . Social media can provide a wider spectrum of information on recreational usage of DXM. Moreover, data collection from social media is cheap and can be done in real-time or close to it.
Although often thought to be limited to perfunctory discussion, social media can be used to investigate medical topics in depth. For example, an analysis of the effects of Salvia divinorum as seen in user-generated YouTube videos first highlighted aspects of recreational drug use that are difficult to click to see more by poison center calls, such as the typical amount ingested that does not result in adverse effect .
YouTube is a popular social network that lets users share, arrange and comment on videos, via website or mobile apps. It receives more than million unique visitors per month .
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Unlike social media sources such as Facebook, most users who post on YouTube do not use their real names when posting. This may limit self-censorship when discussing illicit topics. Here from YouTube have been used to better understand the effects of Salvia divinorum, and the dynamics of drug education .
The corpus derived from YouTube comments has characteristics similar to other English language corpora and so, presumably, is amenable to similar analyses. Most YouTube comments have 15—20 words Figure 1which is comparable in length to English sentences .
Words were defined as strings of characters flanked by white spaces after the preprocessing described in results. The bin size for the histogram is 9 words.
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To quantitatively analyze textual data from social media, those data need to be transformed from letters to series of numbers. One common approach for converting a piece of text, termed a documentinto numeric data is to transform the document into a vector by allowing each word to be a dimension .
Representing texts as vectors reduces quantifying the similarity between documents to computing the angle between vectors Figure 2right panel. Equation 1 illustrates how the cosine of the angle between the two vectors,quantifies the similarity between the two sentences. If the order of words is important in a document, their corresponding dimensions are not orthogonal.
One solution, in that case, is to use not words but combinations of words as axes. Dimensional reduction approaches, such as singular value decomposition, can be used to determine these composite axes.
The major flaw with this approach is that it assumes that words that occur Dating Simulation Games For Pc Download frequently are semantically related.
However, grammar and syntax often require the juxtaposition of semantically unrelated words. Removing stopwords, words that occur nonspecifically across many texts, makes word frequency a more specific indicator of semantic content, albeit still imperfect.
There are elaborations on tf-idf that attempt to disentangle semantics from word frequency . These approaches create measurements of semantic similarity that are specific to each data set. Our approach is more general because it uses a widely accepted measure of semantic similarity. WordNet is a graph representation of the English language that groups words with similar meanings together go here clusters, which roughly correspond to concepts .
Figure 3 shows part of the WordNet cluster for the concept drugs. We use the Youtube Cat Dating Video Introduction To Genetics Powerpoint between clusters in WordNet to quantify how similar two concepts are.
We calculate the similarity between two words as the complement of the ratio of the shortest path length between the two nodes representing those concepts to the diameter of the graph in Figure 3. For example, meperidine's similarity to methadone is but meperidine's similarity to furosemide is. This quantification agrees with our intuition because methadone and meperidine are analgesics whereas furosemide is a diuretic. Spreading out from the root concept of drugs are progressive refinements or hyponyms.
Counting each path as 1 and starting from the Youtube Cat Dating Video Introduction To Genetics Powerpoint node, Drugs, one may calculate the path similarity of any two concepts see text. Figure 4 shows the 40 most frequent words in the corpus.
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The most frequent word is DXM. Roboalso commonly mentioned, refers to Robitussinan over-the-counter cough syrup that is a source for DXM.
The drug with the trade name Coricidinin contrast to Robitussin, contains both DXM and chlorpheniramine, an antihistamine. Some versions also contain acetaminophen and the expectorant Youtube Cat Dating Video Introduction To Genetics Powerpoint. Because punctuation is removed prior to tabulation, Id refers to I'd. Probability density function of words from all YouTube comments analyzed in this paper. The frequency of occurrence was calculated after removing stopwords.
The data from YouTube Figure 5 correspond with prior reports that DXM is most commonly ingested in amounts that range from 0 to mg . Distinct symptoms occur with certain ranges of dosages, termed plateaus see  and summarized in Table 1. The mode of the distribution in Figure 4 occurs at mg. This falls within the most common recreational dosage range of —mg plateau 2 in Table 1. Plateau 2 features words suggestive of alcohol and marijuana use, corresponding with the clinical description of the effects of DXM at those doses.
The dropoff in dosages after mg corresponds with reports of adverse affects above mg. Death is associated with dosages above mg .
All doses were converted to milligrams. The primary goal of this study was to investigate whether signs and symptoms of drug use could be recovered from YouTube. The text presented in Table 2 strongly resembles the effects reported in . To determine whether this similarity is significant and specific to our corpus, we computed the path similarity between the corresponding rows in Tables 1 and 2 and compared that path similarity to words from an equivalently sized random sample of the most popular videos on YouTube.
This significance was assessed by comparing for each plateau the median semantic similarity between our corpus and  using a random sampling of words from YouTube as a control.
All values for path similarity are calculated relative to the signs and symptoms mentioned in . This paper demonstrates information about commonly ingested doses of DXM and their side effects can be retrieved from YouTube comments using techniques from information retrieval and natural language processing.
The benefits and reliability of utilizing natural language processing for clinical purposes have been shown in other clinical applications, for example in identifying postoperative complications in patients undergoing inpatient procedures . Analyzing social media could provide data to answer questions about the recreational use of substances that would be difficult to obtain through other means.
Moreover, the same techniques may apply to the analysis of other topics in social media and to other types of medical data in textual form. This approach may facilitate the automatic extraction of healthcare information continue reading free-text, such as social media or unstructured portions of electronic health records. This poses an opportunity to supplement existing knowledge as well as potentially generate new knowledge around substance abuse.
More broadly, the analysis of anonymous comments from social article source may be useful for the syndromic surveillance of other public health issues.
It is possible that some comments are hyperbolic or sarcastic. Most YouTube comments have 15—20 words Figure 1which is comparable in length to English sentences . YouTube no date Statistics of youtube use.
Mental health disorders are associated with particular patterns of interactions  and communication  online. This analysis may also provide a means to describe diseases by making their current textual descriptions computable.
Prior work on mining text to discover molecular markers or constellations of symptoms focussed on databases of published scientific works . In the case of substance use, where users can be reluctant to admit their identities, anonymous forums can be a good source for see more symptoms otherwise undetected.
A comparison of social media with more traditional sources of medical information highlights several limitations of this study. Traditional sources for data on recreational drug use include national surveys, reports from poison center calls and voluntary reports from physician encounters.
Unlike national surveys, the relationship between YouTube users and the general population is not known. Unlike reports from poison center calls or physician reports, the drug effects are not assessed by trained experts. Furthermore, comments from YouTube are not Youtube Cat Dating Video Introduction To Genetics Powerpoint, and may be hyperbolic or sarcastic.