The blood sugar concentration or blood glucose level is the amount of glucose (sugar) present in the blood of a human or animal. A1C chart on this page has A1C to BS conversion chart and calculator using the DCCT formula.
The hemoglobin A1C result is an important value for long-term glucose monitoring; about three months mean value of glucose level. DCCT (The Diabetes Control and Complications Trial) Formula: Below is the a1c chart to show a relation between A1C and BS equivalent. With Valentine’s Day falling on the day after Ash Wednesday this year, it came in at #13.
The only celebrity to make the list was British boy band One Direction, up substantially at #41. See the top 100 things people are giving up in 2013 for Lent on Twitter, continually updated until February 15, 2013. This list draws from about 300,000 tweets from February 19-25, 2012, and excludes retweets.
See the top 100 things people are giving up for Lent on Twitter, continually updated for the next few days. If you’re storing people’s Bible annotations (notes, bookmarks, highlights, etc.) digitally, you want to be able to retrieve them later. Compare Bible Gateway reading habits, which are much heavier on chapter-level usage, but 98% of accesses still involve a chapter or less.
Nearly all citations involve verses in the same chapter; only 1% involve verses in multiple chapters.
Around 7% of notes contained multiple independent ranges of verses—the more text you allow for an annotation, the more likely someone is to mention multiple verses.
One user can have many rows of annotations, and one annotation can have many rows of verses that it refers to.
I like using this approach over others (sequential integer or separate columns for book, chapter, and verse) because it limits the need for a lookup table. The quirkiest part of the SQL is the first part of the “where” clause, which at first glance looks backward: why is the last verse in the start_verse field and the first verse in the end_verse field?
Visually, you can think of each start_verse and end_verse pair as a line: if the line overlaps the shaded area you’re looking for, then it’s a relevant annotation. The other trick in the SQL is the sort order: you generally want to see annotations in canonical order, starting with the longest range first.
CouchDB is one of the oldest entrants in the NoSQL space and distinguishes itself by being both a key-value store and queryable using map-reduce: the usual way to access more than one document in a single query is to write Javascript to output the data you want. CouchDB has a plugin called GeoCouch that lets you query geographic data, which actually maps well to this data model.
The basic idea is to treat each start_verse,end_verse pair as a point on a two-dimensional grid. The line bisects the grid diagonally since an end_verse never precedes a start_verse: the diagonal line where start_verse = end_verse indicates the lower bound of any reference. You can even support multiple users in this scheme: just give everyone their own, independent box. Given the shape of the data, which is overwhelmingly chapter-bound (and lookups, which at least on Bible Gateway are chapter-based), you could simply repeat chapter-spanning annotations at the beginning of every chapter.

For example, in the Genesis-Revelation case, for John 3 you might create a key like [43000000.01001001,66022021] so that it sorts at the beginning of the chapter—and if you have multiple annotations with different start verses, they stay sorted properly. You have to filter out duplicates when the range you’re querying for spans multiple chapters.
You’re repeating yourself, though given how rarely a multi-chapter span (let alone a multi-book span) happens in the wild, it might not matter that much. I distributed the following handout at the presentation, showing the popularity of Bible chapters and verses cited on Twitter. As I only track in English what people are giving up, there are concentrations in English-speaking countries.
Size indicates the relative number of Twitterers in each country giving up something for Lent. These visualizations show the differences (or lack thereof) in what people are giving up among U.S. Size indicates the relative number of Twitterers in each state giving up something for Lent. The composition of each state’s categories of tweets shows mostly minor variations among states.
I created these charts mostly to explore how the new data-analysis software Tableau Public works. Snow makes the list this year, understandable given the Snowpocalypse and Snowmageddon that gripped much of the Eastern U.S. It’s not quite realtime, but the most recent tweet is rarely more than a few minutes old.
Behind the scenes, it processes tweets to try to ensure their relevance; it has about a 92% accuracy rate based on a training corpus of around 45,000 tweets.
Feel free to leave a comment here if you have a feature idea or want to make any suggestions.
Unless otherwise indicated, all content is licensed under a Creative Commons Attribution License. A voir aussi, dans le mA?me esprit, l'article que nous consacrons au travail d'Andrea Branzi. This A1C chart is based on the DCCT formula, a randomized clinical trial designed to compare intensive and conventional therapies and their relative effects on the development and progression of diabetic complications in patients with type 1. Facebook drops a few places compared to last year–has it become less-central to people’s lives? Boy band One Direction (aka #1D) is at #144, followed by Justin Bieber at #194 and Tim Tebow at #221. I don’t have access to a large repository of Bible annotations, but the Twitter and Facebook Bible citations from the Realtime Bible Search section of this website provide a good approximation of how people cite the Bible.
Of the latter, 77% cited exactly two verse ranges; the highest had 323 independent verse ranges.
Less than 0.01% of passage accesses span multiple books on Bible Gateway, which is probably a useful upper bound for this type of data. Because the start_verse and end_verse can span any range of verses, you need to make sure that you get any range that overlaps the verses you’re looking for: in other words, the start_verse is before the end of the range, and the end_verse is after the start. In other words, you start with an annotation about John 3, then to a section inside John 3, then to individual verses.

If you worry about the performance implications of the SQL join, you can always put the user_id in annotation_verses or use a view or something. Views are one-dimensional, meaning that CouchDB doesn’t even look at the second element in the key if the first one matches the query. In the worst case annotation (Genesis 1-Revelation 22), you end up with about 1200 repetitions.
If you’re willing to make multiple queries, you could create different list functions and query them in parallel: for example, you could have one for single-chapter annotations and one for multi-chapter annotations. It displays a lot of data: darker chapters are more popular, the number in the middle of each box is the most popular verse in the chapter, and sparklines in each box show the distribution of the popularity in each chapter. One of its claims to fame is that you can publish interactive visualizations to the web, a feature I didn’t take advantage of here.
You can also see a list of the most popular verses on Twitter over the past few hours (“Trending Verses”). It could evolve in several directions, but I want to see how people use it before developing it further. All Scripture quotations, unless otherwise indicated, are taken from The Holy Bible, English Standard Version.
Seven-point capillary blood-glucose profiles (pre-meal, post-meal, and bedtime) obtained in the DCCT were analyzed to define the relationship between HbA1c and BG. For example, an annotation with both a start and end verse of 19001001 matches the above query, which isn’t useful for this purpose.
The next-highest celebrity, who didn’t make the top 100, is British boy band One Direction. I would have liked to use opacity or width to indicate this disparity but couldn’t figure out how to do it. Tableau doesn’t do treemaps, so I used Many Eyes to create the treemap; the closest Tableau equivalent appears below the treemap. IPods also made the list after the Bishop of Liverpool asked people to consider praying instead of listening to them.
Et cela marche plutA?t bien : une boutique A  Nice, une autre A  Barcelone, et plusieurs dizaines de points de vente partout dans le monde. A recurring pain or discomfort in the chest that happens when some part of the heart does not receive enough blood. Converting A1C to equivalent blood-glucose level (as shown by the glucometer) can be easier interpreting the result. He recommends DCCT's formula to convert A1C to BS than the formula by ADAG recommended by ADA. And using individual columns, unlike here, does allow you to run group by queries to get easy counts. You could also introduce a separate query layer, such as elasticsearch, to sit on top of CouchDB. Goldstein, MD "Defining the Relationship Between Plasma Glucose and HbA1c, Analysis of glucose profiles and HbA1c in the Diabetes Control and Complications Trial," Diabetes Care 25:275-278, 2002.

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  1. 26.04.2014 at 23:56:18

    Really labile (up and down), he or she.

    Author: 8mk
  2. 26.04.2014 at 15:37:37

    Subjective (which makes most doctors want to shy away.

    Author: nata