The Icelandic Language Still Uses The Letters Þ And Ð, Which Used To Be In The English Alphabet Too

I read once that shortly before The Merchant Of Venice was written, Queen Elizabeths doctor (who was Jewish) tried to poison her; is that true?

Sort of.

There was a doctor accused of trying to poison Queen Elizabeth. His name was Roderigo Lopez. He was Jewish and of Spanish descent and was fairly well off. He was accused of trying to poison her and conspiring against her with Spanish officials. I believe he is the only English physician to have been executed. 

HOWEVER, we don’t know if he actually wanted to poison her. There was no attempt I believe, just accusations. He stated before he died that he “loved the Queen as much as he loved Jesus Christ” which can (and was) be interpreted in many ways. Some people think the Queen thought he was innocent because she took a long time to sign his death warrant. The character may have been the inspiration for Shylock in the Merchant of Venice, since they are both considered villains and evil because they’re Jewish but no one is 100% sure. Based on what I’ve heard, I don’t think he actually wanted to poison the queen and was probably just a target of antisemitism and maybe anti Spanish sentiment but who knows. 

Thanks for bringing this up! I’m sure people would love to read about it. I’ve put the Wikipedia article below:

 https://en.wikipedia.org/wiki/Roderigo_Lopez#Royal_physician

-Admin @thesunofyork 

Rockfleet Castle, Co. Mayo, Ireland. It’s a former home of Grace O`Malley (Gráinne Mhaol), the famous 16th century ‘Pirate Queen’.

Photo: Mikeoem (CC-BY-SA-4.0 )

Four years of failed harvests (1695, 1696, and 1698–99) resulted in severe famine and depopulation, particularly in the north of Scotland. Starvation killed 5 to 15 percent of the Scottish population, but in areas such as Aberdeenshire, death rates reached 25 percent.

Rarely has a natural disaster had such wide-ranging historical consequences as did the famine that struck Scotland in the mid-1690s. Little more than a decade later, as Scotland’s social elites despaired about their nation’s grinding poverty and profound structural weakness, the country’s Parliament finally voted away its age-old independence in favor of unification with England, previously Scotland’s bitterest and most enduring enemy.

The author of this sermon was a very colorful figure. A Scottish Presbyterian minister denounced as a rebel in 1674, he was restored next year but arrested again the following February. Later he was arrested for refusing to pray for the Prince of Wales, but again released. His matrimonial adventures were no less robust than his professional career. He was married seven times and fathered at least nine children.

David Williamson (1636- 1706). Scotland’s sin, danger, and duty: faithfully represented in a sermon preach’d at the West-Kirk, August 23d, 1696: being a solemn fast-day upon occasion of the great dearth and famine. Rare BT162.F3 W54 1720 

Why do men have Adam's apples. It's so sexy but seems so unnecessary

It basically is unnecessary lol

It’s not that only men have Adam’s apples, women do too, but it’s less prominent. An Adam’s apple is just a piece of cartilage that protects your larynx (the voicebox) directly behind it. As boys & girls go through puberty, our voicebox grows which:

1. Causes our voice to deepen 2. Pushes the cartilage further forwards

In boys, the larynx grows in size significantly more which therefore pushes forward the evident bump of cartilage we like to call an Adam’s Apple.

NASA’s new searchable media library is a treasure trove of space pics and video

Do you like space? Do you like looking at it, wishing you were there instead of here, perhaps? Well NASA just did something that will make your day. The U.S...

The U.S. space agency launched a new web-based search engine for much of its catalog of images, video and audio files, which you can browse by keyword and metadata, so that you never have to remember the dismal reality that you’re earthbound ever again.

Everyday Phenomena: The Maillard Reaction

At first glance, steak, French fries, bread, milk caramel, and soy sauce don’t have very many similarities. However, the preparation of these foods all have one thing in common: browning that occurs via the Maillard (my-YAR) reaction.

The Maillard reaction was first discovered in 1912 by Louis-Camille Maillard, and refers to a long chain of reactions that ultimately leads to browning of food. This chain typically begins with the condensation of an amine (often the amino acid lysine) with a reducing sugar (containing an aldehyde); one example of this Amadori rearrangement is shown above with lysine and glucose.

This Amadori product can react in a variety of different ways, including dehydration and deamination to produce a diverse array of molecules that give browned food a distinctive flavor; a few of these compounds are shown above. At the end of the sequence of reactions that occur during browning is a class of polymeric compounds known as melanoidins, which lend a brown color to the food.

Below about 140°C (280°F), the Maillard reaction does not proceed at an appreciable rate, although alkaline conditions (such as the lye used to make pretzels) can accelerate the process. Without this reaction, many foods we enjoy now wouldn’t be nearly as tasty!

Further Reading: Hodge, J. E., J. Agric. Food Chem. 1953, 1 (15), 928-943 (Full text)

Science Fact Friday: Tetrodotoxin, ft. a small gif because I’m avoiding my real obligations. Why does tetrodotoxin not affect its host? More studies need to be done but at least a few species possess mutated sodium ion channels. The tetrodotoxin can’t interact efficiently with the altered channels.

Another interesting tidbit: Animals with tetrodotoxin can lose their toxicity in captivity. It is suspected that the animals accumulate the toxic bacteria as a side-effect of their diet. After several years of captivity on a tetrodotoxin-bacteria-free diet, the bacterial colonies living in the animals die, residual toxin is cleared from the system, and the animal is safe to handle.

These travel booklets from throughout the UK were collected by Barbara Denison over the course of three decades, part of a larger collection consisting of dozens of volumes. Text-dense and diagram-heavy guides like these were meant to both give guidance while visiting and act as inexpensive momentos afterwards. Most of the booklets in the collection concern cathedrals, abbeys, and ruined castles that Denison visited over the course of her travels.

Categorizing Posts on Tumblr

Millions of posts are published on Tumblr everyday. Understanding the topical structure of this massive collection of data is a fundamental step to connect users with the content they love, as well as to answer important philosophical questions, such as “cats vs. dogs: who rules on social networks?”

As first step in this direction, we recently developed a post-categorization workflow that aims at associating posts with broad-interest categories, where the list of categories is defined by Tumblr’s on-boarding topics.

Methodology

Posts are heterogeneous in form (video, images, audio, text) and consists of semi-structured data (e.g. a textual post has a title and a body, but the actual textual content is un-structured). Luckily enough, our users do a great job at summarizing the content of their posts with tags. As the distribution below shows, more than 50% of the posts are published with at least one tag.

However, tags define micro-interest segments that are too fine-grained for our goal. Hence, we editorially aggregate tags into semantically coherent topics: our on-boarding categories.

We also compute a score that represents the strength of the affiliation (tag, topic), which is based on approximate string matching and semantic relationships.

Given this input, we can compute a score for each pair (post,topic) as:

where

w(f,t) is the score (tag,topic), or zero if the pair (f,t) does not belong in the dictionary W.

tag-features(p) contains features extracted from the tags associated to the post: raw tag, “normalized” tag, n-grams.

q(f,p) is a weight [0,1] that takes into account the source of the feature (f) in the post (p).

The drawback of this approach is that relies heavily on the dictionary W, which is far from being complete.

To address this issue we exploit another source of data: RelatedTags, an index that provides a list of similar tags by exploiting co-occurence patterns. For each pair (tag,topic) in W, we propagate the affiliation with the topic to its top related tags, smoothing the affiliation score w to reflect the fact these entries (tag,topic) could be noisy.

This computation is followed by filtering phase to remove entries (post,topic) with a low confidence score. Finally, the category with the highest score is associated to the post.

Evaluation

This unsupervised approach to post categorization runs daily on posts created the day before. The next step is to assess the alignment between the predicted category and the most appropriate one.

The results of an editorial evaluation show that the our framework is able to identify in most cases a relevant category, but it also highlights some limitations, such as a limited robustness to polysemy.

We are currently looking into improving the overall performances by exploiting NLP techniques for word embedding and by integrating the extraction and analysis of visual features into the processing pipeline.

Some fun with data

What is the distribution of posts published on Tumblr? Which categories drive more engagements? To analyze these and other questions we analyze the categorized posts over a period of 30 days.

Almost 7% of categorized posts belong to Fashion, with Art as runner up.

The category that drives more engagements is Television, which accounts for over 8% of the reblogs on categorized posts.

However, normalizing by the number of posts published, the category with the highest average of engagements per post isGif Art, followed by Astrology.

Last but not least, here are the stats you all have been waiting for!! Cats are winning on Tumblr… for now…