Youtube News: Youtube daily report Jul 12 2018

hello and welcome to a new edition of easy remedies 24 today

We will talk about the next topic always tried to lose weight with remedies

and I never did until I knew this and I was in shock

so do not leave and stay with us until the end of this video

where we will be developing this interesting topic now we go with the

information

and our liver is under its responsibility

a huge mission to process food and extract its toxins however the

the passage of time accompanied by an unbalanced diet can make it stop

function properly as a result we will not have good nutrition what

It will make us accumulate fat and it will be difficult for us to lose weight if we do not attack

At the root this problem with enough time is very likely that its effects

are irreversible that is why in this article we want to show you how

prepare a natural drink to combat these consequences the same you

will help cleanse and purify your liver making it easier to lose weight and

feel better and with more energy we will need three units of lemon

a cup of chopped parsley five celery sticks preparation and use to start

we take a blender and we take the lemon juice celery and parsley and we liquefy it

together after everything is well mixed we add the water and go back to

liquefy a little more with this our drink is ready to take

This liquefied drink should be taken three times a day, especially before each

food the recommended amount to have quick effect is two glasses follows

this routine for three consecutive days resting the next seven days

Repeat the treatment until the expected results are achieved.

shortly after you start drinking this drink you will begin to feel better

that said organ is being purified you will also notice that you will be able to lose weight

easily and without much effort in general you will see and feel much more

healthy than before the green leafy vegetables also

clean your liver what are the watercress arugula spinach Brussels sprouts

endive among others are ideal for cleaning our liver are all rich

in chlorophyll substance that allows us to absorb toxins from the torrent

preventing the liver from reaching the results is that we will have a

much healthier and cleaner organism beets and potent carrots played for

cleanse the liver is another perfect option to cleanse our liver of

the harmful toxins that we ingest daily this juice purifies our

organism helps us lose weight and keeps us healthy and is rich in

flavonoids and beta-carotene both substances promote better

functioning of the liver system this fabulous remedy can put you in

practice anyone three times a day if you think your liver needs a

cleaning uses any of the options the result will be the same

you had an idea of all this share this information with your family and friends

in your social networks do not forget to leave us your opinion in the

comment section subscribe to our channel and like to see you

in a next edition of remedies easy 24 thank you very much

For more infomation >> 🔴✅Como Bajar de Peso y Limpiar el Higado Naturalmente es Fabuloso | Remedio para Adelgazar - Duration: 4:32.

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Ahorra energía y dinero este verano reduciendo tu cuenta de luz - Duration: 5:22.

For more infomation >> Ahorra energía y dinero este verano reduciendo tu cuenta de luz - Duration: 5:22.

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La princesa Leonor y la infanta Sofía se van de campamento a EEUU - Duration: 2:08.

For more infomation >> La princesa Leonor y la infanta Sofía se van de campamento a EEUU - Duration: 2:08.

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Wasabi, el Potente y Picante Antimicrobiano Natural. Ayuda a evitar la trombosis y el cáncer - Duration: 4:50.

For more infomation >> Wasabi, el Potente y Picante Antimicrobiano Natural. Ayuda a evitar la trombosis y el cáncer - Duration: 4:50.

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Noticias Telemundo Mediodía, 12 de julio de 2018 | Noticiero | Telemundo - Duration: 16:14.

For more infomation >> Noticias Telemundo Mediodía, 12 de julio de 2018 | Noticiero | Telemundo - Duration: 16:14.

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Playa, langostinos y gambas: las vacaciones de la princesa Mette-Marit en Cádiz - Duration: 3:32.

For more infomation >> Playa, langostinos y gambas: las vacaciones de la princesa Mette-Marit en Cádiz - Duration: 3:32.

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¿Cuándo abrirá el museo dedicado a Joan Sebastian? | Suelta La Sopa | Entretenimiento - Duration: 2:27.

For more infomation >> ¿Cuándo abrirá el museo dedicado a Joan Sebastian? | Suelta La Sopa | Entretenimiento - Duration: 2:27.

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Emotivo: el hincha ciego de Croacia que disfrutará de la final a través de los ojos de su hermano - Duration: 2:23.

For more infomation >> Emotivo: el hincha ciego de Croacia que disfrutará de la final a través de los ojos de su hermano - Duration: 2:23.

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Ask us Anything: Can the TYME Iron Pro Create Any Curl? - Duration: 0:31.

Okay so Christina says that, "This iron looks too good to be true," and she's

wondering if you can actually create any type of style. The answer is yes, you can

go all the way from super super ringlet curls all the way down to super beachy

and anything in between and you can also straighten. All you have to do is know

how to use your tool and if you can't figure it out schedule a STYLE TYME Session.

Feel like the theme here is STYLE TYMES.

It's FREE.

For more infomation >> Ask us Anything: Can the TYME Iron Pro Create Any Curl? - Duration: 0:31.

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¿Por qué a veces existe el distanciamiento entre medios hermanos? - Duration: 3:59.

For more infomation >> ¿Por qué a veces existe el distanciamiento entre medios hermanos? - Duration: 3:59.

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Here & Now Thursday July 12 2018 - Duration: 1:04:53.

For more infomation >> Here & Now Thursday July 12 2018 - Duration: 1:04:53.

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José Manuel Figueroa confiesa distanciamiento con sus hermanos - Duration: 3:07.

For more infomation >> José Manuel Figueroa confiesa distanciamiento con sus hermanos - Duration: 3:07.

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Kookaburras, King Parrots and Rainbow Lorikeets - Duration: 4:49.

Hello everyone, I'm Kerri.

I thought you might like to see some more of our Australian birds.

There are some kookaburras, king parrots, rainbow lorikeets, banana birds and mickey birds.

I hope you enjoy them.

Bye.

For more infomation >> Kookaburras, King Parrots and Rainbow Lorikeets - Duration: 4:49.

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Do you know ED MAVERICK? - Duration: 3:42.

While at the moment his name is not well recognized, that does not mean he lacks an incredible talent to make music.

His name is Eddie Saucedo, who composes under the name Ed Maverick.

Ed is the independent music proposal that takes over our week with sounds full of nostalgia, sadness and melancholy.

Paying attention to your music is allowing you to lock yourself in your particular day-to-day vision.

With the clear intention that "the race feels the feeling and starts crying with me",

Maverick is first and foremost a consummate music lover,

and from that, an independent composer and musician who has decided to break through the national scene.

"I like to sing everywhere except at home," says Ed,

whom we imagine sharing tears among those who allow themselves to pay attention.

In 2017, an EP called KASERO EP was added to SoundCloud, which consists of 3 songs

as it describes his name, they are only homemade recordings,

this EP has songs like "April 17 Feliz Pa 'Mi Compa", "Clubmaster Plata" and "Quiero".

Quoting words from Ed himself, he said that as a result of a couple of years playing in norteña and sierreña music groups as drummer and bassist, Ed Maverick emerged:

A new proposal sincere and made with all the heart.

At the beginning of 2018 he leaked a new album that he named "Mix pa llorar en tu cuarto",

of which "Karpe diem" stands out, a theme that with a melancholic guitar

he begins to tell us the story of someone who complains bitterly about how life has treated him in love;

the one who has left him once more, in what seems to be a constant for those who sing.

In addition, bitterly, the character of the song asks for something simpler than what he has lived.

Musically the song is very simple, remembering the work of musicians such as Dromedarios Mágicos and Juan Cirerol.

Just listen to a bit of the song.

We hope to hear much more from Ed Maverick in the future,

who without a problem I see inserting myself well into the taste of the current music scene.

Remember to like the video, subscribe and activate notifications to be aware of each video.

You can follow us on Instagram, which appear on the screen.

For more infomation >> Do you know ED MAVERICK? - Duration: 3:42.

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Francisca ya quiere tener hijos (al ver a Ana Patricia feliz con sus bebés) - Duration: 1:13.

For more infomation >> Francisca ya quiere tener hijos (al ver a Ana Patricia feliz con sus bebés) - Duration: 1:13.

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¡Qué bochorno, Sánchez La secuencia de imágenes que deja en el ridículo más absoluto al nuevo... - Duration: 3:47.

For more infomation >> ¡Qué bochorno, Sánchez La secuencia de imágenes que deja en el ridículo más absoluto al nuevo... - Duration: 3:47.

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Results of YMCA free swim test show a high percentage of kids can't swim - Duration: 3:09.

For more infomation >> Results of YMCA free swim test show a high percentage of kids can't swim - Duration: 3:09.

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Pixie Haircut Step by Step ✂️ (WOMEN TEXTURED CUT 2018) - Duration: 28:01.

For more infomation >> Pixie Haircut Step by Step ✂️ (WOMEN TEXTURED CUT 2018) - Duration: 28:01.

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El secreto mejor guardado sobre el PLACER FEMENINO 😱 - Duration: 3:46.

For more infomation >> El secreto mejor guardado sobre el PLACER FEMENINO 😱 - Duration: 3:46.

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Cab driver sentenced for sexually assaulting Miami U. students - Duration: 2:01.

For more infomation >> Cab driver sentenced for sexually assaulting Miami U. students - Duration: 2:01.

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Data Mining: How You're Revealing More Than You Think - Duration: 11:13.

[♪ INTRO ]

Data. The word is everywhere these days.

Every company is dying to tell you about its big data, data analytics, data privacy, data

warehouse, data lake, data data data data.

At the center of the data mania is data mining—the practice of sifting through all those piles

of information for insights.

Data mining recently made big news with the Cambridge Analytica scandal.

The political consultancy reportedly sucked up data about millions of Facebook users without

their knowledge, then used it to profile and sway voters in the US, UK, and elsewhere.

And similar techniques let companies like Amazon, Facebook, and Google work out what

we want to see or buy—sometimes with shocking accuracy.

It's a little creepy.

It's not just ads and politics, either.

Data mining allows airlines to predict who's going to miss a flight; it tells big-box stores

who's pregnant; it helps doctors spot fatal infections; and it's even enabled cell phone

companies to predict massacres in the Congo.

The power of data mining and the hype surrounding it can make it sound like a magic wand—one

that will either save your business or sink democracy.

Of course, data mining doesn't really involve any unicorn hair or phoenix tail feathers.

It's just applied statistics, searching lots of data points for patterns that humans might not spot.

Those patterns are based not on human intuition, but on whatever the data suggests, so sometimes

they can seem incredibly subtle or even alien.

But there's no more magic in data mining than there is in a weather forecast.

In fact, data mining is a lot like meteorology.

Meteorologists aim for two things: first, they want to describe patterns in the weather—to

boil down its massive complexity into a few numbers and equations.

And second, they want to predict Tuesday's weather.

That's the whole point.

Similarly, Spotify's data scientists might be interested in describing medieval rock

fans, recognizing them as a group distinct from nerdcore or freak folk fans — yes, that's a real subgenre.

Ultimately, though, what's most important to companies like Spotify is predicting what

each person wants to listen to.

The key with data mining is that it achieves description and prediction not through careful

study by experts, but by analyzing large amounts of data.

In Spotify's case, that might mean scanning for patterns in genre labels, acoustic attributes,

Internet reviews, and anything else about each track, plus the age, location, friend

group, and other scraps of information about each user.

Data mining is more about spotting patterns than explaining them.

Of course, the words "pattern" and "data" can mean just about anything.

There are no clear definitions for data mining, data science, or big data, and they're sometimes

used interchangeably with each other or with machine learning.

That's why it's so easy to slap these buzzwords onto any project for instant venture

capital karma.

That being said, a few types of techniques consistently earn the "data mining" label.

The most broadly applicable one is classification, where you try to categorize things.

For example, Target famously realized as early as 2002 that they could guess who was pregnant

and send them baby-related coupons.

That's a textbook classification problem: Target needed to assign each customer to one

of two categories: either "probably pregnant" or "probably not pregnant."

Classification typically works in several stages.

First, each example, or instance, has to be broken down into a collection of numerical

attributes, or features.

For a store like Target, an instance might be your mom 7 months before you were born.

The features would be things like "How many bottles of unscented lotion did she buy in

the last three months?

How about in the quarter before that?"

And likewise for zinc supplements, Asian pears, and every other product in the inventory.

The store would also need labels for some chunk of the data—the ground truth about

whether those customers were pregnant.

Target got those labels from baby registries and due dates customers had shared.

Once the data's all lined up, it's time for training.

That's where the system tries to tease out patterns from all the labeled examples.

Learning to classify is such a basic, common need that dozens of algorithms, the mathematical

procedures computer programs follow, have been devised for it.

Which algorithm works best depends on all kinds of factors, like how many categories

there are and how different features are connected to each other.

But many classification algorithms are similar in that they treat each feature as a drop

of evidence for one category or the other.

The features get weights indicating how strongly they boost or weaken someone's chances of

falling into the "yes" category — that they are pregnant, for example.

Those weights are what the system learns during training.

Basically, it's figuring out how informative each attribute is.

Finally, to classify instances the system hasn't seen before, it puts together all

the weighted contributions, and maybe stuffs the resulting number through a bit of mathematical

machinery to slide it up or down.

If the result is negative, that instance goes in the "no" bucket.

If it's positive—load up the crib coupons!

Each individual feature doesn't tell you much.

In fact, many turn out to be irrelevant.

But together they can be really powerful.

Target's approach worked so well that when one customer complained that his teenage daughter

was getting coupons for baby clothes, he ended up apologizing to Target.

Turned out the company knew about his daughter's pregnancy before he did!

Classification is useful any time you want to tell one group of things from another.

Insurance companies use it to guess which elderly patients will die soon so that they

can start end-of-life counseling.

Doctors use it to check whether premature babies are developing dangerous infections,

since the classifier can put together subtle disease indicators before humans would notice any signs.

I could spend all day listing uses for classification, but it's far from the only type of data mining.

One close cousin is known as regression.

And no, that doesn't mean deciding you like Limp Bizkit again.

In regression, instead of predicting a category, the goal is to predict a number.

Take Target again.

They wanted to know not just whether each customer was pregnant, but when to send each coupon

So they managed to estimate due dates, too.

That's a regression question—how many weeks until the customer gives birth.

Regression often depends on dozens or even thousands of variables—the features that

describe each example.

It finds an equation or curve to fit the data points, telling you how high you'd expect

the curve to be given any arbitrary input.

Or in this case, how far away you'd expect the customer's due date to be.

Like in classification, many regression techniques give each feature a weight, then combine the

positive and negative contributions from the weighted features to get an estimate.

And, also like classification, regression is used everywhere.

One of the better-known examples is Google Flu Trends.

In 2008, it began publishing real-time estimates of how many people had the flu based on searches

for words like "fever" and "cough."

Regression is also part of predictive policing software — programs that look at historical

data to guess how likely a crime is to occur in each area.

The third major data mining technique is clustering.

As the name suggests, the goal here is to group data points in a way that helps with the analysis

In the marketing world, clustering emerged in the 1980s—well before data mining—with

the work of a market researcher named Howard Moskowitz.

He struck gold when he realized there wasn't one best pasta sauce.

Consumers showed three distinct types of preferences—and the previously unrecognized group that craved

extra-chunky turned out to be worth millions.

Clustering is often used to analyze market segmentation like this, but to understand

how the techniques work, let's take a different example: eBay.

On ebay, you can get millions of products, from antiques to zip ties.

Even within a single category, like electronics, the selection is overwhelming.

So eBay organizes things into subcategories.

But it's a pain for humans to trawl through all the electronics, identify subcategories,

and assign every product to a subcategory.

Instead, the company can use clustering to automatically group the products.

Again, each product first has to be broken down into numerical features, like how many

times "printer" appears in the description, or who manufactured it.

The simplest clustering method is to guess how many distinct subcategories there should be.

Then you randomly lump items together into that many clusters, and keep shifting items

between groups to make each cluster tighter.

In the end, similar products end up settling into clusters together.

But we don't have to stop there!

The blue and silver versions of the same camera don't really deserve separate listings;

they're variants of the same product.

So in addition to subcategories, it would be nice to find listings to merge.

Sites like eBay can do both simultaneously with a technique called hierarchical clustering.

Rather than a single set of categories, hierarchical clustering produces a sort of taxonomic tree.

For example, it might find that cameras are much more like each other than like TVs.

But within cameras, the DSLRs and point-and-shoots each get their own subgroup, albeit slightly less distinct ones.

And within those are many different models, each with a few variants. (on image)

Companies like Cambridge Analytica use these techniques to look for groups of voters who

will respond to the same kinds of advertising, and Spotify can use them to guess who will like similar music.

The fourth staple of data mining is anomaly detection.

It's basically a special case of classification—identifying instances that are unusual or worrisome.

The IRS uses anomaly detection to spot likely tax evaders, and credit card companies use

it to flag transactions that don't fit your usual buying habits.

It also helps industries with heavy-duty equipment.

For instance, power companies and airlines can see when a generator or jet engine is

starting to vibrate differently than usual.

Some anomalies can be detected just by looking for deviations from averages.

Fancier techniques include looking for instances that don't match any cluster, or comparing

instances with the closest other examples to see if their feature values are far off.

Finally, association learning reveals which birds are of a feather.

The idea is to look through, say, millions of grocery store purchases to see what gets

bought together and when.

A classic example is the Osco drug store chain, which once found that many customers bought

beer and diapers together on Friday evenings.

Contrary to popular legend, the store never acted on this profound insight, but stores

regularly use observations like this to optimize their floor layouts and inventory.

For instance, Walmart discovered that shoppers buy lots of Pop Tarts immediately before hurricanes,

so it started to stock up.

Association learning has broader applications, too.

CellTel, an African cell phone company, realized it could spot impending massacres in the Congo

when everyone nearby started buying prepaid phone cards.

The five strategies we've covered—classification, regression, clustering, anomaly detection,

and association learning—form the backbone of data mining.

What makes them so powerful is that they offer standard mathematical tools you can use for

everything from curating Facebook feeds to optimizing store layouts.

But that ease of use can also lead people astray.

Data mining is just one step in the process of extracting knowledge from data—and it's

all too easy to whip out an algorithm without carefully selecting the data, massaging it

into the right form, and considering how to interpret the results.

Remember Google Flu Trends?

It shut down after a few years, but not because the algorithm was broken.

Search auto completion had totally thrown off the data, and engineers had given it too

much leeway to interpret seasonal words like "snow" as evidence of the flu.

Then there are the queasy social implications of sharing data in the first place, and of

letting companies form such an intimate understanding of our behavior.

In other words … the creep factor.

So as powerful as it is, the math of data mining is just the beginning.

Sometimes the hardest part is all the messy human stuff.

Thanks for watching this episode of SciShow!

If you're interested in the ways companies can use psychology to learn even more about

you from your data, you can check out our video about that over on the SciShow Psych channel.

[ ♪OUTRO ]

For more infomation >> Data Mining: How You're Revealing More Than You Think - Duration: 11:13.

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Here's What I Think About Car Engine Idle Problems in 1 Minute - Duration: 1:09.

rev up your engines, Tyler Carroll says, I got an 01 Dodge

Dakota, it Revs and tries to go even when I'm at stop sign why, okay

a car that revs by itself, there's generally two main problems, one is a

vacuum leak, if you have a vacuum leak with air sucking on the engine, it sucks

in more air and it makes the engine idle higher, so check for vacuum leaks, I have

a very good video called, finding engine vacuum leaks with a cigar, check that out

see if that fixes it, now if it's not that, it's often crudded

up throttle. the throttles open and close to let air into the engine, and if they

get dirt and carbon buildup they'll stick open and then it will make them idle

faster, I got a video for that too it's called, make your car run better with a

little spray cleaner, just type that in watch it, the spray cleaner will cost you

like 12 bucks, you can easily clean it yourself, it's not that big of a deal, so

if you never want to miss another one of my new car repair videos, remember to

ring that Bell!

For more infomation >> Here's What I Think About Car Engine Idle Problems in 1 Minute - Duration: 1:09.

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UofL could change name on Papa John's Cardinal Stadium in fallout of founder's comments - Duration: 2:01.

For more infomation >> UofL could change name on Papa John's Cardinal Stadium in fallout of founder's comments - Duration: 2:01.

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Theke Vali Gali Dance || New Haryanvi Dance Video 2018 || Chhaila Music - Duration: 5:00.

For more infomation >> Theke Vali Gali Dance || New Haryanvi Dance Video 2018 || Chhaila Music - Duration: 5:00.

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Brave Hearts~ Hrabra Srca ~Lyrics , Recital & Video By Sladja Curic - Duration: 3:25.

BRAVE HEARTS, Lyrics written and told by Sladja Curic

Someone will maybe say, That loving you is a sin,

But the only voice that I listen to Is the one that comes within.

When light and darkness collide, They make magical shadows.

Brave hearts and love Bring the earth and sky close.

Beauty, that lives in your eyes, Captured my soul

Just with the simple gaze You overcame me whole

Brave hearts are always Looking beyond the stars

When I met you there I found myself in your eyes

From that day on You are my earth and my sky

Our brave hearts are in love Beyond the stars we fly

HRABRA SRCA, stihove napisala i govori Sladja Curic

Netko ce mozda reci Da je grijeh sto te volim

Ali jedini glas koji ja slusam Je onaj sto ga salje moja dusa

Kad se svjetlost i tama sretnu One stvaraju carobne sjene

Hrabra srca i ljubav Nebo i zemlju priblize

Ljepota koja zivi u tvojim ocima Zarobila je moju dusu

I samo sa jednim pogledom Osvojio si me cijelu

Hrabra srca uvijek Gledaju iznad zvijezda

Kad sam te tamo srela Nasla sam sebe u tvojim ocima

Od tog dana Ti si moja zemlja i moje nebo

Nasa srca vole jedno drugo Iznad zvijezada letimo.

For more infomation >> Brave Hearts~ Hrabra Srca ~Lyrics , Recital & Video By Sladja Curic - Duration: 3:25.

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Jaké zvířátko se k vám hodí nejvíce? Hvězdy hodně napoví! - Duration: 6:29.

BERAN (21. 3. až 20. 4.) Už od dětství milujete všechny pejsky, takže váš výběr je naprosto jasný

Nejvíce se k vám však bude hodit plemeno labradora. Má velmi podobné rysy jako vy

Bude to parťák do nepohody, kterému dáte naprosto vše. BÝK (21. 4. až 21. 5.) Beran je nezávislá osoba, takže by si měl pořídit k sobě takového parťáka

Co takhle kočičku? Ta bude mít totálně na háku, co po ní kdo bude chtít. Budete spolu hezky vycházet

BLÍŽENCI (22. 5. až 21. 6.) Blíženci si rádi povídají a jsou rádi v centru dění

Měli by si proto pořídit takové zvířátko. Co takhle papoušek? Když se budete cítit osamoceně, bude vám ku pomoci

A ještě se u toho náramně zasmějete. RAK (22. 6. až 22. 7.) Ze zvěrokruhu je to právě Rak, který je nejcitlivější osobou

Roztomilý křeček pro vás bude proto tou nejlepší volbou. Každý den se budete rozplývat nad tím, jak chroupe svoji potravu

O tyhle malé drobečky se budete umět skvěle starat. LEV (23. 7. až 22. 8.) Jste výrazná osobnost, která chce být vždy na koni a mít navrch

Zkuste si proto pořídit koně, bude to sice náročnější investice, ale vy s tím problém mít nebudete

Rádi ukazujete, že na to máte. PANNA (23. 8. až 22. 9.) Pro Panny, které milují pořádek, se nehodí žádné zvíře, které má chlupy

Nejlepší bude, když ve vašem příbytku budete mít velké akvárko s různými druhy rybiček

Vašemu uklizenému bytu navodí krásnou atmosféru. VÁHY (23. 9. až 23. 10.) Vy než se k něčemu rozhoupete, tak to dlouho trvá

Jste pomalí tvorové a velmi si v tom libujete. Želva je proto nejlepší zvířátko pro vás

Už ji vlastníte? ŠTÍR (24. 10. až 22. 11.) Intenzivní a jedineční Štíři se nespokojí s jen tak ledajakým mazlíčkem

Zajímavou volbou pro vás bude nějaký netradiční mazlíček. Volba pravděpodobně padne na hada

STŘELEC (23. 11. až 21. 12.) Střelci jsou poctivky, takže si mohou pořídit i více náročné zvířátko

Díky vaší houževnatosti se u vás budou mít skvěle frentky nebo činčily. Navíc si budete užívat, jak jsou neskutečně hravé a vtipné

KOZOROH (22. 12. až 20. 1.) Člověk ve znamení Kozoroha je velmi pečlivý a všechno dokáže dotáhnout do konce

Z toho důvodu si může pořídit těžko vychovatelné zvíře, jelikož ho prostě zvládne vychovat

Zkuste klidně i něco exotičtějšího. Třeba opičku. VODNÁŘ (21. 1. až 20. 2.) Vodnář je velmi bláznivé znamení, takže ho neuspokojí normální věci

Potřebuje něco originálního. Málokdo má místo pejska prasátko a do tohoto extrému půjde jedině vodnář, že? RYBY (21

2. až 20. 3.) Ryby jsou občas přecitlivělé. Potřebují někoho, kdo se na ně bude umět navázat a dostane z nich negativní energii

Není tedy od věci si pořídit zakrslého králíka.

For more infomation >> Jaké zvířátko se k vám hodí nejvíce? Hvězdy hodně napoví! - Duration: 6:29.

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Real Madrid transfer news: Man Utd think Gareth Bale will join them despite Ronaldo exit - Duration: 2:24.

Real Madrid have already lost Ronaldo and fears are growing that Bale will follow him out of the Bernabeu door

Manchester United passed on the chance to bring Ronaldo back to Old Trafford, with Jose Mourinho wary of being dragged into a bidding war

Real Madrid star Bale is the one the 55-year-old would prefer to sign, with Mourinho viewing the Wales international as the missing piece in his attacking jigsaw

United have pursued the player every summer for the last five years but, after Real's Champions League final win over Liverpool, Bale finally gave them encouragement

"Obviously I need to play," he said. "That hasn't happened this season for one reason or another

"I was injured for five or six games and I've been fit ever since. "I'll have to sit down in the summer, discuss it with my agent and take it from there

" And Bleacher Report journalist Dean Jones say United are confident they will secure the player's signature

"Am told the situation with Bale is that United have had encouragement that a deal can happen," he tweeted

"Nothing to get excited about just yet - but there is some belief that they might be able to get him

" Bale was full of praise for Mourinho when United and Real met in the Spanish Super Cup last season

"Jose is a great manager, he's done everything in football," the Real Madrid star said

"He knows what he's doing and I'm sure he's building a great team for what he hopes to achieve there

" United are optimistic Bale will sign. But Express Sport understands Chelsea's Willian has been earmarked as an alternative if he chooses to stay put

For more infomation >> Real Madrid transfer news: Man Utd think Gareth Bale will join them despite Ronaldo exit - Duration: 2:24.

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ドンキーコングリターンズ3D #25『ランビの悪夢再び！？』Donkey Kong Country Returns 3D - Duration: 26:49.

For more infomation >> ドンキーコングリターンズ3D #25『ランビの悪夢再び！？』Donkey Kong Country Returns 3D - Duration: 26:49.

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Why Can't I Eat Peanut Butter? - Duration: 3:25.

[intro]

Squeaks and I were just talking about our favorite foods!

Some of my favorite foods are pizza and blueberries … and Squeaks really likes french toast!

There are lots of different kinds of foods, some of which you might like,

and some that you might not

And that's okay!

Not everyone has to eat the same thing.

But for some people, there are foods that they really can't eat.

Not because they don't like them, but because it would make them sick or hurt them.

That's because they're allergic to those foods.

Take, for example … peanut butter!

Lots of people are allergic to it.

Maybe you're one of them!

Or there might be someone in your class or your school who can't eat it.

That's because that person is allergic to peanuts—

so they're allergic to peanut butter which is made from peanuts.

If they eat those things, they might have what's called an allergic reaction.

People can have an allergic reaction to almost any kind of food, or even other things, like

dust, or bee stings, or dog fur!

But all allergies have something in common.

They happen because your body thinks the thing you're allergic to is trying to hurt you.

[Squeaks protests]

Yes, you're right, Squeaks, peanut butter isn't really trying to hurt you!

But if you're allergic to it, your body thinks it's dangerous,

and that's what matters.

So it activates a part of your body with a super important job: your immune system.

Every single day, the different parts that make up your body's immune system work together

to try and fight off the germs that can make you sick.

But in people allergic to peanut butter, the immune system makes a mistake!

It treats something that's in peanut butter the same way it would treat a germ or something

else that's trying to hurt you …

… and goes on the attack!

Now, when germs attack a part of your body, that part of your body sends a message to

the immune system — and that message is, there's something attacking us!

The immune system uses special chemicals to spread the word about the attack around the

rest of the body.

These chemicals let other parts of the immune system know that there's something wrong,

so that they're ready to help fight the germs.

If someone with a peanut allergy eats peanut butter — which again, they should never

do — their body releases those chemicals even though it's not really being attacked.

Then, there are lots of things that can happen.

Their body's immune system might try to flood the imaginary attackers out by making

lots of mucus, or snot, and making their eyes water.

That might make them cough, or their eyes water and itch, or their skin bumpy and itchy,

or give them a bellyache, or worse.

It just depends on the person.

Sometimes the effects of an allergic reaction will go away by themselves after a little

while.

But sometimes it takes a special medicine to help to make the person having the reaction

better, and until they get the medicine, the reaction is very dangerous for them.

Even a tiny little bit of peanut butter can give someone who's allergic to it an allergic

reaction.

So it's important to listen if someone says they have a peanut butter allergy—or any

other kind of food allergy!

All this talk about food makes me feel like getting a snack.

Whaddaya say, Squeaks, want to go find some French Toast?

Thanks for joining us!

If you want to keep learning and having fun with Squeaks and me all summer — and the

rest of the year, too! — hit the subscribe button, and don't forget to check us out

on the YouTube Kids app.

We'll see you next time, here at the Fort!

outro

For more infomation >> Why Can't I Eat Peanut Butter? - Duration: 3:25.