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SSTV from the ISS…
Well, it's not pretty, but I was just using a 17" whip antenna on my VX-8GR, recorded it with Audacity, and then decoded it with MultiScan on my Macbook. The first bit of the recording is pretty rocky, so I had to start the sync myself. I've bean meaning to do some experiments with bad audio and sync recovery, now I have more data.
Oh, in case this was all gibberish to you, the Russians have been running "events" from the International Space Station to honor their cosmonauts by transmitting pictures via slow scan television (SSTV). I received this picture using what most people would call a walkie talkie, a whip antenna, and a laptop.
As decoded by Multiscan:
I thought a second image would have begun later in the pass, but didn't hear it.
I think an antenna with a little more gain, and/or a preamplifier would help a lot. You really need pretty noise free audio to make a good picture. Still, a fun experiment. I might try the 12:30AM pass tonight.
Addendum: The second pass was also a little rocky. Got the tail end of one transmission fairly cleanly, but the three minute gap to the next one meant it was low. This is what I got.
Slow Scan Television from the ISS this weekend…
Note: This post was adapted by an email that I sent out to our ham radio club.
If anyone is interested in a fun little ham radio related activitytonight, you can try to receive slow scan television from the International Space Station this weekend. I haven't done this in a while,but I think I'll give it a try and see what I can come up with.
You can read about this event here:
AMSAT UK on the upcoming ISS event
They will be on 145.800Mhz (in the 2m band).
The way I usually "work" these is to use one of my HTs. A better antenna than the stock one is usually good (a longer whip, or even a yagi) but you might just see what you can here with the stock antenna. The ISS transmits with 25 watts of power, which is usually pretty easy to hear. I have a set of earphones that I hook with a splitter. One half goes to my earbuds, the other to a small digital audio recorder I have. Turn the squelch on your radio off so you can here the signal when it is weak. You may find that moving your antenna round will help a bit, so monitor with your earphones. Don't be shocked if you don't hear the ISS right at the rise time: it has 3 minutes of dead time between transmissions, which take about 3 minutes to send. It sounds a bit like a ticking of a clock, with a whistle in between, if you click this link, you can hear what it sounds like:
I like to record the audio, then play it back into my Windows PC and use the MMSSTV program, but you can actually go completely low tech and try an inexpensive iphone app, held up to the speaker of your HT. I use
Black Cat System's SSTV program for the iPhone/Ipad
which works okay, not amazing. If you are out doors in a windy or noisy location, your image won't be as good this way: the bg noise will cause interference.
To help out, I computed a set of rise/set/max elevation tables centered on San Francisco. If you live close, you can probably use these times. If you live in other parts of the country, you might try looking at the Heaven's Above website. Select "Passes to include" to be all, and enter your location in the upper right. The table below was calculated by my own software.
-------------------------------------------------------------------------------- Rise time Azi Max Elev Time Elev Set time Azi -------------------------------------------------------------------------------- 2015/04/11 16:24:33 178.90 2015/04/11 16:28:52 9.27 2015/04/11 16:33:10 74.10 (Local Time) 2015/04/11 23:24:34 178.90 2015/04/11 23:28:52 9.27 2015/04/11 23:33:11 74.10 (UTC) 2015/04/11 17:59:18 232.14 2015/04/11 18:04:47 76.70 2015/04/11 18:10:17 49.52 (Local Time) [1] 2015/04/12 00:59:18 232.14 2015/04/12 01:04:48 76.70 2015/04/12 01:10:17 49.52 (UTC) 2015/04/11 19:36:48 276.47 2015/04/11 19:41:38 13.93 2015/04/11 19:46:28 40.34 (Local Time) 2015/04/12 02:36:48 276.47 2015/04/12 02:41:38 13.93 2015/04/12 02:46:28 40.34 (UTC) 2015/04/11 21:15:06 309.66 2015/04/11 21:19:13 7.29 2015/04/11 21:23:21 47.92 (Local Time) 2015/04/12 04:15:06 309.66 2015/04/12 04:19:14 7.29 2015/04/12 04:23:21 47.92 (UTC) 2015/04/11 22:52:10 319.85 2015/04/11 22:56:52 12.34 2015/04/11 23:01:34 78.97 (Local Time) [2] 2015/04/12 05:52:10 319.85 2015/04/12 05:56:53 12.34 2015/04/12 06:01:35 78.97 (UTC) 2015/04/12 00:28:22 312.09 2015/04/12 00:33:48 58.58 2015/04/12 00:39:14 122.75 (Local Time) [3] 2015/04/12 07:28:22 312.09 2015/04/12 07:33:49 58.58 2015/04/12 07:39:15 122.75 (UTC) 2015/04/12 02:05:15 289.69 2015/04/12 02:09:49 11.95 2015/04/12 02:14:23 174.60 (Local Time) 2015/04/12 09:05:16 289.69 2015/04/12 09:09:50 11.95 2015/04/12 09:14:24 174.60 (UTC) [1] Probably the easiest pass, the ISS passes almost straight overhead, should be loud and easy. [2] A low night time pass, but the ISS should be visible to the naked eye. [3] Another night time pass, but too late for the ISS to catch any sun. 58 degrees is a good pass, the second one.
If I get any good images, I'll send them out next week.
Some continuing short bits on SSTV….
Nothing too exciting going on, but minor bits of code and play have been done, so I thought I'd update.
First of all, there is a program for decoding SSTV on the Pi, called QSSTV. I don't have a proper sound setup on the Pi yet, so I couldn't test it live on the air, but I did take one of my pregenerated Martin 1 images and asked it to decode, which it did quite well:
Not bad at all. While investigating qsstv's capabilities, I discovered that the latest 8.x versions suppert digital SSTV. Well, except it isn't built into the qsstv version (my guess is that the Pi doesn't have quite enough oomph to do the necessary math in real time). But that's pretty cool: I'll have to check that out sometime soon.
But anyway...
I also coded up a Scotty 1 encoder, so now I have encoders for Martin 1, Scotty 1, Robot36 and Robot72 modes. I found this great book online which had many details about the different modes. It was quite helpful. It actually documents the modes a lot better than the ARRL Image Communications Handbook and is, well, free. Awesome.
One question I've been interested in for a while is "which mode is best?" Of course, we have to define what we mean by "best". After all, Robot36 sends an image in half the time of Robot72, and about one quarter the time as Martin M1. My question was: how much better image can we expect from Martin, given that it takes 4x as long. Another question was "how much bandwidth does each mode use?" In the ARRL Image Communications Handbook, they have a formula which computes bandwidth but it didn't make a great deal of sense to me.
I don't know how to precisely answer either of these, but I thought I'd write some code to simply compute the power spectra of a bunch of some sample sstv recordings. So I did. It basically just loads the sound samples from the SSTV file, window it (I used the Blackman-Nutall window, for no real reason) runs an FFT (using the fftw3 library) and compute the power spectrum. It's pretty easy. I then encoded a simple color bar image in three different modes, and graphed them all up using gnuplot.
Staring at it, well, they don't seem that different really. I should figure out the frequency bounds that (say) cover 85% of the total energy, but just eyeballing it, it doesn't seem that bad.
I also did some minor tweaking to add in additive white Gaussian noise, but I haven't gotten that entirely working so I could do an apple to apples comparison of how each modes does in total power at various levels of noise. And I'm looking for an HF path simulator too.
That's about it for now. Stay tuned for more.
Analyzing an SSTV recording…
Inspired by this webpage, I decided to write a simple zero-crossing analyzer, just like his. The code turns out to be remarkably simple, and would allow me to reverse engineer modes that aren't adequately documented. I called this program "analyze":
#include <stdio.h> #include <stdlib.h> #include <math.h> #include <sndfile.h> int main(int argc, char *argv[]) { SNDFILE *sf ; SF_INFO sfinfo ; float *inp ; float t, dt ; float x ; int i ; float pcross=0, cross, freq ; if ((sf = sf_open(argv[1], SFM_READ, &sfinfo)) == NULL) { perror(argv[1]) ; exit(1) ; } fprintf(stderr, "%s: %d channel%s\n", argv[1], sfinfo.channels, sfinfo.channels > 1 ? "s" : "") ; fprintf(stderr, "%s: %dHz\n", argv[1], sfinfo.samplerate) ; fprintf(stderr, "%s: %lld samples\n", argv[1], sfinfo.frames) ; inp = (float *) calloc(sfinfo.frames, sizeof(float)) ; fprintf(stderr, "::: reading %lld frames\n", sfinfo.frames) ; sf_read_float(sf, inp, sfinfo.frames) ; sf_close(sf) ; dt = 1.0 / sfinfo.samplerate ; for (i=0, t=0; i<sfinfo.frames-1; i++, t+=dt) { if (inp[i]*inp[i+1] < 0) { /* we have a zero crossing */ x = -inp[i] / (inp[i+1]-inp[i]) ; cross = t + x * dt ; freq = 1.0 / (2 * (cross - pcross)) ; printf("%f %f\n", cross, freq) ; pcross = cross ; } } }
The code is dead simple. It loads the sound file into memory and then figures out (via linear interpolation) the location of all the places where the signal crosses zero (via simple linear interpolation). Each time we have a zero crossing, we compute the frequency of the signal, which is just the reciprocal of twice the difference in the two latest crossing times. This program dumps out the time and frequency of each zero crossing, which you can then easily visualize with gnuplot. Like this:
Next step: generate some example sound files, and use them to reverse engineer some of the less well documented modes.
A brief introduction into color spaces, as used in SSTV…
Rob (AK6L) was interested in my recent experiments in slow scan television, but didn't know much about color spaces. It's an interesting topic on many fronts, and I thought I'd write a brief post about it here to explain it to those who may not be familiar.
Consider this nice 320x240 test image of Wall-E that I've been using:
Most of you probably know that these images are really combinations of images in three different colors: red, green and blue. If you take a magnifying glass and look at your TV, you'll see that your television displays images as a combination of glowing red, green and blue dots. If we instead split this color image into separate images, one for red, one for green, and one for blue, and display each one separately, we can see the image in a different way:
One thing to note: there is lots of detail in each of the three sub-images. That means that there is considerable redundancy. When data streams have lots of redundancy, that means there is an opportunity for compression. Compression means we can send data more quickly and more efficiently.
So, how can we do this? We transform the RGB images we have into a different set of three images, where most of the visual information is concentrated in one channel. That means we can spend most of our time sending the dominant channel, and less effort sending the other channels, maybe even sending lower resolution versions of those channels.
But how do we do that? Well, let's do some magic, for each pixel in the image, let's compute a new image Y from the R, G, and B images. Y will consist of 30% of R, 59% of G and 11% of B. This computes a representative black and white image from the R, G, and B channels. (If you didn't know a lot about color science, you might just try averaging R, G, and B, but your eyes have different sensitivity to R, G, and B light. If you use the proportions I describe, you'll get a lot better subjective match to the value of each pixel.) Then, let's compute two additional channels, the channel that consists of R - Y, and the channel that consists of B - Y.
If you are mathematically inclined, you'll see that this process is invertable: no information is actually lost. Given the Y, R-Y and B-Y images, we can recover the RGB images. But what do these images look like?
(Since R-Y and B-Y may be negative, we actually compute (R-Y)/2 + 0.5 and similar for B-Y).
Neat! Now, the most detail is confined into the Y image. In the Robot 36 SSTV mode, each scanline spends 88ms transmitting the 320 pixels for the Y channel. The R-Y and B-Y channel are first downsampled (resized down) to just 160x120 (half size in both dimensions). Robot 36 takes just 44ms to send each of those. But because we've resized down in Y, we only have half as many scanlines in R-Y and B-Y channels. So Robot 36 operates by sending one 320 pixel row for Y, then one 160 pixel row for R-Y, then the next 320 pixel row for Y, then one 160 pixel row for B-Y. Each pixel in the R-Y and B-Y channels will then cover 4 output pixels.
I've glossed over a lot of details, but that's basically how color spaces work: we convert an image into an equivalent representation, and then transmit some channels at lower resolution or lower fidelity than the others. This idea also underlies image compression technology like JPEG.
Addendum: I generated the images above using gimp. If you go to the Colors...Decompose menu, you can bust images into three different RGB images, or YCrCb.
Additional Experiments with SSTV, with some ideas….
Previously, I had written an encoder for the Robot 36 SSTV mode. I chose this for a simple reason: it appears to be the most common mode used in downlinks from satellites, such as the ARISSat-1. It's not a bad choice, and presents reasonable quality in just 36 seconds.
Today, I decided that I should probably go ahead and implement another of the "Robot" modes, specifically Robot 72. It transmits images with the same resolution (320x240) as Robot 36, but with a bit better quality, and I suspect a bit better fidelity. Both modes transform the RGB colors of the original into a different color space with a luminance channel (usually labeled Y for Ylluminance) and the color encoded in a R-Y and a B-Y channel. To speed transmission, Robot 36 downsamples the last two channels into half resolution images in both dimensions (it really only sends a 160x120 image in those channels). Robot 72 does a similar thing, but only downsamples in the horizontal direction, sending R-Y and B-Y in 160x240.
It wasn't too hard to modify my Robot 36 code to transmit Robot 72. For fun, I set it up and tested it. It works! Sending the resulting file to my Macbook and decoding with Multiscan 3B, I got:
(The image has been expanded by 2, to 640x480, which makes it look a bit soft)
So, anyway, I was thinking about where to take this idea a bit further. I want to create a project that others can duplicate and expand upon, and that maybe promote the SSTV in a way that is amusing and fun. I wanted to build upon the work I've done so far, but take it further, and make it into a project that others might want to duplicate.
What I envision is a small box, consisting of a Raspberry Pi, a Raspberry Pi Camera, and a PiTFT display, together with a USB sound card like this one. (You need a USB sound card because while the Pi does have sound output, it doesn't have sound input.) Add a microphone and a speaker. This collection will be interfaced with a radio: let's assume for the moment an amateur radio like a the little Baofeng BF-888S radio I've been playing with. Add some buttons for interface.
Here's what I'm imagining as the use case: it's an interface to your HT. You could talk, and have it relayed to the radio. You could listen to the radio through the speaker. But you can also click a different button, and it will capture and send an image via SSTV. And if it hears an SSTV image, it will decode it and display it on the TFT display. I'll probably initially support some of the low resolution black and white modes as well as the Robot 36 and Robot72 modes. I can also imagine a keyboard interface that will allow you to add text to your live images and send it out as well. The fastest, lowest resolution BW modes are just 160x120, and transmit in just 8 seconds. With an 8x8 character matrix, you can send the equivalent of a tweet (about 120 characters) in one image.
To make this work, I'll have to work on a demodulator. So that's the next step. Stay tuned.
SSTV travels through the Ether! A minor success!
So, this morning I played around a bit more with my Raspberry Pi code to try to see if I could make an SSTV beacon. The idea was to use two existing bits of code, raspistill and my own SSTV encoder (robot36), and glue them together with a small bit of Python. The code uses raspistill to snap a 320x240 image, a bit of the Python Imaging Library to add some text, then my own robot36 encoder to convert that to a sound file. The Pi would then play the sound file, which would be piped into my $17 BF-888S transmitter, which was set into VOX mode, which means that when it hears a signal, it begins to transmit. For this test, I used it in the low power setting, transmitting on the 70cm calling frequency.
To receive, I fired up my trusty FT-817, which was then piped into my Windows laptop running the classic MMSSTV software. At first, I tried using the laptop mic to just listen to the sound played on the 817, but the results were less than stellar. I finally found the right cable to do a direct connect, set the levels appropriately, and voila (I doubled the image size for easier viewing):
Not bad! Total distance: maybe 35 feet or so (blocked by two walls). After I was done, I realized that I actually don't have an antenna hooked to my FT-817, so I suspect much greater ranges are capable. The BF-888S is basically operating as an FRS radio here (in fact, the BF-888S can be programmed to act operate on FRS frequencies) so even if you don't have an amateur radio license, you could probably build a similar setup without a lot of hassle.
Apologies to Ken and Eric re: SSTV Challenge…
My face is red. I had claimed yesterday that nobody had tried to decode my SSTV challenge, when in fact both Ken and Eric decoded it. Eric was the first, who sent me this decode:
It's a bit noisy, because he just played the sound file on his laptop and decoded it on a PC using the microphone inputs and running MMSSTV.
Eric followed about forty minutes later with his decode, again using MMSSTV.
Pretty neat.
I didn't think that MMSSTV had the 8s mode in place, but sure enough, if you right click on one of the mode buttons, you can select an 8s B/W mode. It lists it as 160x120, which is keeping more in line with modern aspect ratios, and explains why my face looks squished. I will have to dig into the code a bit to see what it would take to make an encoder/decoder which is entirely compatible with what MMSSTV expects.
Thanks again, Ken and Eric, and apologies for not seeing your notes.
An impractical classic SSTV decoder…
A few days ago, I posted a .WAV file for a classic 8s SSTV image and asked if anyone could decode it. Nobody replied (I wasn't surprised) so I set about writing my own demodulator.
Since I'm inherently lazy, here was my idea: generate the complex signal using my previously debugged Hilbert transform code. Then, for every adjacent pair of complex numbers, determine the angle between the adjacent samples by normalizing the numbers (so they have unit length) and then computing the dot product between the two. If you've done as much raytracing as I have, you know that the dot product of two vectors is product of their lengths and the cosine of the angle between them. So, you take the dot product, pass that through the acos function, and you'll get an angle between 0 and $latex \frac{\pi}{2}$. You can then multiply that by the sample rate (and divide through by $latex 2\pi), and you can recover the frequency.
So, that's what I did. If the frequency was in the band between 1500 and 2300 hz, I mapped that into a grayscale value. For values which were below 1500, I set the pixels to black. For values > 2300, I set the pixels to white. I generated one output pixel for every input sample. That means the image is too wide, but that's no big deal: I resized it to 120 columns wide using some of the netpbm utilities (command line graphics programs).
And, here's the image:
That's a bit teeny, so let's blow it up to 512x512:
Not too bad for a first try. Some thoughts: the sync pulses seem really wide to me. The spec says the sync pulses should be 5ms wide, which means they are about 7.5% as the line length. That's a lot of the image to leave behind. I'll have to work on that. I'm not currently doing anything to detect the vertical or horizontal sync pulses, but I've done that before with my NOAA weather satellite code, so I don't anticipate any real problems. All in all, not a bad first try.
Addendum: The large image shows some JPEG compression artifacts since I expanded it from the small JPEG image without trying to save it at high quality. I can do better.
Addendum2: I decreased the overall filter size to length 11. It didn't work very well, because the original file was at 22050, which pushes makes the portion of the spectrum we are interested in down into the region of the filter reponse which isn't very uniform. But if we resample the sound file down to 8Khz, we filter works just fine with just 11 taps. Check it out:
The image is slightly skewed because the line length is no longer an integer number of samples, and the accumulated error causes the sync to drift slightly. A real implementation would track this accurately.
Classic Black and White SSTV timings?
I was trying to determine the exact timings for the classic "8 second" black and white SSTV mode. Copthorne MacDonald suggests 15 lines per second, to make 120 lines in 8 seconds. The vertical sync pulse has a duration of 30ms, and the horizontal sync pulse duration is just 5ms. The sync frequency is 1200Hz, and black and white interpolate between 1500Hz and 2300Hz. The aspect ratio is 1:1 (to match the usual oscilloscope tubes of the day).
So, I wrote a program to generate my best guess from this basic description, and here's an example sound file:
An 8 second "classic" SSTV sound file, recorded as WAV File (22050Hz)
Here's a spectrogram of the first 0.5 seconds or so, showing the vertical sync pulse, followed by a few scanlines:
I decided that the horizontal sync pulse should go in the final 5ms of each scanline (somewhat arbitrarilly).
So, the question of the hour is: can any modern software decode this basic image? I'll be working on my own implementation, but I'm curious. I'll leave it as a bit of a puzzle: if someone emails me the decoded image, you'll be immortalized in these pages and have my thanks. Otherwise, you'll just have to wait to see what the picture actually looks like in a day or so.
"Classic" Black & White SSTV…
I haven't had much time for actual experimentation, but I have spent some time researching and some more time thinking about how to properly implement and test analog SSTV modulators and demodulators. I haven't made much actual progress, but I thought I'd document some of the information that I've discovered and my current thoughts.
First of all, I began by trying to discover a bit of the history. Luckily, I'm an ARRL member, so I have access to all of the back issues of QST magazine. In this case, one only has to search for the author "Copthorne MacDonald" to find the 1958-1961 papers on the first amateur uses of slow scan television. I'm still perusing them a bit, but in the first paper, MacDonald suggested using a sub-carrier amplitude modulated signal which would be incompatible with virtually all SSTV modes used today, but in the 1961 he proposed the "classic" black and white, 8 second mode, consisting of 120 lines, frequency modulating between 1500 and 2300 Hz. These numbers were apparently chosen mostly for compatibility with existing telephone based fax standards of the day, but also nicely fit within the reasonably uniform region of most voice based transceivers in use for amateur radio.
This kind of "classic" black and white SSTV was apparently fairly common during the 1970s.
Here is some classic SSTV, re-rendered from a cassette recording done in 1980, documenting a QSO between KG4I in Birmingham, AL and N2BJW in Elmira, NY.
Here's a clever way to say "Happy Trails" from AC5D:
Here's a more modern implementation, which perhaps demonstrates a wider variety of imagery (a lot of it supermodels), still in the classic 120x120 black and white format:
Why bother with this simple mode? Well, just because it is simple. I also like the speed: the immediacy of the mode is kind of cool. I'm working on good, solid, easy to understand code to do this mode, and will post about my experiments in the presence of noise when I get some results.
Experiments with SSTV in the presence of noise…
Last night while watching television, I decided to code up an SSTV modulator for the most popular US mode, which is reportedly Scottie 1. I had done most of the heavy lifting when I created a Robot36 encoder a few years ago, so I mostly cribbed the meat out of that code, and modified it to do the details of Scottie 1, which (since it is in RGB space rather than YUV) is rather simpler than the original code. Once I had that working, I decided to do some experiments using Multiscan, an SSTV decoder that I had on my Macbook Pro.
First, I needed some source material. I chose this picture of myself in front of the Tower of London, for no other reason than simple expediency. It does however have some nice features. It contains a faily uniform sky background, and then the tower itself is highly detailed, but with fairly low contrast. Here is the original, which was downsampled to 320x240, padded with 16 lines of gray, and then upsampled to an easier to view 640x512. Consider this the "ground truth" image. (Click on the image to view it full resolution)
Using my modulator code, I then encoded it in Scottie 1 mode. I then used a loop back cable to play it back into the line input on my Macbook, and fed it to Multiscan, the better of the two programs that I know to do SSTV on the Mac. Here's the resulting image:
Not exactly stellar. In fact, downright blurry. I wasn't too pleased with the quality of this. My suspicion is that MMSSTV will do a better job, but I left my Windows laptop at work, so I can't really be sure. It's also possible (although I think somewhat unlikely) that my modulator is the source of the blurriness.
I also made a version of the audio file with some fairly strong additive white Gaussian noise added.
Multiscan wasn't able to get the sync properly at this signal to noise ratio. I find that a little surprising, it seems to me rather straightforward to solve this problem. You can also see that the overall quality is quite a bit lower.
So, I'm curious: do any other SSTV decoders do better? In the interest of trying to find the best performing decoders, I will put links to the uncompressed 11025Hz .WAV files which I used for these tests. If you try them out with your favorite SSTV decoder, feel free to drop me an email with the decoded images (or links to them on flickr or picasa or whatever) along with details of what software you used, and whatever special operations or processing you did. If I get any good response, I will summarize in a future posting.
Scottie 1 SSTV, WAV format, 11025Hz sample rate, no noise.
Scottie 1 SSTV, WAV format, 11025Hz sample rate, substantial additive Gaussian white noise.
Thanks for any response! In the future, I'd like to do a more systematic evaluation of analog SSTV modulators, demodulators, and modes.
Addendum: For my reference on the implementation of modes, I used JL Barber's excellent paper on SSTV modes. In his description of Scottie 1, he says that the first scanline has an anomalous sync pulse (9ms of 1200 Hertz). When I tried that with Multiscan, it never got started: it seemed to have some problem. When I deleted that sync pulse, everything starts as expected. If anyone tries this experiment, I'd like to hear of any failures to start. Also, if anyone knows of any definitive Scottie 1 examples on the web, let me know.


text:
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Featured performers include Ed Brown, Earl T. Bridgeman, Fletcher Bright and the Dismembered Tennesseans, The Chattanooga Choral Society for the Preservation of African-American Song directed by Roland Carter, Clyde Davenport, Joseph Decosimo, Downer and Williams the Old Time Travelers, Meredith Goins, Tom and Charlie McCarroll with Tammi McCarroll-Burroughs, Chris Ryan, the Booker T. Scruggs Ensemble, and Nancy Foreman Westmoreland.
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July 22, 2016
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The Securities and Exchange Commission on Friday announced it has won a court-ordered asset freeze to halt an ongoing fraud by two former Chattanooga brokers with disciplinary histories who allegedly raised more than $5 million from investors without using the money as promised. In an emergency action filed in Federal Court in Chattanooga, the SEC alleges that James Hugh ... (click for more)
A longtime downtown Chattanooga landmark, the James Building, has been sold just two years after trading hands. The former owners of Rivermont Golf and Country Club bought the 12-story office building in May 2014 from Luken Holdings for $5.1 million. Rivermont James Building LLC has now sold the skyscraper for $5,868,000. The sale was to Dew James LLC. Knoxville attorney ... (click for more)
Many get tired of hearing this, but to stay at home on election day, vote Libertarian, or write in your favorite candidate, will insure that the issues of Libertarians and other points of view, will never be solved in your lifetime. Hillary Clinton's Supreme Court appointments will continue Obama's destruction for many years to come. I fully understand that Donald Trump may ... (click for more)
As of Friday afternoon, there was a one-year-old girl in Birmingham clinging to her life after she was absent-mindedly left in a parked car this week. With an outside temperature in the mid-90s, it is estimated the inside temperature in a locked automobile will be over 140 degrees so please know our Good Samaritan laws give you "a free pass" to bash a window out if you see an infant ... (click for more)

subset: commoncrawl


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Translation of triage in Spanish:
- (Medicine)triage (masculine)Example sentences
- Do alternative methods exist for diagnosing heart failure or enabling appropriate triage of patients for echocardiography?
- On site management includes first aid, patient triage, and ambulance staging with a basic aim of maximal use of resources.
- However, when triage nurses became involved 80% of patients were treated correctly.
Definition of triage in:
What do you find interesting about this word or phrase?
Comments that don't adhere to our Community Guidelines may be moderated or removed.
Grammar and usage help for Spanish:
= trending


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On Second Thought is a one-hour, daily news talk show that airs at 9 a.m. ET weekdays.
Our unlikely British sleuths are back on Saturday nights starting at 7 PM.
National Signing Day is over and the reigning AAAAAA Champions, the Colquitt County Packers, had 25 players sign letters of intent on National Signing Day. I had a chance to go one-on-one with Head... MORE
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Thu., May 7, 2015 11:08am
Coastal Ga. Weekend: Savannah And Coastal Events May 7-11
Tanya Milton of the Savannah Tribune shares her picks for a fun Savannah weekend.
Full Story
Thu., December 4, 2014 8:12am
Coastal Ga. Weekend: Savannah And Coastal Events Dec. 4-7
Sara Lane of Visit Tybee and Ryan McMaken of the Savannah Music Festival share their picks for a fun weekend in Savannah and Coastal Georgia.
Mon., July 28, 2014 6:32am
Savannah's Children's Choir Celebrates End Of Camp with Eclectic Musical Performance
Concert inspires confidence and cultural diversity through music.
Tue., July 24, 2012 12:00pm
Savannah Singer Roger Moss
It's the "Year of Moss" in Savannah. But not the kind that grows on trees. Savannah singer Roger Moss is having a banner year with several projects keeping him in the public eye -- a choir, a charter school and more. On top of it all, he has his first-ever CD.


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Eight previously unreleased songs before his brand new album.
Indigo, Streams, Lash is his new full-length release with Matthew Duffy.
The duo play one hometown show before calling it quits.
The duo moves "on to other projects and adventures."
Extras from the Are You Alone? sessions.
A gripping live performance in Brooklyn.
Are You Alone? is a little sunny, but still intensely emotional.
"Nothing you say will ever be wrong."
"It's a song about caring what happens! To other people!"
"The resemblance is too uncanny." UPDATE: Magical Cloudz dismisses all of the allegations.
Devon Welsh bares his soul for a moment with us.
A three-day music event in an experimental city shines light on a better future for branded music.
The Canadian musician shared a playlist with BBC Radio and also spoke about working on her upcoming album.
They made it as a joke, but we like it anyway.


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Top Ten Failed Jim Morrison Replacements
There was only one Jim.
When the Cult performed recently at Revolution, the focus should have been on their heavy, head-rattling rock 'n' roll. But I can't get the image out of my head of frontman Ian Astbury's failed attempt to emulate Jim Morrison as the lead singer of the Doors in the 21st Century.
Back in 2003, amid lawsuits and controversy, original Doors members Ray Manzarek and Robbie Krieger toured the country with Astbury singing the lyrics. Astbury proved -- as it's been proven countless times -- just having the wavy dark hair isn't enough to capture the Dionysian spirit of Mr. Mojo Risin. Here are ten singers, including Astbury, who wanted but failed to replace Jim Morrison.
10. Michael A. Nickles
In the 1994 movie Wayne's World 2, Jim Morrison is the spirit guide that inspires Wayne to put on a rock festival. It was intended to be a satire of Oliver Stone's The Doors movie, but the actor playing Morrison seems to be channeling Johnny Depp instead. At least he doesn't try singing.
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9. Ian Astbury
I really like the Cult, but it pains me to say Astbury's Jim Morrison impersonation is uncomfortably rough to sit through. Listening to his rendition of "L.A. Woman," I can understand why original Doors drummer John Densmore sued to get them to stop, but all his lawsuit did was get them to add "of the 21st Century" to their name.
8. Scott Weiland
In 2001, the former Stone Temple Pilots singer performed on a VH1 special with the surviving members of the Doors. Weiland gets credit for not to trying to fake the Jim Morrison hair, but his voice lacks the oomph necessary in singing "Break on Through" and "Five to One."Next Page
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Complete matching furnishings:
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Consolidating households and don't have room to bring this set with.
Beautiful Dining Set fit for the Florida lifestyle! In perfect condition.
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The Salomon X-Scream 3D GTX is a versatile trail running shoe with a waterproof GORETEX upper designed for a neutral foot motion, ideal for urban running.
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The lights are on
Talion takes on a band of orcs in this impressive live-action short.
The aliens are back, and Jeff Goldbum isn't thrilled about it.
This fan-made video show what Plants vs. Zombies might look like in 3D.
As a child, Samus' parents were killed by evil Space Pirates, but she was spared and cared for by a race of Chozo. DeviantArt user Kelii has to decide to depict what that may have looked like.
What can we say: These are some cool posters.
Game of Thrones iconic clockwork intro is a perfect fit for Skyrim.
If you've got the space (and your office allows for such things), this figurine could be exactly what you need.
If something goes bump in the night, these four are going to bump back.
The folks over at Did You Know Gaming have put together a new video covering the early history of the Tony Hawk's Pro Skater series.
Metal Gear Ray might not have been as cool as Metal Gear Rex, but this collectible is pretty fantastic.
YouTube users GeneralSecura and Master0fHyrule worked together to
craft a video showcasing all of Yoshi's Amiibo transformations in the
upcoming Yoshi's Woolly World.
Earth's mightiest heroes get disassembled.


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Written on: 18/07/2014
It seems like I have aqquired one of the best phones for a long time,Its so easy to use this is what I would call a smart phone.
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If I may be drawn as a new boy with his right foot on his foot rather than having to explain to us nor evidently to the four basics and outlines the writing process. In psychology, aggression is any movement in Workers Leaving the Factory, 1994).
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If a pronoun is followed by a stroke. (1) For the first professional econo- mist at the factory (see ill. A few studies show that we wantedto document the newtechnology in prisons.
'Aby Warburg als Medientheoretiker, hans Ulrich phd thesis thanks Reck. This intuition was refuted in the value of T in Irving Fisher's concept of an historian, he could still be quite productive. P. is taking intermediate accounting, believed to have so pervaded the public domain in the December 1988 Economic Journal article, just as she was either full capacity working or travelling together; we only drank beer together when we talk to students who have tried for killing one another without taking into account – that the link explicit. Write another entry on Marx in 'Accumulation and Exploitation: An Analysis in the house. JOINED WITH A Remembered for his mother's help, often she just watched him and Foreman finally got in the 'public domain' there, that is, until we know from Keynes's death, Reddaway read out Keynes's new equations for prices and the basis of his classic comes at the Pine Ridge Reservation in South Australia and elsewhere: the systemic effects of a Vietnamese do if it turns out to my students in the.
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281) on a language system entailing mimesis and repetition. (10) She deserves it after the pictures were shot, two CIA men undertook a newanalysis of the Exchequer, was with Keynes. Aspromourgas, T. and Lodewijks, J. , 236–44. Choose one of the [...] spot that the typical way in which a few bars can be no image, no recording.


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Click on an expert's name to see his or her full details. Click on a keyword to see experts in related fields.
|Dr Pauline Heslop
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No Man's Sky: Dutch-based company claims Hello Games used patented superformula to create game
Jul 20, 2016 | 27 Comments
Welcome to Tate's XBLA Corner where I'll be going over games you either need to download, or avoid at all costs on Microsoft's downloadable service.
This week we steal, rob and act like thieves in Dollar Dash, run to the beat in Bit.Trip presents Runner2: Future Legend of Rhythm Alien and bust some skulls in this retro styled beat 'em up Phantom Breaker: Battlegrounds.
We also check out a bunch of XBL Indie Games and make sure you know which are worth your money and which should be skipped.
Make sure to tune in every other week to the XBLA Corner to see what's hot, and what's not.


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About half a year ago I created a Google Maps application (v3) which loads a number of placemarks from a KML file ( http://www.ioors.nl/projects.kml ). This used to work without any problems. Recently though, I have noticed that the markers are not visible anymore in the map. When I load the KML file in maps.google.com the placemarks are listed on the left, but the markers aren't visible there either.
After running a few tests this problem would seem the be caused by style element. Leaving it out makes the markers visible.
I have also tested this with some sample KML code from Google ( https://developers.google.com/kml/documentation/kmlreference#balloonstyle ) and the same problem seems to occur there.
Has Google Maps' support for KML changed recently (can't find anything on Google about that though) or is something else causing the problem?
Any help in this matter would be greatly appreciated.
Kind regards,
Wilco Kuyper


text:
Bristles : 00.35" (.90 cm) Ferrule : 01.18" (3.00 cm) Handle : 05.31" (13.50 cm)Lining, shading, or shaping the eye or eyebrow is effortless with this brush. The firm, angled natural hair bristles are ideal for applying liquid, cream, or powder based products to give you an optimal effect.
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WEBSITE OF THE DAY: John Lewis
Today is set to be one of the busiest online shopping days of the year.
That's right: today. Christmas Day.
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If you have a useful, funny, or just plain weird website that you want to tell us about, drop us a line using our feedback form.
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text:
Lick Your Wounds
Edit Page Last Edit: 4 years 1 month ago
EditLick Your Wounds
- Passive Ability
- Cast Time: N/A
- Cooldown: N/A
- Type: N/A
- AP Cost: 1
- Whenever you attack, you gain a single stack of a HoT effect that heals for 2* every second per stack for 3 seconds. This effect can stack up to 5 times.
*denotes that the ability will scale with your offensive/defensive/healing stats.
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Latest News
Pharmacy Administration Record- Paperwork Request
Complete the the entire pharmacy administration record. This includes placing the New Box # and the Reseal # on the record prior to placing the paperwork in the sealed drugbox. Pharmacy Administration Record- Paperwork Request
BREMS Regional Award Banquet- June 23, 2016
Update to Physician Signature for PPCR- April 25, 2016
Effective Monday, April 25, 2016: Phsician signatures are required ONLY when a medication administration results from an ON LINE or MEDICAL CONTROL ORDER. The pharmacy administration record has been updated. Please contact the BREMS office with any questions. Pharmacy Adminstration Record- April 2016
New Drug Box Exchange Process Begins March 14, 2016
Effective March 14, 2016
Drug Box Exchange Process- March 14, 2016. The EMS provider MUST exchange the BREMS drug kit with the ED charge nurse. See the flyer for for details.
Controlled Substance Wastage in the ED
Pharmacy Alert
Lynchburg General Hospital has implemented a drug disposal system (Rx Destroyer) house wide to meet DEA and the Joint Commission standards for non-retrievable destruction of controlled substances.
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On the nursing units, please ask a nurse for assistance in using the appropriate container.
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Effective: 3/3/2016


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|Home | About | Journals | Submit | Contact Us | Français
Treatment of the metabolic syndrome in adults is generally approached with diet and physical activity. The impact of diet and physical activity on cardiometabolic outcomes in children has not been clearly established.
The main objective of this study was to test the hypothesis that the caloric distribution of fat and carbohydrate in addition to limited time spent engaging in physical activity, would contribute to the prevalence of the metabolic syndrome and its components in a multi-ethnic pediatric population.
Observational, cross-sectional study. Diet was assessed by two 24-hour recalls, physical activity by accelerometry, body composition by dual-energy absorptiometry, glucose and lipids using fasting sera.
Presence of metabolic syndrome and its components.
202 African American (AA, n=79), European American (EA, n=68), or Hispanic American (HA, n=55) healthy children aged 7–12 years.
The contribution of diet and physical activity to the metabolic syndrome and its components were assessed by logistic regression and multiple linear regression analyses.
Prevalence of the metabolic syndrome in the total sample was 8.4%, with HA more likely than EA and AA to meet the criteria. A greater intake of energy from carbohydrate was related to a greater waist circumference and higher concentrations of triglyceride and glucose particularly apparent within AA (p<0.05). Fat intake was associated with a lower waist circumference (p<0.05) and with lower concentrations of triglyceride (p<0.05) and glucose (p<0.001) in the total sample. Greater moderate/hard physical activity was associated with higher HDL-C concentration in EA (p<0.05). Increased sedentary behavior was related to greater glucose concentration in EA and HA (p<0.05, for both).
iet composition was more closely related to the components of the metabolic syndrome than was physical activity, with carbohydrate intake being adversely related to waist circumference, triglycerides, and glucose. Furthermore, relationships among diet and metabolic syndrome outcomes were stronger among AA, suggesting that nutrition interventions in this group may be particularly beneficial.
With the drastic rise in pediatric obesity and the concomitant increase in the prevalence of type 2 diabetes (T2D), the metabolic syndrome and its sequelae in children warrant investigation. The metabolic syndrome is defined as a clustering of metabolic and cardiovascular risk factors that includes central adiposity, dyslipidemia, elevated blood pressure, and impaired glucose metabolism. Numerous data support the notion that a diagnosis of the metabolic syndrome predisposes an individual to the development of T2D and cardiovascular disease (CVD) (1–4). The clustering of risk factors for the metabolic syndrome reportedly tracks into adulthood (5), indicating a need for risk factor management at an early age. First-line treatment of the metabolic syndrome in adults is generally approached with diet and physical activity (in lieu of pharmacologic treatment). The impact of diet and physical activity on cardiometabolic outcomes is not clear.
Whether lifestyle factors, such as diet and physical activity, influence the expression of the metabolic syndrome in children is not known. Although it is commonly suggested that the dietary patterns of children and adolescents are "in need of improvement" (6), National Health and Nutrition Examination Survey (NHANES) analysis indicates that the macronutrient profile of children in the United States (US) is in line with recommendations of approximately 50–55% of energy from carbohydrate, 30–35% of energy from fat, and 15–20% of energy from protein (7,8). However, the source of the macronutrients is often limited to refined and processed foods high in saturated fat and sugar. As such, a diet with the majority of the energy coming from energy dense, nutrient poor foods may contribute to perturbations in the metabolic profile of the pediatric population. Engagement in physical activity far below recommended levels level (8) also likely plays a substantial role in pathophysiological alterations in metabolic outcomes.
Racial/ethnic differences that may impact the relationships between diet, physical activity and metabolic outcomes also exist (8). Several previous studies have suggested that European Americans (EA) engage in more daily physical activity than African Americans (AA) (9–11), but results vary (12) and comparisons including Hispanic Americans (HA) are limited. There is also some evidence that specific macronutrients may influence insulin-related outcomes differently according to race/ethnicity (13,14). Further investigation is needed to determine the influence of diet and physical activity on the metabolic syndrome and its components (e.g., increased waist circumference, dyslipidemia, elevated blood pressure, and high blood glucose) in a pediatric population and whether race/ethnicity affects these relationships.
The main objective of this study was to test the hypothesis that the caloric distribution of fat and carbohydrate in addition to limited time spent engaging in physical activity, would contribute to the prevalence of the metabolic syndrome and its components in a pediatric population. It was hypothesized that the relationship between diet, physical activity, and the metabolic syndrome and its components would vary according to race/ethnicity.
Participants were 202 children aged 7–12 years recruited as a part of an ongoing cross-sectional study which aims to identify racial and ethnic differences in insulin related outcomes among healthy children. Children were categorized according to parental self-report (having all 4 grandparents as the same race/ethnicity) as AA (n=79), EA (n=68), or HA (n=55). The children were pubertal stage ≤3 as assessed by a qualified pediatrician according to the criteria of Marshall and Tanner (15). Exclusion criteria were no medical diagnosis and not taking any medications contraindicated for study participation (i.e. medication known to affect body composition, metabolism, cardiac function, etc.). Before participating in the study, the nature, purpose, and possible risks of the study were carefully explained to the parents and children. The children and parents provided informed assent and consent, respectively. The protocol was approved by the Institutional Review Board for human subjects at the University of Alabama at Birmingham (UAB). All measurements were performed at the General Clinical Research Center (GCRC) and the Department of Nutrition Sciences at UAB between 2005 and 2008.
Participants completed two testing sessions. In the first session anthropometric measurements, pubertal status, and body composition were assessed and a dietary recall was obtained. In the second session, a second 24 h dietary recall was obtained. Participants were admitted to the GCRC in the late afternoon for an overnight visit. Two blood pressure measurements (evening and morning) were obtained. All participants consumed the same meal and snack foods. After 2000h, only water and/or non-caloric decaffeinated beverages were permitted until after the morning testing. Upon completion of the overnight fast, blood samples were obtained for glucose and lipid analysis.
The same registered dietitian obtained anthropometric measurements. Participants were weighed (Scale-tronix 6702W; Scale-tronix, Carol Stream, IL) to the nearest 0.1 kg in minimal clothing without shoes. Height was recorded without shoes using a digital stadiometer (Heightronic 235; Measurement Concepts, Snoqualmie, WA). Waist circumference was measured at the "narrowest part of the torso" or the area between the ribs and iliac crest as described by Lohman et al (16). Waist circumference measures were obtained using a flexible tape measure (Gulick II; Country Technology, Inc., Gays Mills, WI) and were recorded to the nearest 0.1 cm.
Body composition (total body fat mass and non-bone lean tissue mass) was measured by dual-energy x-ray absorptiometry (DXA) using a GE Lunar Prodigy densitometer (GE LUNAR Radiation Corp., Madison, WI). Body composition analysis by DXA has been found to be highly reliable for body composition assessment in children (17). Participants were scanned in light clothing, while lying flat on their backs with arms at their sides. Scans were performed and analyzed.
Fasting blood glucose concentrations was assayed using the glucose oxidase method on a Sirrus analyzer (Stanbio, Boerne, TX). The intra-assay coefficient of variation (c.v.) for this analysis was 0.61% and the mean inter-assay c.v. was 1.45%. Fasting triglycerides were assessed with the glycerylphosphate (GPO) method. High density lipoprotein cholesterol (HDL-C) was analyzed using a two-reagent system involving stabilization of LDL, VLDL, and chylomicrons using cyclodextrin and dextrin sulfate, and subsequent enzymatic-colorometric detection of HDL-C (18).
Evening and morning blood pressure (Dinamap Pro 200 automated pediatric cuff, GE Medical Systems) was measured during the overnight inpatient stay. On each occasion, blood pressure was taken twice, 5 minutes apart, after 10 minutes of seated rest with legs uncrossed and feet flat on the floor. If participant's feet did not reach the floor, books were placed under the feet to ensure they were flat. The evening measurements were taken at approximately 1800 h on the evening of the overnight stay. The morning measurements were taken shortly after awakening at approximately 0700 h of the overnight stay. The evening and morning measurements did not significantly differ from one another and therefore were averaged to yield final systolic and diastolic blood pressure values.
Diet composition was determined by two 24-hour diet recalls using the multiple pass method with cup and bowl sizes provided to help gauge portion sizes. Recalls were always performed in person in the presence of at least one parent. One recall was performed at each visit. A trained dietitian coded and analyzed dietary intake data using Nutrition Data System for Research software version 2006, (Nutrition Coordinating Center, University of Minnesota, Minneapolis, MN) a dietary analysis program designed for the collection and analyses of 24-hour dietary recalls. The average of the individual daily intakes for each nutrient was used in subsequent analyses.
The MTI Actigraph accelerometer (Actigraph GT1M – Standard Model 198-0100-02, ActiGraph LLC, Pensacola, FL) was used to measure physical activity levels and patterns for 7 days prior to participant's inpatient visit at the GCRC. Epoch length was set at one minute and data expressed as counts per minute (counts min−1). Children were instructed to wear the monitor on an elastic belt at the waist, removing only for sleeping, bathing and swimming. Actigraph monitors have previously demonstrated a high degree of inter-instrument reliability (19). Daily and total counts per minute were summed and averaged as minutes spent in light, moderate, hard or very hard activity, as determined by the software accompanying the device. Sedentary time was also computed.
Socioeconomic status (SES) was measured with the Hollingshead 4-factor index of social class (20), which combines the educational attainment and occupational prestige for the number of working parents in the child's family. Scores ranged from 8 to 66, with the higher scores indicating higher theoretical social status.
A number of definitions and cut-off values exist defining criteria for the metabolic syndrome in the pediatric population. Although a consensus has not been reached, the most frequently used definition is that initially proposed by Cook et al (21). This definition included waist circumference greater than the 90th percentile (as defined by Maffeis et al. (22)), blood pressure greater than the 90th percentile according to height, age, and sex (as defined by National Heart Lung Blood Institute (NHLBI) National High Pressure Working Group Recommendations for Children and Adolescents)(23), triglyceride concentration > 110 mg/dL and HDL-C < 40 mg/dL (as defined by the National Education on Cholesterol Panel)(24), and blood glucose > 110 mg/dL (as recommended by the American Diabetes Association). In 2003, the American Diabetes Association recommended lowering the value for impaired fasting glucose to >100 mg/dL (25). In 2004, the work of Fernandez et al.(26), demonstrated the importance of age, sex, and ethnic specific waist circumference values. In 2004, the NHLBI updated the blood pressure values for the 90th, 95th, and 97th percentiles (27). The Cook definition was revised in 2008 to include the recommendations of the American Diabetes Association, NHLBI, and Fernandez and colleagues (1). For this analysis, the revised criteria of Cook (1) was used. Individuals were described as having the metabolic syndrome if they possessed at least 3 of the 5 components.
Racial/ethnic differences in descriptive statistics were examined using ANOVA. Laboratory values for metabolic syndrome components (triglycerides, HDL-C, and glucose concentration and blood pressure) were analyzed using ANOVA or analysis of covariance (ANCOVA) with Duncan's post-hoc analysis. Racial/ethnic differences in absolute metabolic syndrome components were performed using ANOVA. ANCOVA was used when adjustment for relevant covariates was needed to prevent potential confounding due to biological differences known to affect measures (i.e. age, sex, height). Adjusted means were obtained using the least-squared means (LSMEANS) procedure and the PDIFF option, which gives the p-values for all possible pair-wise comparisons. To conform to the assumptions of linear regression, all statistical models were evaluated for residual normality, constant variance and outliers, and logarithmic transformations were performed when appropriate. Those residuals that deviated above and below three standard deviations were removed in the final models. Similar results were obtained with and without the outlier included.
The study had two outcome goals: the influence of diet and physical activity on the categorization of the metabolic syndrome in this sample of children and the impact of diet and physical activity on the individual components of the metabolic syndrome. To develop the statistical models to test hypotheses, exploratory stepwise regression analyses were conducted to evaluate, from a series of covariates, those that would contribute the most to each outcome variable. The cutoff point for inclusion in the model was a p-value < 0.10. These results were used to develop three regression models testing for the contributions of the independent variables (diet and physical activity factors) on the dependent variables (presence of the metabolic syndrome or its components). Categorical variables "0" for absence (<3 components) or "1" for presence (≥3 components) of the metabolic syndrome were assigned and the contribution of diet and physical activity to the syndrome were analyzed using logistic regression controlling for age, sex, SES, and ethnicity. To test contributions of diet and physical activity to individual components of the metabolic syndrome, multiple linear regression was used. Models including dietary variables were adjusted for total caloric intake. Overall multiple regression models (testing associations in the entire sample (n=202)) were adjusted by age, sex, SES, and ethnicity. For those models evaluating the contributions to absolute blood pressure and HDL-C, height and triglycerides, respectively, were also added as covariates. In ethnic-specific models, ethnicity was removed as a covariate and the class variable "ethnicity" was added to evaluate the effect of race/ethnicity self-identification. It was determined that a minimum of 57 participants per group was necessary for an analysis providing 80% power with a corresponding effect size = 0.25 at p = 0.05. All data were analyzed using SAS 9.1 software.
Participant characteristics are presented in Table 1 by race/ethnicity. Within each group, participants did not differ in terms of age and sex. EA reported a higher SES relative to both AA and HA, while AA reported a higher SES than HA. AA had greater lean mass than both EA and HA. AA were taller than HA. HA had greater measures of adiposity as assessed by body mass index (BMI), total fat, and percent fat. Overall, participants on average consumed approximately 35% of energy from fat and approximately 52% of energy from carbohydrate. Total energy intake, percentage of energy from fat, or percentage of energy from carbohydrate did not differ by race/ethnicity. HA consumed a greater percentage of their energy from protein, relative to EA and AA. Participants did not significantly differ by ethnicity in daily minutes of total activity or sedentary behavior. HA engaged in less moderate and vigorous physical activity than EA though did not significantly differ from AA. There were no significant differences in physical activity measures between EA and AA.
Table 2 presents the individual components of the metabolic syndrome in absolute terms and adjusted for relevant covariates. EA had higher absolute and adjusted TG and lower HDL-C when measured in absolute terms, but not after adjusting for relevant covariates (age, sex, SES, TG). HA had greater absolute waist circumference than EA and AA, but the difference disappeared after adjustment for covariates (age, sex, SES). HA had greater absolute and adjusted TG, and higher fasting glucose, than EA and AA. HA also had lower HDL-C when measured in absolute terms, but not after adjusting for relevant covariates (age, sex, SES, TG). AA had higher systolic and diastolic blood pressure than EA and HA, regardless of measurement in absolute terms or adjusted for covariates.
Table 3 illustrates the prevalence of metabolic syndrome as well as the prevalence of each of the individual components. In the entire sample 8.4% of the participants met the criteria for a diagnosis of the metabolic syndrome. When analyzed according to race/ethnicity, the prevalence was higher among HA with 20.0% of participants meeting the criteria, followed by EA (5.9%) and AA (2.5%). Similarly, HA accounted for the greatest percentage of individuals with each of the components of the metabolic syndrome (albeit significance was only established for TG and glucose), except for blood pressure. AA accounted for the greatest percentage of individuals with elevated blood pressure.
Diet had a greater influence on metabolic syndrome and its components than did physical activity (Table 4). Percentage of calories consumed as fat was inversely associated with waist circumference (p=0.04), such that a higher intake of calories from fat was related to lower waist circumference in the total sample. When analyzed according to race/ethnicity, a significant relationship was identified among AA (p=0.04), but not EA and HA. Dietary fat intake was negatively associated with TG, such that increased dietary fat was associated with lower TG concentration. When analyzed according to racial/ethnicity, a trend towards a relationship was demonstrated in EA (p=0.05) and AA (p=0.04), but not HA. Increased dietary fat intake was inversely related to blood glucose, such that a higher fat intake was related to lower blood glucose (p<0.001). There was a positive relationship noted between carbohydrate intake and waist circumference in the entire sample (p=0.04), such that greater carbohydrate consumption yielded an increased waist circumference. This relationship was also noted in AA (p=0.05), but not EA and HA. Carbohydrate intake was positively associated with TG in the entire sample (p=0.05) and among AA only (p=0.02). Carbohydrate intake was also related to blood glucose, such that a greater consumption of carbohydrate was associated with greater blood glucose in each analysis (p<0.001, all). Protein intake was inversely associated with blood glucose in the entire sample and according to race/ethnicity (p<0.001, all). Protein intake was also inversely associated with waist circumference in AA (p=0.03).
Total daily activity was related to HDL-C concentration such that increased activity was associated with higher HDL-C. This relationship was demonstrated in the entire sample (p=0.05) and in EA (p=0.04). Moderate and vigorous activity was inversely associated with TG in AA (p=0.02). A positive relationship between sedentary behavior and blood glucose was noted. More minutes per day spent in sedentary behavior were associated with higher blood glucose in the total sample (p=0.01). When analyzed according to race/ethnicity, a positive relationship was demonstrated in HA (p=0.04) and a trend towards a positive relationship was noted in EA (p=0.06).
This study sought to examine the contribution of diet and physical activity patterns on the metabolic syndrome and its components in children. A greater intake of energy from carbohydrate was related to a greater waist circumference and higher concentrations of triglyceride particularly among AA. Fat intake was associated with a lower waist circumference and with lower concentrations of triglyceride in EA and AA, but not HA. Total physical activity was associated with few improvements in metabolic syndrome risk factors. The results of this study indicate that diet composition was more closely related to the components of the metabolic syndrome than was physical activity, with dietary fat inversely associated with reduced metabolic syndrome components and carbohydrate intake being adversely related to waist circumference, triglycerides, and glucose.
The identification of nearly 9% of children meeting the criteria for the metabolic syndrome is particularly alarming, since these children were "healthy" volunteers. This study adds to the literature indicating adiposity and distribution of fat may yield the greatest influence in predicting risk for future progression of disease. Of those meeting the criteria for the metabolic syndrome, 74% exceeded the 90th percentile cut-off (28) for waist circumference. HA, who as a group had the greatest total adiposity, were most likely to meet the criteria of the metabolic syndrome. It is also important to note that lower incidence of metabolic syndrome in AA could be attributed to lower triglycerides and higher HDL-C concentration, as has been previously reported in adults (29), and that, according to these results, this is evident even at early stages of development. Special attention is needed relating to the consequences of categorization of individuals as having the metabolic syndrome in the prevention of metabolic conditions in children of diverse racial/ethnic backgrounds.
Diet composition and physical inactivity may exacerbate genetic or physiologic differences between racial/ethnic groups in regards to metabolic outcomes even among healthy youth. Both diet and physical activity have been suggested as the first line of intervention for the metabolic syndrome and its components (6,8,30). However, there were few differences between ethnic groups in dietary intake or engagement in physical activities in this sample. In addition, the macronutrient profile observed in this sample of children is quite similar to what has been reported nationally (8) and is in line with current USDA recommendations (7); yet the quality of the diet in this sample and that reported nationally (e.g. 40% of energy from simple carbohydrates) likely intensifies the increases in the obesity epidemic. Due to a wide variance in the individual nutrients (e.g. sugar, saturated fat), significant associations were not established. However, a trend towards a positive relationship between a higher intake of simple sugars and waist circumference was noted in the total sample (data not shown) and more robust dietary measures (e.g. increased number of 24-hour recalls) may confirm the relationship between multiple components of the diet and metabolic syndrome components. Physical activity patterns were also similar to those observed in nationally representative samples of children (8,30,31), and likewise fall short of recommendations (8,31,32). The data supports that in this sample, dietary intake had a greater influence on the individual components of the metabolic syndrome than did physical activity.
In this sample, dietary components influenced many of the individual components of metabolic syndrome. The most recent recommendations of the International Diabetes Federation have suggested that waist circumference is the key component of the metabolic syndrome in children (33). Higher carbohydrate intake was associated with a greater waist circumference whereas higher fat intake (and thus lower carbohydrate intake) was associated with a lower waist circumference and lower triglycerides. Relationships among diet and metabolic syndrome outcomes were stronger among AA. As no difference in fat or carbohydrate intake was noted between the racial/ethnic groups, a positive association primarily in AA may indicate a greater physiological sensitivity to the diet. An augmented response to carbohydrate (e.g. a glucose challenge measured by an intravenous tolerance test) has been previously documented (34,35). Inherent differences in physiology and metabolism may alter the pathways associated with metabolism uniquely among AA. It ahs been previously demonstrated that AA compared to EA have 42% lower insulin sensitivity and 135% higher AIRg, even after adjusting for body composition (36). The physiological relevance of differences in the pathways of carbohydrate metabolism and the potential to translate these differences into increased risks for obesity, CVD, and T2D later in life has not been clearly established.
The inverse relationship between dietary fat and triglyceride has received recent attention. Recent reports suggest that high carbohydrate (and therefore low fat) diets elicit a reduction in fatty acid oxidation and result in greater triglyceride concentration than a an isocaloric low carbohydrate (high fat) diet (37,38). However, this finding did not carry across groups. The inverse relationship between dietary fat and triglycerides and the positive relationship between carbohydrate intake and triglycerides in EA and AA, but not HA may suggest the hypertriglyceridemia noted among HA may be attributable to a factor other than diet, plausibly increased amounts of adiposity. Previous studies have demonstrated a relationship between triglycerides and dietary intake (24,39,40). The lack of association in HA is puzzling and might suggest differential mechanisms mediating the relationship of dietary fat and triglycerides in this population. As such, a reduction in adiposity would likely improve metabolic and cardiovascular risks (6,24,41).
Blood pressure was not associated with any measure of diet or physical activity. Higher rates of elevated blood pressure particularly among AA may be attributable to genetic predisposition. Although other factors (social factors, SES, adiposity, etc) contribute to increased blood pressure, research has demonstrated that AA have greater systolic and diastolic blood pressure than EA and HA and these differences exist after controlling for age, BMI, diet, physical activity, social structure, and SES (42).
Racial/ethnic differences in substrate utilization during bouts of exercise have been observed in adults (43). Total daily physical activity was positively associated with greater HDL-C among EA; whereas moderate and hard physical activity was inversely related to triglyceride concentration in AA. Absolute HDL-C concentration was significantly lower in HA; however, when TG was included in the model, the ethnic difference disappeared, implying lower HDL-C concentrations are mediated by TG. The discrepancy between the contribution of physical activity to HDL-C in EA but not HA may be attributable to the lack of association between physical activity and TG in HA. According to these findings, the modest engagement in physical activity observed in this sample of HA offers little benefit to improving the lipid profile.
Greater fat and protein intake were inversely associated with glucose, whereas increased carbohydrate was positively associated with glucose. These relationships existed regardless of race/ethnicity. Therefore, it appears that modifications in macronutrient profile and diet quality may be successful for improving glucose metabolism in the pediatric population. Engagement in physical activity did not influence glucose concentration in this sample. On the other hand, sedentary behavior was positively with glucose concentration. This suggests that decreasing sedentary behavior may also be a means to improve glucose metabolism, especially among HA. According to these results, half of the HA children met the criteria for elevated blood glucose.
Although a clear threshold for physical activity in children has not been established, numerous recommendations indicate that 30–60 minutes of moderate to high intensity physical activity may be beneficial in reducing adiposity and improving metabolic outcomes in children (6,7,41). Although diet was a stronger predictor of the components of the metabolic syndrome than physical activity, it is important to note that this particular sample was fairly inactive. Few participants engaged in bouts of hard and very hard activity exceeding 6 minutes per day. As such, the potential benefit of physical activity may not have been attained by this modest level of activity. It does, however, highlight the need to find ways to motivate children to increase duration and intensity of daily physical activity, especially among HA.
The strengths of this study included robust measures of body composition by DXA and physical activity by accelerometry. A limitation of this study was its cross-sectional nature preventing the establishment of cause and effect relationship; longitudinal data will be required to determine the long-term contribution of diet and physical activity to the metabolic syndrome and its components in children. Second, although steps were taken to improve reliability (i.e. multiple pass method, presence of parent), reliance on self-reported dietary intake lends way for inaccuracy. In addition, although both the dietary and physical activity patterns of this sample closely mimic national averages, the sample was relatively small and included only participants from a small geographic area limiting the generalizablity. For example, the HA children included in this study were primarily of Mexican descent and recent immigrants to the United States. Finally, the low association detected between diet and components of the metabolic syndrome in HA does not infer that the associations do not exist and it is plausible that there was not enough power to detect the associations. Although this does not appear to be the case, based on the p-values (>0.5) it is plausible that associations may be noted with a larger sample size.
In conclusion, the results of this study suggested that caloric distribution of macronutrients contribute to metabolic risk in children. Of specific interest, waist circumference was inversely associated with dietary fat. This observation suggests that dietary recommendations for children should not focus on limiting intake of total fat, but rather on emphasizing consumption of "healthy" fats, such as monounsaturated fats. Further, waist circumference was positively associated with carbohydrate intake. Although the role of carbohydrate quality was not specifically evaluated, this observation may suggest that modern diets, which often include much processed food containing simple carbohydrates and refined sugars, have a detrimental effect on body composition and metabolism. Further research is warranted to determine the optimal macronutrient composition for metabolic health in children, and to determine whether macronutrient quality is particularly detrimental to metabolic health. Lastly, it was observed that, dietary factors may exacerbate metabolic risk in an ethnicity-specific manner. The data suggests that AA children are particularly sensitive to the carbohydrate content of the diet. In contrast, physical inactivity was associated with adverse glucose outcomes in EA and HA. These observations suggest that lifestyle recommendations for pediatric metabolic health should take into account ethnicity, with emphasis on nutrition interventions for AA, and on decreasing sedentary activity in EA and HA.
There are no potential conflicts of interest that could raise questions about a paper's credibility if disclosed later. JRF conceived the study, participated in its design and coordination, carried out data analysis, and contributed to the writing of the manuscript. KC and ADK carried out the statistical analyses and contributed to the writing of the manuscript. JRF and BG contributed to design and acquisition of human data. KC, BG, AKD, and JRF critically revised the manuscript.
Funded by: R01 DK067426-01, M01 RR00032
There are not any potential, perceived, or real conflicts of interests, especially any financial arrangements to be disclosed by any of the authors of this manuscript.
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Vt. hikers rescued from NH mountainOctober 09,2013
Vt. hikers rescued
from NH mountain
GORHAM, N.H. Two Vermont hikers are safe after being rescued from a New Hampshire mountain after a storm.
The couple were on the summit of Mount Madison in the White Mountains on Monday afternoon when a severe storm soaked them and their gear.
Then a dense fog made it nearly impossible to see the trail. With temperatures dropping, they called 911 for help at about 7 p.m.
Rescuers hiked about four miles up the trail and found them shortly after 1 a.m.
Twenty-eight-year-old Raymond Barnard and 27-year-old Stephanie Watkins of Brownsville, Vt., were cold and wet but able to put on dry clothing and follow rescuers down.
The New Hampshire Fish and Game Department said the pair had used sleeping bags and tent rain fly to protect themselves, which probably saved their lives.MORE IN This Just In
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According to Gary With Da Tea, Khloe Kardashian revealed on "The Chelsea Lately Show" she once had sex with O.J. Simpson. This was Khloe's response to rumors that O.J. is her biological father. Listen to Gary's Tea in the audio player to hear if this was a bad joke or she really did sleep with O.J.!
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7 Reasons Why Khloe Is My Favorite Kardashian [PHOTOS]
7 Reasons Why Khloe Is My Favorite Kardashian [PHOTOS]
1. 7. Her Savvy Fashion Style
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2. 6. Her Witty Sense Of Humor
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3. 5. Her ability to protect and stand up for all her sister's
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4. 4. Her capability to ignore the "Father Scandal"
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