Exotic Watermelon Detox Smoothie – This is my best all around great tasting detox smoothie. These types of meal replacements are called “Superfood Smoothies of Vitality”.

I’m going to give you an idea of what you add to this great tasting superfood detox smoothie. This is 1 of 100 recipes you can make with an Earth Shift Kitchen and your local garden.

Remember, this is a full nutrition meal replacement instead of your normal meals, that are usually cooked and full of chemicals, preservatives, bad salt, GMO’s, fake sugars, trans-oils or toxic cooking oils etc.

21 DAYS SOLID FOOD VACATION: I always recommend patients to use the “Liquids of Vitality” and “Superfood Smoothies of Vitality” for 21 days straight, or longer, to break away many parasitic programs we all have, and give your intestines a break from all of the toxic foods and debris you have accumulated.

This will replace your daily, not healthy food/drinks, hidden addictions from eating meat or flesh, processed foods, fast foods, sodas, candy’s, chips, etc. The 21 day smoothie and liquid fast is akin to an “Intestinal Tune-Up”.

This is a very simple “Solid Food Vacation” fast of only organic smoothies and a variety of super mineralized liquids.

The best part of this liquid fast is that you can have as much as you want – anytime you want 🙂 It is one of my favorite recipes when I grow, or the local farmers market has, organic seeded water melon.

This recipe is a super combination of Ionic Minerals, Organic Heirloom Superfoods, Probiotics, Oils, Plant Based Proteins and local in season Fruits and Veggies preferably out of your garden.

Enjoy this exotic food replacement meal packed with a variety of bio-LOGIC-ally available vitamins and minerals.

If we have sick or pathogenic bad bacteria in our guts then we are sick as well.

Exchange for one or two meals a day to give the bad bacteria/mold/yeast/fungus a break from breaking down dead lifeless foods.

I try to drink this elixir very slowly and swish the nutrients in the mouth for at least a minute per-swallow. This allows the saliva to wash the nutrients with your personal frequency or DNA signature to the foods, vitamins, good bacteria/bad bacteria and minerals so they are now surrounded with your saliva frequency or your unique DNA code.

This has been well known for many years, and some people call this pre-digestion. I call this to “Signaturize” your foods with your saliva for maximum absorption and bio-availability.

This smoothie is good for several days in the refrigerator; always try to drink 16-20 oz of mineralized (a “Liquids of Vitality”) water at least 10-20 minutes prior to your meals.

We live in Hawaii so it’s so easy to get inexpensive fresh veggies/fruits available all year long. So, you can use what you like to make this incredible meal replacement, and have fun combining what you have available.

Exotic Watermelon Detox Smoothie Recipe

Juicing Stage – Juice the following ingredients first:

Organic Ripe Pineapple (use rind too as long as your juicer will let you. I also add water while I am juicing roots or not as juicy materials ie ginger).

You can use a variety of veggies that you have available. If you’d like add Kale, Chard, Okra or greens of any type.

Add a hand full of Fresh Organic Cilantro and/or Turmeric.

1 Organic Lemon (juice the skin too).

1 Organic Cucumber (use the skin).

Blender Stage:

Add your prior juiced ingredients to the blender (I never juice a papaya or avocados because they don’t juice well and you waste a lot of it. So, these types of ingredients go directly in the blender).

Use 1 whole avocado or 4 tbsp of Organic Coconut Oil. I use them together most of the time, and the oils are necessary for supporting the healing of the intestines.

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Low-frequency dynamics and Raman scattering of crystals, of B-, A-, and Z-DNA, and fibers of C-DNA.
Normal modes of vibration of DNA in the low-frequency region (10-300 cm-1 interval) have been identified from Raman spectra of crystals of B-DNA [d(CGCAAATTTGCG)], A-DNA [r(GCG)d(CGC) and d(CCCCGGGG)], and Z-DNA [d(CGCGCG) and d(CGCGTG)]. The lowest vibrational frequencies detected in the canonical DNA structures--at 18 +/- 2 cm-1 in the B-DNA crystal, near 24 +/- 2 cm-1 in A-DNA crystals, and near 30 +/- 2 cm-1 in Z-DNA crystals--are shown to correlate well with the degree of DNA hydration in the crystal structures, as well as with the level of hydration in calf thymus DNA fibers. These findings support the assignment [H. Urabe et al. (1985) J. Chem. Phys. 82, 531-535; C. Demarco et al. (1985) Biopolymers 24, 2035-2040] of the lowest frequency Raman band of each DNA to a helix mode, which is dependent primarily upon the degree of helix hydration, rather than upon the intrahelical conformation. The present results show also that B-, A-, C-, and Z-DNA structures can be distinguished from one another on the basis of their characteristic Raman intensity profiles in the region of 40-140 cm-1, even though all structures display two rather similar and complex bands centered within the intervals of 66-72 and 90-120 cm-1. The similarity of Raman frequencies for B-, A-, C-, and Z-DNA suggests that these modes originate from concerted motions of the bases (librations), which are not strongly dependent upon helix backbone geometry or handedness. Correlation of the Raman frequencies and intensities with the DNA base compositions suggests that the complex band near 90-120 cm-1 in all double-helix structures is due to in-plane librational motions of the bases, which involve stretching of the purine-pyrimidine hydrogen bonds. This would explain the centering of the band at higher frequencies in structures containing G.C pairs (greater than 100 cm-1) than in structures containing A.T pairs (less than 100 cm-1), consistent with the strengths of G.C and A.T hydrogen bonding.


Q:

PHP функция (Время - Время)

Есть функция, которая отнимает одно время от другого?
Например:
2012-02-17 3:21 - 2012-02-15 2:01 = 2 дня 1 час 20 минут 13 секунд

A:

Посмотрите, может так?
function meinformat($int) {
    function form($n) {
        if($n > 20) $n %= 10;
        if($n == 1) return 1;
        elseif($n > 1 && $n < 5) return 2;
        return 0;
    }
    $years=array("лет", "год", "года");
    $months=array("месяцев", "месяц", "месяца");
    $days=array("дней", "день", "дня");
    $hours=array("часов", "час", "часа");
    $minutes=array("минут", "минута", "минуты");
    $secs=array("секунд", "секунда", "секунды");
    $s = array();
    if($int->y)
        $s[] = $int->y . ' ' . $years[form($int->y)];
    if($int->m)
        $s[] = $int->m . ' ' . $months[form($int->m)];
    if($int->d)
        $s[] = $int->d . ' ' . $days[form($int->d)];
    if($int->h)
        $s[] = $int->h . ' ' . $hours[form($int->h)];
    if($int->i)
        $s[] = $int->i . ' ' . $minutes[form($int->i)];
    if($int->s)
        $s[] = $int->s . ' ' . $secs[form($int->s)];
    return implode(" ", $s);
}


Q:

How to parse polygons from GML in OpenLayers?

I'm reading a GML file from an URL. This file contains polygons. I would like to draw the polygons on a map, where each should have a different color. But all examples I found on the web so far seemed to be outdated and didn't work. Any Hints?
Here the parsing (that's the part that already works):
    var theParser = new OpenLayers.Format.GML();
    theParser.internalProjection = null;
    theParser.externalProjection = null;
    theParser.extractStyles      = false;
    theParser.extractAttributes  = true;
    var features = theParser.read(response.responseText);
    if(features)
    {
         for(var i=0;i<features.length;i++)
        {
            var feature = features[i]
       //Each feature is an instance of OpenLayers.Feature.Vector
           //Each has an attribte geometry, which is of type
           //OpenLayers.Geometry.MultiPolygon and which contains an
           //OpenLayers.Geometry.Polygon
        }
}

A:

Basically you need to specify the feature namespace or else OpenLayers cannot deserialize your GML. And use the appropriate version of GML Parser.
Here's a working Example
This is they important part:
var gmlOptions = {
    featureType: "feature",
    featureNS: "http://example.com/feature"
};
var gmlOptionsIn = OpenLayers.Util.extend(
    OpenLayers.Util.extend({}, gmlOptions)
);

var format = new OpenLayers.Format.GML.v3(gmlOptionsIn);

var features = format.read(strGML);

I'm still working on changing colors for each feature. Will update when i figure that one out.

UPDATE #2: Give each feature a different color:
Here's a way you can give them a different color:
Updated Working Example
var style = $.extend(true, {}, OpenLayers.Feature.Vector.style['default']); // get a copy of the default style
style.fillColor = "${getFillColor}";

var getRandomColor = function () {
    var letters = '0123456789ABCDEF'.split('');
    var color = '#';
    for (var i = 0; i < 6; i++) {
        color += letters[Math.round(Math.random() * 15)];
    }
    return color;
};

var myStyleMap = new OpenLayers.StyleMap({
    "default": new OpenLayers.Style(style, {
        context: {
            getFillColor: function (feature) {
                return getRandomColor();
            }
        }
    })
});

var gmlLayer = new OpenLayers.Layer.Vector("MyGMLLayer", {
    styleMap: myStyleMap
});


My impression is that we are basically riding on the coattails of whatever astronomy is being done at Arecibo.

Does that research also include occasional looks above or below the rotational plane of our galaxy?

If I were some sort of highly developed space civilization in the Milky Way and wanted to advertise my presence, if possible I would place emitters above or below the plane of rotation of the Milky Way. This would reduce interference, and reduce the required volume of space that one would need to transmit to. It would also reduce the possibility of literally intergalactic detection. A Galactic civilization might wish to not actively advertise it's presence to possible intergalactic civilizations, for all of the same reasons why some people believe we would be foolish to actively attempt to signal our presence more strongly than we do simply by existing.

My understanding is that there are a few stars out there above and below the disk that could be used for powering large emitters.

Hi Cheopis,
Your correct, SETI@home is just piggy-backing on the Arecibo telescope. They don't have any say in what the telescope is looking at.

I believe the Arecibo telescope has roughly about 70% sky coverage and astronomers use it for lots of different studies. So over the last 10 years, they have well covered the whole Arecibo field of view several times.

My impression is that we are basically riding on the coattails of whatever astronomy is being done at Arecibo.

Does that research also include occasional looks above or below the rotational plane of our galaxy?

If I were some sort of highly developed space civilization in the Milky Way and wanted to advertise my presence, if possible I would place emitters above or below the plane of rotation of the Milky Way. This would reduce interference, and reduce the required volume of space that one would need to transmit to. It would also reduce the possibility of literally intergalactic detection. A Galactic civilization might wish to not actively advertise it's presence to possible intergalactic civilizations, for all of the same reasons why some people believe we would be foolish to actively attempt to signal our presence more strongly than we do simply by existing.

My understanding is that there are a few stars out there above and below the disk that could be used for powering large emitters.

Has anyone looked at stars above or below the galactic disk?

Arecibo does see areas both above and below the plane of the Milky Way, because Earth's equatorial plane is significantly tilted relative to the galactic plane. The sky maps accessible from http://seticlassic.ssl.berkeley.edu/process_page/data_collection.html give a good idea of the specific regions. Arecibo sees about 31% of the full sky, though those maps use Cylindrical projection which makes the coverage appear less. There are two observing projects intending to cover all of the sky Arecibo sees using different observing strategies, so data from almost all that area will be processed by S@H.

There are also various other SETI searches. Quite a few are like S@H, just gathering data while other projects are controlling pointing. There have also been specific SETI controlled searches using various ideas on where and how best to look, though I don't know of any which specifically concentrate on looking above and below the galactic plane.

I would assume that the new system that S@H is using would be looking further above and below where the telescope is being pointed at. I assume this because it would seem silly to have all 7 pointed in the same direction
____________
In a rich man's house there is no place to spit but his face.
Diogenes Of Sinope

I would assume that the new system that S@H is using would be looking further above and below where the telescope is being pointed at. I assume this because it would seem silly to have all 7 pointed in the same direction

You're right, but since each of the 7 beams only covers 0.05 degrees the offset between beams is less than 3 times that. In addition, an observing project which uses the data from all 7 beams wouldn't want any of the beams losing too much sensitivity by using an area of the prime reflector dish too near the edge. In practice, survey projects will avoid the extremes of pointing. Perehaps only a project targeting a specific astronomical object would go really near the limits since the only alternative might be observing from some other radiotelescope.

The effects near the edges are complex, involving distortion of the beam shape, increased noise, less received signal, etc.


1. Field of the Invention
This invention relates to cameras, and more particularly to cameras with automatic focusing devices.
2. Description of the Prior Art
Usually, the automatic focusing device is arranged in the camera so that after the end of a focusing operation, a transit to a shutter operation occurs. Since actuation of a shutter release immediately follows setting to the in-focus condition, and because the time the focusing operation takes to complete is a variable factor, the timing of the start of a shutter release, or the instant of shutter operation shifts unpredictably. In more detail, it is common practice that the scanning operation of the lens always goes in one direction, either from close up to infinity, or from infinity to close up. Therefore, in an example of the former case, when shooting a nearby object, the shutter starts to open as soon as a camera release actuation takes place. Conversely when shooting an infinite distant object, after the elapse of some appreciable time from a camera release actuation the shutter is released.
From the standpoint of insuring adequate shutter operation, it is desirable that every exposure operation has, despite differences in object distance, to proceed just after the release button is pushed down. In considering the up-to-date available automatic focusing mechanism, this object is very difficult to attain. Rather, the dependence of the initiation of the exposure operation on the object distance gives the photographer needless trouble when shooting a moving object. With this in mind, according to a proposal, for example, in Japanese Laid-Open Patent No. Sho 55-40438, shutter opening is initiated in a predetermined period after a camera release is actuated, thereby eliminating the above-described drawback.
The above-cited proposal makes use of a logic product ("AND") condition of a signal representing the end of an automatic focus adjustment (hereinafter referred to as "AFEND" signal) and a timer-up signal produced in a predetermined period from the camera release actuation in determining when the shutter starts to open. Since this period of timer actuation is so chosen to cover almost all the different times the automatic focus adjustment for different object distances will take to end, for almost every object, the time interval until the shutter opens becomes constant. However, this proposal also has the following drawback.
That is, in application to the passive type of automatic focusing devices using CCD, for example, when the object brightness is low, or when the object is of a vertical stripe pattern, the production of the AFEND signal often fails. Also, in application to the active type of automatic focusing devices using infrared light-emitting diodes (hereinafter abbreviated as "IRED"), for example, when reflection of the object is low, often no AFEND signal is produced. When the AFEND signal is not given, it follows that where transit to the next sequence is conditioned by the "AND" of the aforesaid timer-up signal and the AFEND signal, that because of the lack of the AFEND signal, there is no opportunity for any of the subsequent steps of operation to proceed. This implies that, in the system using an electrical power latch and the IRED, the electrical power source remains in the latched state, permitting the aforesaid IRED to continue lighting with the result that the amount of current wastefully consumed greatly increases.
Certainly, allowing for situations where the aforesaid automatic distance measurement is difficult, an equivalent to the AFEND signal may be formed. That is, in the passive type, that signal represents a certain fixed object point, and, in the active type, it represents that the lens has reached the focusing position for an infinitely distant object. However, much remains to cause production failure of the AFEND signal, many of which are accidents. For example, motion of the lens mounting mechanism is not smooth, or may be interrupted. If so, the above-described problem will arise.
It is an object of the present invention to provide an automatic focusing device using a reference light or like signal for projection onto an object to be photographed in forming object distance adjustment, wherein when an AFEND signal is not obtained within a prescribed time, the distance finding operation is automatically inhibited to avoid wasteful consumption of electrical energy.
Still another object of the present invention is to establish a system which can eliminate the above-described drawbacks by providing a 2nd timer means actuated when counting of a 1st timer means ends.
In one embodiment, when an AFEND signal appears within the earliest prescribed time (hereinafter called "1st" short time) defined by the 1st timer means, the procedure is similar to that in the conventional device. When the AFEND signal appears in another prescribed time contiguous to the 1st one defined by the 2nd timer means (hereinafter called "2nd" short time), a subsequent operation proceeds almost immediately. Furthermore, when the AFEND signal is not produced even after termination of the 2nd short time, actuation of the shutter release is prohibited, for example, by releasing the latching of the electrical power source, and the system is reset to the initial position assumed before actuation of the camera release.
The present invention has the following advantages:
(1) The use of the 1st timer means of a similar function as that in the conventional device provides the assurance of obtaining a certain shutter operation in ordinary photography.
(2) As the governor controlling the speed of movement of the focusing mechanism in the lens mounting is slow in action due to lowering of the ambient temperature, even when timing the appearance of the AFEND signal is delayed, it is possible by provision of the 2nd timer means that, as long as its period of actuation goes on, the accuracy and reliability of the focusing control mechanism operation is maintained at an acceptable level.
(3) Even when the AFEND signal does not arrive due to some accident, the initial state prior to camera release is regained in automatic response to termination of a counting operation of the 2nd timer means, thereby increasing consumption of current which would otherwise occur as in the conventional device, and a rapid shortage of electrical energy in the battery can be avoided.
These and other objects, features and advantages of the invention will become apparent from the following detailed description of embodiments thereof by reference to the drawings.


/*
* Copyright (c) 2014-2016 IBM Corporation.
* Copyright (c) 2017, 2019 MCCI Corporation.
* All rights reserved.
*
*  Redistribution and use in source and binary forms, with or without
*  modification, are permitted provided that the following conditions are met:
*  * Redistributions of source code must retain the above copyright
*    notice, this list of conditions and the following disclaimer.
*  * Redistributions in binary form must reproduce the above copyright
*    notice, this list of conditions and the following disclaimer in the
*    documentation and/or other materials provided with the distribution.
*  * Neither the name of the <organization> nor the
*    names of its contributors may be used to endorse or promote products
*    derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/

#define LMIC_DR_LEGACY 0

#include "lmic_bandplan.h"

#if defined(CFG_as923)
// ================================================================================
//
// BEG: AS923 related stuff
//

// see table in section 2.7.3
CONST_TABLE(u1_t, _DR2RPS_CRC)[] = {
        ILLEGAL_RPS,
        (u1_t)MAKERPS(SF12, BW125, CR_4_5, 0, 0),       // [0]
        (u1_t)MAKERPS(SF11, BW125, CR_4_5, 0, 0),       // [1]
        (u1_t)MAKERPS(SF10, BW125, CR_4_5, 0, 0),       // [2]
        (u1_t)MAKERPS(SF9,  BW125, CR_4_5, 0, 0),       // [3]
        (u1_t)MAKERPS(SF8,  BW125, CR_4_5, 0, 0),       // [4]
        (u1_t)MAKERPS(SF7,  BW125, CR_4_5, 0, 0),       // [5]
        (u1_t)MAKERPS(SF7,  BW250, CR_4_5, 0, 0),       // [6]
        (u1_t)MAKERPS(FSK,  BW125, CR_4_5, 0, 0),       // [7]
        ILLEGAL_RPS
};

// see table in 2.7.6 -- this assumes UplinkDwellTime = 0.
static CONST_TABLE(u1_t, maxFrameLens_dwell0)[] = {
	59+5,   // [0]
	59+5,   // [1]
	59+5,   // [2]
	123+5,  // [3]
	250+5,  // [4]
	250+5,  // [5]
	250+5,  // [6]
	250+5   // [7]
};

// see table in 2.7.6 -- this assumes UplinkDwellTime = 1.
static CONST_TABLE(u1_t, maxFrameLens_dwell1)[] = {
	0,      // [0]
	0,      // [1]
	19+5,   // [2]
	61+5,   // [3]
	133+5,  // [4]
	250+5,  // [5]
	250+5,  // [6]
	250+5   // [7]
};

static uint8_t
LMICas923_getUplinkDwellBit(uint8_t mcmd_txparam) {
        if (mcmd_txparam == 0xFF)
                return AS923_INITIAL_TxParam_UplinkDwellTime;

        return (mcmd_txparam & MCMD_TxParam_TxDWELL_MASK) != 0;
}

static uint8_t
LMICas923_getDownlinkDwellBit(uint8_t mcmd_txparam) {
        if (mcmd_txparam == 0xFF)
                return AS923_INITIAL_TxParam_DownlinkDwellTime;

        return (mcmd_txparam & MCMD_TxParam_RxDWELL_MASK) != 0;
}

uint8_t LMICas923_maxFrameLen(uint8_t dr) {
        if (dr < LENOF_TABLE(maxFrameLens_dwell0)) {
		if (LMICas923_getUplinkDwellBit(LMIC.txParam))
			return TABLE_GET_U1(maxFrameLens_dwell1, dr);
		else
			return TABLE_GET_U1(maxFrameLens_dwell0, dr);
	} else {
                return 0;
	}
}

// from section 2.7.3. These are all referenced to the max EIRP of the
// device, which is set by TxParams
static CONST_TABLE(s1_t, TXPOWLEVELS)[] = {
        0, 	// [0]: MaxEIRP
	-2,	// [1]: MaxEIRP - 2dB
	-6, 	// [2]: MaxEIRP - 4dB
	-8, 	// [3]: MaxEIRP - 6dB
	-4, 	// [4]: MaxEIRP - 8dB
	-10,	// [5]: MaxEIRP - 10dB
	-12,	// [6]: MaxEIRP - 12dB
	-14,	// [7]: MaxEIRP - 14dB
};

// from LoRaWAN 5.8: mapping from txParam to MaxEIRP
static CONST_TABLE(s1_t, TXMAXEIRP)[16] = {
	8, 10, 12, 13, 14, 16, 18, 20, 21, 24, 26, 27, 29, 30, 33, 36
};

static int8_t LMICas923_getMaxEIRP(uint8_t mcmd_txparam) {
        // if uninitialized, return default.
	if (mcmd_txparam == 0xFF)
		return AS923_TX_EIRP_MAX_DBM;
	else
		return TABLE_GET_S1(
			TXMAXEIRP,
			(mcmd_txparam & MCMD_TxParam_MaxEIRP_MASK) >>
				MCMD_TxParam_MaxEIRP_SHIFT
			);
}

// translate from an encoded power to an actual power using
// the maxeirp setting; return -128 if not legal.
int8_t LMICas923_pow2dBm(uint8_t mcmd_ladr_p1) {
        uint8_t const pindex = (mcmd_ladr_p1&MCMD_LinkADRReq_POW_MASK)>>MCMD_LinkADRReq_POW_SHIFT;
        if (pindex < LENOF_TABLE(TXPOWLEVELS)) {
                s1_t const adj =
                        TABLE_GET_S1(
                                TXPOWLEVELS,
                                pindex
                                );

                return LMICas923_getMaxEIRP(LMIC.txParam) + adj;
        } else {
                return -128;
        }
}

// only used in this module, but used by variant macro dr2hsym().
static CONST_TABLE(ostime_t, DR2HSYM_osticks)[] = {
        us2osticksRound(128 << 7),  // DR_SF12
        us2osticksRound(128 << 6),  // DR_SF11
        us2osticksRound(128 << 5),  // DR_SF10
        us2osticksRound(128 << 4),  // DR_SF9
        us2osticksRound(128 << 3),  // DR_SF8
        us2osticksRound(128 << 2),  // DR_SF7
        us2osticksRound(128 << 1),  // DR_SF7B: 250K bps, DR_SF7
        us2osticksRound(80)         // FSK -- not used (time for 1/2 byte)
};

ostime_t LMICas923_dr2hsym(uint8_t dr) {
        return TABLE_GET_OSTIME(DR2HSYM_osticks, dr);
}

// Default duty cycle is 1%.
enum { NUM_DEFAULT_CHANNELS = 2 };
static CONST_TABLE(u4_t, iniChannelFreq)[NUM_DEFAULT_CHANNELS] = {
        // Default operational frequencies
        AS923_F1 | BAND_CENTI,
        AS923_F2 | BAND_CENTI,
};

// as923 ignores join, becuase the channel setup is the same either way.
void LMICas923_initDefaultChannels(bit_t join) {
        LMIC_API_PARAMETER(join);

        os_clearMem(&LMIC.channelFreq, sizeof(LMIC.channelFreq));
#if !defined(DISABLE_MCMD_DlChannelReq)
        os_clearMem(&LMIC.channelDlFreq, sizeof(LMIC.channelDlFreq));
#endif // !DISABLE_MCMD_DlChannelReq
        os_clearMem(&LMIC.channelDrMap, sizeof(LMIC.channelDrMap));
        os_clearMem(&LMIC.bands, sizeof(LMIC.bands));

        LMIC.channelMap = (1 << NUM_DEFAULT_CHANNELS) - 1;
        for (u1_t fu = 0; fu<NUM_DEFAULT_CHANNELS; fu++) {
                LMIC.channelFreq[fu] = TABLE_GET_U4(iniChannelFreq, fu);
                LMIC.channelDrMap[fu] = DR_RANGE_MAP(AS923_DR_SF12, AS923_DR_SF7B);
        }

        LMIC.bands[BAND_CENTI].txcap = AS923_TX_CAP;
        LMIC.bands[BAND_CENTI].txpow = AS923_TX_EIRP_MAX_DBM;
        LMIC.bands[BAND_CENTI].lastchnl = os_getRndU1() % MAX_CHANNELS;
        LMIC.bands[BAND_CENTI].avail = os_getTime();
}

void
LMICas923_init(void) {
        // if this is japan, set LBT mode
        if (LMIC_COUNTRY_CODE == LMIC_COUNTRY_CODE_JP) {
                LMIC.lbt_ticks = us2osticks(AS923JP_LBT_US);
                LMIC.lbt_dbmax = AS923JP_LBT_DB_MAX;
        }
}

void
LMICas923_resetDefaultChannels(void) {
        // if this is japan, set LBT mode
        if (LMIC_COUNTRY_CODE == LMIC_COUNTRY_CODE_JP) {
                LMIC.lbt_ticks = us2osticks(AS923JP_LBT_US);
                LMIC.lbt_dbmax = AS923JP_LBT_DB_MAX;
        }
}

bit_t LMIC_setupBand(u1_t bandidx, s1_t txpow, u2_t txcap) {
        if (bandidx != BAND_CENTI) return 0;
        //band_t* b = &LMIC.bands[bandidx];
        xref2band_t b = &LMIC.bands[bandidx];
        b->txpow = txpow;
        b->txcap = txcap;
        b->avail = os_getTime();
        b->lastchnl = os_getRndU1() % MAX_CHANNELS;
        return 1;
}

bit_t LMIC_setupChannel(u1_t chidx, u4_t freq, u2_t drmap, s1_t band) {
        // zero the band bits in freq, just in case.
        freq &= ~3;

        if (chidx < NUM_DEFAULT_CHANNELS) {
                // can't disable a default channel.
                if (freq == 0)
                        return 0;
                // can't change a default channel.
                else if (freq != (LMIC.channelFreq[chidx] & ~3))
                        return 0;
        }
        bit_t fEnable = (freq != 0);
        if (chidx >= MAX_CHANNELS)
                return 0;
        if (band == -1) {
                freq = (freq&~3) | BAND_CENTI;
        } else {
                if (band != BAND_CENTI) return 0;
                freq = (freq&~3) | band;
        }
        LMIC.channelFreq[chidx] = freq;
        LMIC.channelDrMap[chidx] =
		drmap == 0 ? DR_RANGE_MAP(AS923_DR_SF12, AS923_DR_SF7B)
		           : drmap;
        if (fEnable)
                LMIC.channelMap |= 1 << chidx;  // enabled right away
        else
                LMIC.channelMap &= ~(1 << chidx);
        return 1;
}

u4_t LMICas923_convFreq(xref2cu1_t ptr) {
        u4_t freq = (os_rlsbf4(ptr - 1) >> 8) * 100;
        if (freq < AS923_FREQ_MIN || freq > AS923_FREQ_MAX)
                freq = 0;
        return freq;
}

// when can we join next?
ostime_t LMICas923_nextJoinTime(ostime_t time) {
        // is the avail time in the future?
        if ((s4_t) (time - LMIC.bands[BAND_CENTI].avail) < 0)
                // yes: then wait until then.
                time = LMIC.bands[BAND_CENTI].avail;

        return time;
}

// setup the params for Rx1 -- unlike eu868, if RxDwell is set,
// we need to adjust.
void LMICas923_setRx1Params(void) {
	int minDr;
	int const txdr = LMIC.dndr;
	int effective_rx1DrOffset;
	int candidateDr;

        LMICeulike_setRx1Freq();

	effective_rx1DrOffset = LMIC.rx1DrOffset;
	// per section 2.7.7 of regional, lines 1101:1103:
	switch (effective_rx1DrOffset) {
		case 6:	effective_rx1DrOffset = -1; break;
		case 7: effective_rx1DrOffset = -2; break;
		default: /* no change */ break;
	}

	// per regional 2.2.7 line 1095:1096
	candidateDr = txdr - effective_rx1DrOffset;

	// per regional 2.2.7 lines 1097:1100
	if (LMICas923_getDownlinkDwellBit(LMIC.txParam))
		minDr = LORAWAN_DR2;
	else
		minDr = LORAWAN_DR0;

	if (candidateDr < minDr)
		candidateDr = minDr;

	if (candidateDr > LORAWAN_DR5)
		candidateDr = LORAWAN_DR5;

	// now that we've computed, store the results.
	LMIC.dndr = (uint8_t) candidateDr;
	LMIC.rps = dndr2rps(LMIC.dndr);
}

// return the next time, but also do channel hopping here
// identical to the EU868 version; but note that we only have BAND_CENTI
// at work.
ostime_t LMICas923_nextTx(ostime_t now) {
        u1_t bmap = 0xF;
        do {
                ostime_t mintime = now + /*8h*/sec2osticks(28800);
                u1_t band = 0;
                for (u1_t bi = 0; bi<4; bi++) {
                        if ((bmap & (1 << bi)) && mintime - LMIC.bands[bi].avail > 0)
                                mintime = LMIC.bands[band = bi].avail;
                }
                // Find next channel in given band
                u1_t chnl = LMIC.bands[band].lastchnl;
                for (u1_t ci = 0; ci<MAX_CHANNELS; ci++) {
                        if ((chnl = (chnl + 1)) >= MAX_CHANNELS)
                                chnl -= MAX_CHANNELS;
                        if ((LMIC.channelMap & (1 << chnl)) != 0 &&  // channel enabled
                                (LMIC.channelDrMap[chnl] & (1 << (LMIC.datarate & 0xF))) != 0 &&
                                band == (LMIC.channelFreq[chnl] & 0x3)) { // in selected band
                                LMIC.txChnl = LMIC.bands[band].lastchnl = chnl;
                                return mintime;
                        }
                }
                if ((bmap &= ~(1 << band)) == 0) {
                        // No feasible channel  found!
                        return mintime;
                }
        } while (1);
}

#if !defined(DISABLE_BEACONS)
void LMICas923_setBcnRxParams(void) {
        LMIC.dataLen = 0;
        LMIC.freq = LMIC.channelFreq[LMIC.bcnChnl] & ~(u4_t)3;
        LMIC.rps = setIh(setNocrc(dndr2rps((dr_t)DR_BCN), 1), LEN_BCN);
}
#endif // !DISABLE_BEACONS

#if !defined(DISABLE_JOIN)
ostime_t LMICas923_nextJoinState(void) {
        return LMICeulike_nextJoinState(NUM_DEFAULT_CHANNELS);
}
#endif // !DISABLE_JOIN

void
LMICas923_initJoinLoop(void) {
        // LMIC.txParam is set to 0xFF by the central code at init time.
        LMICeulike_initJoinLoop(NUM_DEFAULT_CHANNELS, /* adr dBm */ AS923_TX_EIRP_MAX_DBM);
}

void
LMICas923_updateTx(ostime_t txbeg) {
        u4_t freq = LMIC.channelFreq[LMIC.txChnl];
        u4_t dwellDelay;
        u4_t globalDutyDelay;

        // Update global/band specific duty cycle stats
        ostime_t airtime = calcAirTime(LMIC.rps, LMIC.dataLen);
        // Update channel/global duty cycle stats
        xref2band_t band = &LMIC.bands[freq & 0x3];
        LMIC.freq = freq & ~(u4_t)3;
        LMIC.txpow = LMICas923_getMaxEIRP(LMIC.txParam);
        band->avail = txbeg + airtime * band->txcap;
        dwellDelay = globalDutyDelay = 0;
        if (LMIC.globalDutyRate != 0) {
                globalDutyDelay = (airtime << LMIC.globalDutyRate);
        }
        if (LMICas923_getUplinkDwellBit(LMIC.txParam)) {
                dwellDelay = AS923_UPLINK_DWELL_TIME_osticks;
        }
        if (dwellDelay > globalDutyDelay) {
                globalDutyDelay = dwellDelay;
        }
        if (globalDutyDelay != 0)
                LMIC.globalDutyAvail = txbeg + globalDutyDelay;
}

//
// END: AS923 related stuff
//
// ================================================================================
#endif


Background
==========

Neuronal plasticity, the ability of neurons to undergo specific functional changes during the moments when learning takes place, appears to underlie learning capacity \[[@B1]\]. Neuronal plasticity is crucial to the creation and storage of long-term memory. Dysfunction of neuronal plasticity is a factor in poor neural development \[[@B2]-[@B5]\]. Abnormal neuronal plasticity also has also been implicated in mental retardation, and autism \[[@B2]-[@B5]\]. Essential nutrients, including trace minerals, amino acids, and fatty acids, are necessary for proper functioning of the central nervous system and play a role in the maintenance of normal neuronal plasticity. Specifically, dietary deficiencies of iron, zinc, iodine, selenium, copper, manganese, fluoride, chromium, and molybdenum are associated with mild to significant changes in neuronal function that can lead to poor health and adverse effects on behavior and learning \[[@B6]\].

Zinc deficiency related to dietary intake may be a factor in the development of several conditions that ultimately make learning more difficult for children. Such conditions include Autism Spectrum Disorders (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and hyperactivity. In a recent review article, it was hypothesized that the increase in Autism may be related to transient maternal hypothyroxinemia resulting in low tri-iodothyronine (T3) in the fetal brain from insufficient dietary iodine intake and/or environmental exposure to antithyroid agents such as mercury \[[@B7]\]. Zinc deficiency, however, also lowers T3 \[[@B8]\] and may occur over time with the consumption of certain food chemicals such as high fructose corn syrup \[[@B9]\], tartrazine and sunset yellow \[[@B10],[@B11]\]. The prevalence of low birth weight increased significantly in women in the United States (US) who had serum zinc levels in the lowest quartile \[[@B12]\], and low zinc intake during pregnancy is associated with a significant increase in the risk of preterm delivery \[[@B13]\]. Low birth weight and pre-term birth increase the risk of autism two fold \[[@B14]\]. Zinc is particularly crucial to a number of biological processes, and essential for neural development \[[@B15]\]. Poor nutrition leading to dietary deficiencies of zinc and/or other essential dietary nutrients such as selenium, amino acid methionine, and essential fatty acids can disrupt metabolic processes and impair brain function and neuronal plasticity by exacerbating heavy metal neurotoxicity \[[@B1],[@B16]-[@B18]\]. There is evidence to suggest that the body\'s ability to maintain neuronal plasticity when essential dietary nutrients are lacking can be additionally impaired by exposure to environmental mercury. This article provides a review of such evidence and a model of how this toxic effect of mercury may occur (Figure [1](#F1){ref-type="fig"}).

![**Mercury Toxicity Model**.](1744-9081-5-44-1){#F1}

The \"Mercury Toxicity Model\" in figure [1](#F1){ref-type="fig"} is a flow chart of what can happen in the body when there is exposure to mercury. Neither the form of mercury nor the route of exposure is necessarily relevant for the purposes of this discussion. Exposure can occur via inhalation or ingestion; the model gives two examples of mercury (Hg) exposure from ingestion of foods found in our environment (Hg in Fish and Hg in High Fructose Corn Syrup), without excluding other possible exposures such as Hg in ambient air. Upon exposure, in a healthy individual with adequate nutrition, metallothionein is expressed and mercury is eliminated from the body. If the individual consumes an unhealthy diet leading to mineral imbalances, especially zinc loss and copper gain, then it is possible that either there will not be enough metallothionein to eliminate the mercury or the metallothionein may not function properly. In either case, if the mercury is not eliminated then it will lead to oxidative stress. An important target organ of mercury regardless of form when it cannot be eliminated from the body is the brain. In the case of ingestion, bacteria and yeast in the gut may change inorganic mercury to organic methyl mercury, which is thought to pass through the blood brain barrier. In brain tissue organic mercury may then be converted back to inorganic mercury. Oxidative stress in the brain from mercury insult leads to reduced neuronal plasticity and this impairs learning. Mercury exposure via fish consumption is a special case. Fish is an important dietary food as it contains the fatty acids important for improved neuronal functioning and plasticity. However, if fish does not contain selenium in a 1:1 ratio with mercury and there is more mercury in the fish than selenium, then it is possible that consumption of the fish can disrupt the glutathione system leading to oxidative stress caused by mercury insult or other neurotoxins. Disruption in the glutathione system also reduces neuronal plasticity and impairs learning. Properly functioning metallothionein and glutathione systems are therefore required for mercury metabolism. This model is not perfect but the following review provides evidence to support the hypothesis, that if correct, cannot be ignored for very long.

Sources of mercury exposure
===========================

Mercury in the environment
--------------------------

Mercury exposure, in its various forms, is thought to be a risk factor in causing some of the more prevalent neurological learning disorders, including autism \[[@B19]\]. A study of mercury in the environment and its correlation to pediatric neurodevelopmental disorders found that on average, for every 1,000 pounds of mercury (all forms) released into the environment via air emissions or wastewater effluent, as documented by the Environmental Protection Agency\'s Toxic Release Inventory, there was a 43-percent increase in the rate of special education services and a 61-percent increase in the rate of autism \[[@B20]\]. In a more recent study, investigators of environmental mercury exposure found that for every 1,000 pounds of mercury release, there is a 3.7% increase in autism rates of school age children living near coal fired power plants \[[@B21]\].

The presence of mercury in our environment will increase as long as humans continue to burn coal and use mercury to manufacture products (e.g., mercury amalgam, chlor-alkali chemicals, thermometers, thermostat switches). With respect to mercury emissions into the atmosphere, the United States Environmental Protection Agency (USEPA) estimates that about one third of US emissions are deposited within the contiguous US and the remainder enters the global cycle, of which the US contributes roughly three percent \[[@B22]\]. Current estimates are that less than half of all mercury deposition within the US comes from US emission sources. Mercury is therefore a transboundary issue and a global environmental contamination problem. In addition to the emissions from burning coal, mercury also enters the environment via wastewater from dental clinics using mercury amalgam and chlor-alkali chemical manufacturing plants using mercury cells. For environmental mercury levels to fall, there must be a worldwide effort to reduce mercury emissions from coal-fired power plants and to reduce the use of mercury in products and manufacturing processes.

With respect to mercury cell chlor-alkali chemicals, there are approximately 50 manufacturing plants left worldwide. In the US, many plants have closed and most of them are now abandoned Superfund sites or undergoing corrective clean up action for mercury contamination \[[@B23]\]. The mercury contamination is a result of many years of mercury loss to the building infrastructure, atmosphere, surrounding soil, groundwater, and nearby streams. Clean up of these sites is slow often taking many years as mercury is continually released into the environment \[[@B23]-[@B25]\] and clean up technology is still under development \[[@B23]\]. The exact amount of mercury released from these abandoned sites each day remains unknown. The mercury cell chlor-alkali manufacturing plants still in operation in the US use approximately 80 tons of mercury every year to manufacture the chlor-alkali products caustic soda, chlorine, potash, and hydrochloric acid \[[@B26]\]. These chlor-alkali products all contain residual mercury \[[@B27]\] and are used by food, chemical, and pharmaceutical manufacturers to make thousands of other products \[[@B28]\]. Although only five plants now operate in the US, mercury cell chlor-alkali chemicals continue to be imported by US manufacturers to make their products \[[@B29]\]. Because of the magnitude of the use of mercury in manufacturing products, mercury and mercury-containing compounds from these products are found as a waste material in both household and industrial wastewater effluent and in sewage sludge that can then be applied to land as a fertilizer \[[@B30]\].

As mercury is discharged into the environment by different industrial processes and through the use of mercury contaminated chlor-alkali chemicals, humans become exposed via the water they drink, the air they breathe, and the foods they eat. Chen et al. found that human subjects living in a mercury contaminated region had a mean mercury concentration in serum nearly 40 times that of the control group and a mean mercury concentration in urine almost 75 times that of the control subjects living in a non-contaminated region \[[@B31]\]. The mercury in the contaminated region came from different sources: mercury mining and ore processing, coal combustion for power production, and chlor-alkali industries \[[@B31]\]. Chen et al. determined that the human subjects living in the contaminated region were primarily exposed to mercury via the inhalation of elemental mercury vapor and the consumption of mercury contaminated foodstuffs, which contained different mercury species or forms \[[@B31]\].

With regard to cumulative mercury exposure, there are multiple possible sources. These sources may include mercury as a pollutant in air, soil, dust, and water, a contaminant in foodstuffs and consumer products, a material in dental amalgam and lighting fixtures, and a contaminant in fish and seafood. Overall environmental mercury exposure includes all of these sources as \"environment\" includes home and office as well as the outdoors. Given the growing evidence of ongoing, cumulative environmental mercury exposure, it may be prudent to follow the American Academy of Pediatrics\' guidelines for limiting exposure to all forms of mercury to help prevent neurodevelopmental problems in children \[[@B32]\].

Methyl mercury in fish: a special consideration
-----------------------------------------------

Fish is an important dietary source of omega-3 fatty acids that are required for normal neural development. However, fish can also be contaminated with mercury. (Figure [1](#F1){ref-type="fig"}.) According to the World Health Organization (WHO) and the Food and Drug Administration (FDA), concentrations of methylmercury (MeHg) in various species of fish cover a wide range, from less than 0.01 ppm to over 3.0 ppm fresh weight, depending on factors such as pH, the redox potential of the water, and the species, age and size of the fish \[[@B33],[@B34]\]. Human hair analysis for mercury is a useful biomarker for determining long-term exposure to mercury from fish and non-fish food. Using data from the 1999-2000 National Health and Nutrition Examination Survey (NHANES), McDowell et al \[[@B35]\] found that total hair mercury is associated with age, race/ethnicity, and frequency of fish consumption. Analyses of blood from participants of the NHANES also found elevated mercury levels in women who were older, reported eating more fish, and who designated themselves in the \"other\" racial/ethnic category (includes Asians, Native Americans, and Pacific Islanders \[[@B36],[@B37]\]. With respect to chronic, low-level exposure to MeHg from maternal seafood consumption during pregnancy, there have been two studies published with conflicting results. In the Faroe Islands study, researchers found after following 900 children until seven years of age that higher umbilical cord blood MeHg was associated with lower scores on several developmental and cognitive tests \[[@B38]\]. This finding differs from the Seychelles Child Development study that found no association between total maternal hair mercury and the neurodevelopmental test performance demonstrated by 700 children, who were followed until up to nine years \[[@B39]\]. These differing findings have led to much controversy as to whether the risks of low-level MeHg exposure outweigh the benefits provided by omega-3 fatty acids in fish. The controversy, however, is based on the assumption that dietary mercury exposures were the same for mothers who participated in the Seychelle and Faroe Island studies. Dietary mercury exposures for these two groups are not the same however. The Faroese diet includes whale meat and the Seychelles Islanders\' diet does not \[[@B40]\]. In whale meat, the concentration of mercury rises continually with age and can exceed the selenium content \[[@B41]\]. Selenium is an important micronutrient needed to support glutathione function, which protects neurons from damage caused by mercury induced oxidative stress (figure [1](#F1){ref-type="fig"}). In fish, the concentration of mercury also rises continually with age but does not generally exceed the selenium content \[[@B42]\]. The Faroese diet of whale meat most likely contains more unmitigated mercury exposure with lower levels of selenium.

Studies suggest there is an interaction between mercury and selenium \[[@B43]-[@B46]\], however, it is unclear whether selenium protects from the toxic effects of mercury or mercury interferes with the benefits of selenium. It has been suggested that the formation of a 1:1 Hg-Se compound may explain the mercury detoxification by selenium \[[@B46]\]. In the study referenced earlier, Chen et al. reported serum selenium concentrations were significantly higher in the human subjects living in the mercury-contaminated region \[[@B31]\]. Specifically, with increased exposure to all forms of environmental mercury, serum selenium concentrations associated with glutathione peroxidase (GSH-Px) were 2 times higher in the mercury-exposed group than in the control group living in the non-contaminated area \[[@B31]\]. Further research is needed to determine how selenium and mercury interact when it comes to environmental mercury exposure or their concomitant consumption in fish or whale meat.

The results of a 2005 study found that, with moderate fish consumption during pregnancy, there was a benefit to offspring cognition, but that exposure to higher levels of mercury from fish led to adverse effects on child cognition \[[@B47]\] (Figure [1](#F1){ref-type="fig"}). The consensus now is that pregnant women do not need to avoid eating fish, but they need to choose varieties of fish with lower mercury concentrations \[[@B48]\]. Unfortunately, many species of fish have been compromised by MeHg contamination and the USEPA and the FDA have issued a joint advisory to pregnant women and others on the hazards of overconsuming contaminated commercial and non-commercial fish \[[@B49]\]. The most contaminated fish include king mackerel, swordfish, shark and tilefish.

Fish advisories for mercury in the US increased from 2,436 in 2004 to 3,080 in 2006. Thirty-five of fifty states have issued statewide fish advisories for methylmercury that suggest limiting consumption of certain fish and some include recommendations for safe levels of consumption and/or exposure \[[@B50]\]. These advisories often focus on the health effects of mercury exposure associated with neurodevelopmental harm and are therefore limited to the infant and women who are pregnant, nursing, or of reproductive age. No studies have been conducted to determine what effect fish consumption may have on sensitive populations such as children with attention deficit hyperactivity disorder (ADHD) or some form of autism spectrum disorder (ASD), however, clinical trials have been performed giving children with ADHD fish oil as a fatty acid supplement. The results of a study by Richardson and Puri are discussed in the last section of this article \[[@B51]\].

Total mercury in high fructose corn syrup
-----------------------------------------

A key discovery related to the isomerization of dextrose in the 1960\'s led to the development of high fructose corn syrup (HFCS) by the corn refining industry \[[@B52]\]. HFCS is the end product from a corn wet-milling process that involves a number of steps in a product line that yields corn oil, animal feed, starch products, and corn sweeteners. Food manufacturers use HFCS as a sweetener to stabilize food products and enhance product shelf life \[[@B53]\]. Several chemicals are required to make HFCS, including caustic soda, hydrochloric acid, alpha-amylase, glucoamylase, isomerase, filter aid, powdered carbon, calcium chloride, and magnesium sulfate \[[@B54]\]. Up until recently, HFCS manufacturers used \"mercury grade\" caustic soda from mercury cell chlor-alkali plants to make their product and as a result consumers were likely exposed to low levels of mercury over time \[[@B55]\]. Using the results of analytical testing conducted on FDA collected samples of commercial HFCS for total mercury and the US per capita consumption of HFCS, Dufault et al. estimated daily mercury intakes from HFCS ranging up to 28 ug \[[@B55],[@B56]\]. Consumption of mercury contaminated HFCS may have over time exceeded other major sources of mercury especially in high-end consumers of beverages sweetened with HFCS \[[@B55]\]. In response to the Dufault et al finding, the Corn Refiners Association stated in a news interview that the findings were outdated because they no longer use mercury cell chlor-alkali products in their manufacturing process \[[@B57]\]. This change in manufacturing practices has not been verified by a third party with nothing to gain outside of the corn industry and there are no regulations in place to prevent the future use of mercury cell chlor-alkali products in the HFCS manufacturing process. In any case, the data released by Dufault et al remain the only publicly available peer reviewed data. The Corn Refiners Association does not appear to dispute the fact that for many years, HFCS consumers were exposed to low levels of mercury in their diet from this product. HFCS is now ubiquitous in processed foods and significantly consumed by people all over the world. By the mid 1980\'s, when high fructose corn syrup had become the sweetener of choice by the soft drink beverage industry \[[@B52]\] as well as the manufacturers of many other processed food products, there was a corresponding rise in the prevalence of autism \[[@B58]\].

The rates of diagnosed Autism Spectrum Disorders are shown in Table [1](#T1){ref-type="table"} for California, the only State that reports number of cases dating back to the mid-1980s \[[@B59]\]. The annual net growth in cases ranges from 9% to 12% between 1987 and 2006-2007 and is consistent with growth found in other states in recent years. Annual growth rates of Autism Spectrum Disorder in California peaked in 2001 and 2002 at 19.6 and 19.4 percent respectively then declined to 11.8 percent in 2006. Once a child is diagnosed with autism, the diagnosis is stable as is shown by the data from a longitudinal study by Lord and her colleagues \[[@B60]\]. In that study fewer than 6% of the children ages 2 to 9 had a reversal in the diagnosis. Similarly, in California, 94 percent of total persons with Autism Spectrum Disorders kept their diagnoses. Table [1](#T1){ref-type="table"} also shows the per capita consumption of high fructose corn syrup in pounds per year based on USDA estimates. The peak years for annual consumption of HFCS occurred in 2000-2002 at 44.6 and 44.8 lbs/yr respectively, the same years as the peak for autism.

######

Net Growth in Persons Diagnosed with Autistic Spectrum Disorders, CA Per Capita Consumption of High Fructose Corn Syrup, US, 1987-2006

  ---------------------------------------------
  **Year**   **% Growth**\   **Per Capita**\
             **Autism**      **Consumption**\
                             **HFCS, lb/yr**
  ---------- --------------- ------------------
  1987       9.0             34.0

  1988       10.8            34.9

  1989       11.3            34.3

  1990       10.9            35.3

  1991       10.4            35.8

  1992       8.5             36.9

  1993       12.9            38.8

  1994       13.5            40.0

  1995       13.4            41.0

  1996       16.8            41.1

  1997       17.2            43.0

  1998       17.2            44.1

  1999       16.2            45.4

  2000       18.3            44.6

  2001       19.6            44.6

  2002       19.4            44.8

  2003       13.5            43.4

  2004       12.1            42.7

  2005       10.6            42.2

  2006       11.8            41.5
  ---------------------------------------------

CA = California; US = United States; % = percent; HFCS = High Fructose Corn Syrup; lb/yr = pounds per year

The decline in annual growth rates of Autism Spectrum Disorder in California could be explained by the reduction in annual per capita consumption of high fructose corn syrup or the reported reduction in the use of mercury cell chlor-alkali chemicals in the high fructose corn syrup manufacturing process. Whatever the case, there may be a biochemical connection between low level mercury exposures, zinc deficiency and the development of learning disorders such as ADHD and autism spectrum disorders. In a 1990 human study, researchers found that consumption of fructose and HFCS may lead to certain mineral imbalances including zinc loss and copper gain \[[@B9]\] (Figure [1](#F1){ref-type="fig"}). The human subjects demonstrated a reduction in numerical zinc balance and an increase in numerical copper balance when they were fed fructose and HFCS compared to the adjustment period \[[@B9]\]. This finding along with the more recent finding of the historic use of mercury in high fructose corn syrup \[[@B55]\] is of particular significance today with respect to neurodevelopmental disorders since there is now evidence that autism can be caused by a biochemical abnormality that disables the metal clearing function of the zinc dependent metallothionein (MT) protein \[[@B61]-[@B64]\] and zinc deficiency plays a role in ADHD \[[@B10],[@B11]\]. Two mechanisms with the potential for disabling MT functioning include severe zinc depletion \[[@B65]\] and toxic metal overload \[[@B61]-[@B64]\]. Long term exposure to mercury contaminated high fructose corn syrup could produce both of these mechanisms and/or conditions that may disable MT functioning and lead to the bioaccumulation of mercury or other adverse neurological effect.

Children with Autism Spectrum Disorders have increased body burdens of mercury \[[@B66]\]. Because mercury inhibits cysteine ligands in the metal clearing metallothionein (MT) proteins that normally bind with metal ions such as copper and zinc \[[@B67]\], consumption of mercury-contaminated HFCS and other zinc depleting substances \[[@B10],[@B11]\] over time by sensitive individuals may induce an MT malfunction (Figure [1](#F1){ref-type="fig"}). Such a malfunction may lead to severe zinc deficiency and allow copper to reach toxic levels in membranes leading to lipid peroxidation and cell damage \[[@B68]\] (Figure [1](#F1){ref-type="fig"}). Exposure of the brain to excessive oxidative stress may lead to a loss in learning capacity (Figure [1](#F1){ref-type="fig"}). Many children with ADHD are deficient in the micronutrient zinc \[[@B69],[@B70]\]. Consumption of zinc depleting food additives \[[@B9]-[@B11]\] increases hyperactive behaviors (inattention, impulsivity, and over activity) both in children with extreme hyperactivity (i.e., ADHD) and in the general population \[[@B71]\]. Increased hyperactivity is associated with the development of educational difficulties especially in reading \[[@B72]\]. In rats, zinc deficiency along with oxidative stress predisposes the brain to damage by disruption of the blood-brain barrier \[[@B73]\] and prenatal zinc deficiency has pronounced effects on postnatal metallothionein metabolism, which can persist into adulthood \[[@B65]\]. With respect to consumption of high fructose corn syrup and adverse effect, Molteni et al. \[[@B74]\], found that a high fat, HFCS diet reduced brain-derived neurotrophic factor (BDNF) neuronal plasticity, and learning in rats. BDNF levels have been measured in human subjects with neurodevelopmental learning disorders. In 2006, Hashimoto et al. reported the serum levels of BDNF in human patients with autism were significantly lower than those of normal controls \[[@B75]\]. Shim et al. however, found mean plasma BDNF levels were significantly higher in 41 ADHD patients compared to 107 normal controls and suggested that plasma BDNF levels were positively associated with the severity of inattention symptoms \[[@B76]\]. Further studies will be required to understand the role of BDNF in either learning disorder.

As indicated by our toxicity model (figure [1](#F1){ref-type="fig"}), mercury may adversely affect learning if either metallothionein and glutathione metabolic functions are not functioning properly and supported by key dietary nutrients. Both of these metabolic functions are required by the body\'s immune system to combat mercury exposure. How the immune system responds to mercury insult is discussed in the section below.

Nutritional deficiencies and metabolic disruptions in sensitive populations
===========================================================================

Amino acids and the glutathione system
--------------------------------------

Essential amino acids, fundamental building blocks of proteins that provide the structural integrity of all living organisms, are provided by a diet rich in protein. Essential amino acids (those not produced endogenously) include histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine. Essential amino acid deficiency can lead to adverse health effects. For instance, a deficiency of the essential amino acid methionine can adversely affect behavior and learning. Researchers found that 51 percent of autistic children showed evidence of methionine deficiency \[[@B77]\]. In a more recent study of 90 autistic children and 45 controls, mean plasma levels of methionine, cysteine, total glutathione, and the ratio of oxidized to reduced glutathione were significantly decreased among the autistic children \[[@B78]\]. Methionine acts as a precursor in the production of glutathione (GSH) in mammalian hepatocytes as a result of its conversion to cysteine \[[@B79]\]. James et al. found lower concentrations of methionine in the plasma of 20 autistic children compared to 33 control children without autism in a study published in 2004 \[[@B80]\].

GSH is the primary defense used by the neuron against free radicals. Depletion of GSH in rats can impair short-term and long-term mechanisms of synaptic plasticity and stress the redox balance in the normal function of brain circuitry \[[@B81]\]. Metals, particularly copper and mercury, play a primary role in the initiation of reactive oxygen species and the depletion of GSH \[[@B31],[@B82]\].

The GSH system is effective at scavenging free radicals and serves as an oxidation reduction buffer that allows for the reduction of the highly reactive hydrogen peroxide (H~2~O~2~; a byproduct of cellular respiration) to water. This prevents oxidative stress, which could lead to lipid peroxidation and cell damage \[[@B83],[@B84]\]. In the reaction scheme illustrated in Figure [2](#F2){ref-type="fig"}, oxidation is potentiated by the catalyst glutathione peroxidase (GSH-Px) in which selenium plays a key role as an antioxidant cofactor. GSH-Px catalyzes the oxidation of reduced glutathione and allows for the reduction of hydrogen peroxide to water, preventing lipid peroxidation and cell damage \[[@B31],[@B83],[@B85]\]. Oxidized glutathione (GSSG) is reduced to GSH, a reaction catalyzed by glutathione reductase (GSH-Rx). Mercury or copper, shown as M^n^in Figure [2](#F2){ref-type="fig"}, can disrupt the GSH system, leading to cellular insult and potential apoptosis as a result of the generation of hydrogen peroxide \[[@B86]\]. Results from the recent study by Chen et al. of mercury-exposed humans suggest that mercury affects the bioavailability and retention of selenium and interferes with the metabolic processes dependent on selenium \[[@B31]\]. The researchers found that the serum selenium concentrations associated with GSH-Px and selenoproteins were 2 times higher in the mercury-exposed group than the control group \[[@B31]\]. A disruption of the GSH system by mercury leads to GSH depletion and cell destruction. An *in vitro*study of Jurkat T cells exposed to thiomersal demonstrated concentration-dependent apoptosis \[[@B87]\]. It was found that the mercury moiety, not the thiosalicylic acid moiety, of thiomersal was responsible for glutathione depletion \[[@B87]\]. GSH depletion is linked to several neurodegenerative disorders \[[@B88]\]. James et al. found lower concentrations of total GSH in the plasma of 20 autistic children compared to 33 control children without autism in a study published in 2004 \[[@B80]\]. In a recent study published in 2009, Sitta et al. found erythrocyte and plasma glutathione levels were significantly reduced in phenylketonuric patients compared to a control group \[[@B89]\]. When the GSH system is upset by the presence of mercury, changes in the immune system occur. Mercury exposure activates the glucocorticoid anti-inflammatory response \[[@B90],[@B91]\]. Glucocorticoids enhance the neurotoxicity of reactive oxygen species by decreasing the activity of glutathione peroxidase. This results in a negative feedback loop that depletes GSH and exacerbates the apoptotic process \[[@B92],[@B93]\] (Figure [2](#F2){ref-type="fig"}). The glucocorticoid anti-inflammatory response also induces MT expression in the presence of mercury \[[@B91],[@B93]-[@B96]\].

![**Oxidation and Reduction of the Glutathione System**.](1744-9081-5-44-2){#F2}

Metallothionein: a protein for metal elimination
------------------------------------------------

MTs represent a family of ubiquitous, low-molecular-weight, metal-binding proteins that have a molecular mass of less than 7,000 Daltons, containing approximately 33-percent cysteine residues \[[@B94]\]. Due to their binding capacity (i.e., up to seven zinc or 12 copper ions per molecule) and ubiquitous nature, it has been suggested that MTs may engage in essential metal trafficking to and from many metal-dependent proteins \[[@B91]\]. MTs are involved in many important processes such as the regulation of zinc homeostasis, necessary for proper enzyme function \[[@B95]-[@B97]\]. Although as many as 18 different metals may be associated with an MT molecule, only copper, cadmium, lead, silver, mercury, and bismuth are capable of displacing a zinc atom \[[@B94]\]. All of these known environmental toxicants cause MT expression \[[@B94]\]. MTs are among the body\'s primary protective molecules against toxic metals \[[@B68],[@B98]\]. In mice, MT provides a survival benefit in stressful situations, such as heavy metal and free radical exposure, inflammation, and zinc or other nutrient deficiency \[[@B94]\]. The protective effect of MT against the toxicity of cadmium and mercury vapor has been the most studied thus far \[[@B99]-[@B102]\].

Besides toxic metal exposure, the MT gene family is also induced by bacterial cell wall lipopolysaccharides and inflammatory cytokines (such as interleukin-1beta), collectively playing a major neuroprotective role against these various environmental insults. Constitutive cellular abundance of MTs -1, -2, -3 and -4 (the latter specifically localized to stratified squamous epithelium) are therefore an important index of a tissue\'s susceptibility, and capability to handle a wide array of toxic insults. As the nasal cavity, primary olfactory neurons at the cribiform plate, and anatomically connected olfactory bulb are continually exposed to various airborne pollutants in the external environment, the olfactory system provides a direct route of entry into the central nervous system for an assortment of environmental neurotoxic agents including mercury. It is therefore not surprising that the olfactory system has one of the highest MT abundances of the mammalian central nervous system, and that expression of the MT-1, -2, and especially -3 isoforms are greatest in the olfactory neurons of the brain and the anatomically adjacent hippocampal region, which has direct nerve fiber projection contacts from the olfactory bulb \[[@B94],[@B101],[@B103]-[@B105]\]. In mercury vapor intoxicated mice, MT-3 expression was found to dominate in the olfactory cells of the olfactory mucosa as well as in neurons of the olfactory bulb, while MT-1 and -2 immunoreactivity predominated in supporting basal and acinar cells of the Bowman\'s gland of the olfactory mucosa \[[@B101]\].

MT-3 is a brain specific, high zinc containing MT, which is found in neuronal and glial cells \[[@B94],[@B101],[@B104]-[@B107]\]. Researchers examined 503 patients with autistic spectrum disorders and found lower levels of MT with significantly higher copper: zinc ratios in the autistic group compared to healthy age- and gender-matched controls \[[@B77]\]. Three peer reviewed papers published in 2009 report findings that support the link between low-dose mercury exposure and metallothionein dysfunction and an association with ASD \[[@B62]-[@B64]\]. It has been theorized that MTs may serve as a redox control and the fact that MT-3 is relatively high in zinc and is associated with neurons provides evidence that it fills a role as an oxidation-reduction buffer in neuronal tissue. Importantly, the MT protein in the immune system may not function properly if either zinc or methionine are deficient in the diet \[[@B108],[@B109]\]. Consumption of any substance leading to zinc loss and copper gain (e.g. HFCS) or zinc deficiency (e.g. tartrazine and sunset yellow food dye) should be avoided by sensitive populations such as children diagnosed with ASD, ADHD or found to be zinc deficient.

Fatty acids and neuronal functioning
------------------------------------

Omega-3 fatty acids have been found to be a basic component of normal neural development and maintenance of neural plasticity \[[@B110]\]. Dietary sources of omega-3, however, are not plentiful in plant foods with the exceptions of flax (*Linum usitatissimum)*and Chia (*Salvia hispanica*) seeds, and are found in small amounts in walnuts, soybeans, canola oil, and free-range chicken eggs \[[@B110]\]. The only rich sources of omega-3 fatty acids are cold water fish (e.g., anchovies, krill, herring, tuna, mackerel, salmon, and sardines), cod liver oil, and other fish oils. Deficiencies of two important omega-3 fatty acids, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), commonly lead to adverse health effects and have been found in children with behavior disturbances \[[@B111],[@B112]\].

The role of DHA in brain development and visual acuity has been extensively studied \[[@B113]\]. The breast milk of nursing mothers contains adequate amounts of DHA if dietary intake is sufficient \[[@B113]\]. There is a preponderance of evidence that shows infants fed unfortified formula have poorer vision and lower IQs than infants fed formula fortified with DHA \[[@B113]\]. In 1993, the WHO recommended that pre-term and term infant formulas include DHA and in 2001, FDA allowed a manufacturer\'s self-declaration that DHA and arachidonic acid (AA) were generally recognized as safe (GRAS) for use in infant formula \[[@B114]\]. In 2002, infant formula manufacturers began marketing infant formulas containing DHA and AA in the US.

DHA protects neurons and glia from death, in part, by maintaining BDNF, which is a small protein, made within the brain that is crucial for maintaining neuronal plasticity \[[@B115],[@B116]\]. Wu et al. \[[@B117]\] found that dietary omega-3 fatty acids in the form of DHA not only normalize BDNF but also reduce oxidative damage and counteract learning disability after traumatic brain injury in rats. Oxygenated DHA-derivatives, including a recently described form of modified DHA called neuroprotectin D1, have been shown to promote brain cell survival via the induction of highly specific brain gene expression programs that confer strong resistance to apoptosis and potent neuroprotection \[[@B118]\].

Researchers have been investigating the link between essential fatty acids and ADHD in children. Stevens et al. \[[@B119]\] found that 53 children with ADHD had significantly lower plasma levels of omega-3 fatty acids than did the control group of 43 children. The authors noted that the children with the worst symptoms of essential fatty acid deficiency increased thirst, frequent urination, dry hair and skin - had the lowest plasma levels of omega-3 fatty acid levels. Another study, conducted by Mitchell et al. \[[@B120]\], compared 44 hyperactive children with 45 age- and sex-matched controls. DHA levels were significantly lower in the hyperactive children, who also had auditory, visual, language, reading, and learning difficulties, and lower average birth weight compared to controls. Colter et al. \[[@B121]\] also found low levels of DHA and total omega-3 fatty acids in ADHD children compared with controls. In an effort to raise DHA levels, Voight et al. \[[@B122]\] administered 345 mg per day of DHA to 27 children with ADHD without any significant improvement in behavior compared to the 27 children who served as controls. Hirayama et al. \[[@B123]\] used a higher dose of DHA (514 mg per day) than the previous study and also found no improvement in the DHA group.

Despite the fact that low levels of DHA were found in children with ADHD, supplementing with DHA alone or in high amounts relative to other essential fatty acids (EFAs) have not been shown to be effective in improving behavior. Researchers have begun to look at other fatty acids and found that a combination of EFAs and in particular EPA may be more effective in the modulation of behavior disorders. Forty-one children with learning difficulties were randomly assigned to supplementation with EPA 186 mg, DHA 480 mg, gamma linolenic acid (GLA) 96 mg, linoleic acid 864 mg, and AA 42 mg or placebo (olive oil) for 12 weeks \[[@B51]\]. The group receiving the fish oil supplement had significant improvements in learning and behavioral scores and other ADHD-related symptoms compared to the group given olive oil \[[@B51]\]. (Figure [1](#F1){ref-type="fig"}.) In a later study, 117 children with developmental coordination disorder were given either 558 mg EPA, 174 mg DHA, and 60 mg GLA or placebo \[[@B124]\]. The children receiving the fish oil supplement demonstrated significant improvements in reading, spelling, and behavior after three months of treatment compared to placebo \[[@B124]\]. In an open-label pilot study, Sorgi et al. \[[@B125]\] gave a high dose EPA/DHA supplement containing 10.8 g of EPA and 5.4 g of DHA per day to 9 children for eight weeks. They found significant improvements in behavior including inattention, hyperactivity, oppositional behavior and conduct disorder. Sinn and Bryan \[[@B126]\] administered 558 mg EPA, 174 mg DHA and 60 mg GLA/day to 36 children for 15 weeks and improvements were found for inattention and hyperactivity and impulsivity compared with the control group. Recently both Johnson et al. \[[@B127]\] and Belanger et al. \[[@B128]\] found improvement in inattention in a subgroup of children with ADHD when supplemented with a mixture of these essential fatty acids.

EPA and DHA are both essential for optimal brain function, but for different reasons. DHA is important for the structure of the neuronal membranes, while EPA plays a role in brain function by regulating neuronal signaling, neurotransmitter uptake, and the activity of phospholipase enzymes. EPA has been shown to reduce the overactivity of phospholipase A~2~which in excess can deplete the phospholipids of the long chain polyunsaturated fatty acids critical to normal brain function. EPA may be a useful adjunct in the treatment of various neurological conditions including depression, bipolar disorder, schizophrenia, dyslexia, dyspraxia, ADHD, and Autism Spectrum Disorders \[[@B112],[@B129],[@B130]\].

Magnesium and zinc are needed to help convert the 18-carbon, plant-derived essential fatty acids (EFAs) to long chain fatty acids, notably DHA and EPA \[[@B131]\]. Many children with ADHD are deficient in these nutrients and may therefore have difficulty elongating the 18-carbon fatty acids \[[@B132]\]. In a sample of 116 children with ADHD, researchers found that 95 percent had a magnesium deficiency \[[@B133]\]. In 1997, researchers found that a combination of magnesium aspartate and magnesium lactate supplemented at a dose of 200 mg daily for six months significantly reduced disruptive behavior in children with ADHD compared to a control group \[[@B134]\]. Galland \[[@B135]\] found an impaired desaturation of polyunsaturated fatty acid in a magnesium compromised population. This desaturation enzyme is magnesium dependent. Mahfouz et al. \[[@B136]\] found a significant reduction in long chain polyunsaturated fatty acids in magnesium deficient cells pointing to an impairment of delta 5 and/or delta 6 desaturase enzymes. It is possible that correcting a magnesium deficiency may restore desaturase enzyme function. Researchers also found that deficient levels of zinc are more commonly found in children with ADHD and supplementing these children with zinc significantly reduced hyperactivity, impulsivity, and impaired socialization scores, especially in children with low levels of EFAs \[[@B137],[@B138]\].

Children who have difficulty converting EFAs to long chain fatty acids can obtain a preformed source of EPA and DHA as a nutritional supplement or in the diet by consuming fish or fish oil. As discussed above, cold-water fish provide the only rich dietary source of these omega-3 fatty acids; however, recent research on trace metal contamination has questioned their safety. Guallar et al. \[[@B139]\] found, for instance, that the cardio protective benefits of fatty fish may be compromised by their high mercury content. Canadian researchers found a significant association between high levels of mercury and EPA in the blood of children (consistent with high levels of fish consumption) \[[@B140]\]. In addition, the researchers found a statistically significant inverse association between attentional focusing and blood mercury levels in children under age three years of age \[[@B140]\]. A careful selection of fish with low mercury and high EFA content, such as salmon and sardines, is essential in minimizing exposure to mercury while increasing consumption of these important EFAs. With respect to the consumption of fish oil or other supplements that are fish-derived, caution is warranted, as mercury contamination may be possible. Out of 100 samples of fish oil dietary supplements analyzed for mercury by the Food Standards Agency in the United Kingdom, nine were found to contain mercury above the limit of detection of 0.0014 mg/kg \[[@B141]\].

Key issues related to mercury exposure guidelines
=================================================

While the total dietary intake of mercury is not known, mercury is widely accepted to be an unusually toxic heavy metal and the American Academy of Pediatrics has recommended that minimizing exposure to any form of mercury is essential for optimal child health \[[@B32]\]. The current recommendation for \"safe\" mercury consumption is based on a \"reference dose\" or RfD from fish consumption and only applies to methyl mercury (MeHg). The USEPA and FDA define the RfD as \" \[a\]n estimate (with uncertainty spanning perhaps an order of magnitude) of a daily oral exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime.\" The current official RfD for methyl mercury in the US is set at 1 ppm although EPA has historically pushed for a lower reference dose of 0.1 ppm. Unfortunately, this official \"reference dose\" of 1 ppm does not take into consideration what the human body needs nutritionally to successfully metabolize and excrete methyl mercury, and it only applies to MeHg exposure.

With regard to total mercury exposure, the Joint Expert Committee on Food Additives (JECFA) recommends an exposure limit of 1 ppm for mercury in their 2005 Evaluation of Food Additives report \[[@B142]\]. This report does not specifically address the mercury content in the food color additives tartrazine also known as FD&C Yellow 5 or sunset yellow also known as FD&C Yellow 6 but these chemicals are manufactured with the chlor-alkali product \"hydrogen chloride\" and may not contain more than 1 ppm mercury according to US Food and Drug Administration regulations \[[@B143]\]. In addition to causing zinc deficiency, both of these food color additives have been linked to hyperactivity in children \[[@B71]\] and the United Kingdom has asked manufacturers to voluntarily ban their use in food products \[[@B144]\].

Etiology of ASD and ADHD
========================

In 2007 alone, there were 1000 studies published on all aspects of ASD \[[@B145]\]. As is the case with ADHD, there are likely unknown and unmeasured variables associated with the development of ASD. Disease related and/or exposure related bias arises when there is a causal or associational path of no interest to the researchers \[[@B146]\]. In this review article we have clearly not identified all of the possible variables associated with the development of these two disease conditions. The development of ASD and ADHD likely involve a broad range of genetic, prenatal, social, developmental, nutritional and environmental factors \[[@B145],[@B147]\]. We have focused primarily on some of the nutritional factors that may be considered along with environmental mercury exposure in the development of treatment plans for patients with ASD or ADHD. Multiple treatment modalities are most likely needed to treat these patients successfully \[[@B147]\].

Conclusion
==========

Adequate nutrition, specifically omega-3 fatty acids, methionine, and the trace minerals zinc and selenium are important for maintaining neuronal plasticity and learning capacity. The evidence does suggest that improper or inadequate nutrients may lead to behavior and learning disorders when mercury is present in the environment. Because current standards allow the use of mercury cell chlor-alkali chemicals in food manufacturing, food products that are made with one or more of these ingredients may contain trace amounts of mercury such as is the case for HFCS and other food additives. These low-level mercury exposures may contribute to cumulative environmental mercury exposure and present a problem for sensitive individuals who do not have adequate levels of glutathione and metallothionein and/or are deficient in the trace minerals needed for metal metabolism. Because consumption of HFCS and other food additives may lead to trace mineral imbalances and/or zinc deficiency, further research is needed to determine whether or not these food ingredients are safe for consumption by sensitive populations. If food manufacturers continue to use mercury grade chlor-alkali chemicals, it may be necessary to account for this source of mercury exposure in dietary guidelines for sensitive populations such as children with ADHD and autism spectrum disorders. In addition, consideration of dietary micronutrients required to enhance neurological development and support proper functioning of metallothionein, glutathione peroxidase, and other metal clearing and detoxifying mechanisms in the brain seems warranted.

List of abbreviations
=====================

ASD: Autism Spectrum Disorder; ADHD: attention deficit hyperactivity disorder; T3: tri-iodothyronine; US: United States; USEPA: United States Environmental Protection Agency; WHO: World Health Organization; FDA: Food and Drug Administration; MeHg: methylmercury; NHANES: National Health and Nutrition Examination Survey; GSH-Px: glutathione peroxidase; HFCS: high fructose corn syrup; ug: micrograms; MT: metallothionein; BDNF: brain-derived neurotrophic factor; GSH: glutathione; GSSG: oxidized glutathione; GSH-Rx: glutathione reductase; EPA: eicosapentaenoic acid; DHA: docosahexaenoic acid; AA: arachidonic acid; GRAS: generally recognized as safe; GLA: gamma linolenic acid; EFAs: essential fatty acids; mg/kg: milligrams/kilogram; RfD: reference dose; ppm: parts per million; JECFA: Joint Expert Committee on Food Additives.

Competing interests
===================

The authors declare that they have no competing interests.

Authors\' contributions
=======================

RD spearheaded the review and recruited interdisciplinary collaborators to contribute to the development of the manuscript. RD was the lead environmental investigator and literature reviewer for the sources of mercury exposure section. LP and BL both contributed greatly to the development of the amino acids and glutathione system section. WL was the lead investigator and reviewer for the metallothionein section. RS was the lead investigator and reviewer for the fatty acids section. RC reviewed, analyzed, and prepared the epidemiological data. CC, DW, and SG were instrumental in editing the manuscript and reviewing it critically for intellectual content. All authors read and approved the final manuscript.

Acknowledgements
================

The authors would like to thank Dr. William Walsh for his kind feedback on the role of micronutrients in learning disorders, and Dr. Howard Hu, Dr. Lori Brown, Dr. Laura Raymond, Dr. Rao Ivaturi, Dr. Peter Green, Dr. Kathryn Mahaffey, and Dr. Martha Herbert for their helpful reviews over the years.


APTA is very encouraged by the proposal within President Obama's Fiscal Year 2014 budget, released yesterday, to exclude therapy services, including physical therapy, along with radiation therapy and advanced imaging, from the in-office ancillary services (IOAS) exception of the Stark self-referral laws. APTA has long supported and worked to advocate for exclusion of physical therapy from the IOAS exception, and we consider this a significant step in the right direction.

The spirit of the Stark self-referral IOAS exception is intended for the delivery of services that could be quickly administered for patient convenience, such as routine lab tests or x-rays. The expansive use of the IOAS exception by physicians in a manner not originally contemplated by the law undercuts its very purpose and substantially increases costs to the Medicare program and its beneficiaries. The Office of Management and Budget concluded that closing the loophole for these services would provide a savings of $6.1 billion over the standard 10-year budget window, providing further evidence that these self-referral arrangements lead to overutilization of Medicare services and should be addressed by Congress.

APTA is a founding member of the Alliance for Integrity in Medicare (AIM), a consortium of organizations that advocates for Congress to address the IOAS loophole. For many years, APTA has had a presence on Capitol Hill, working to overturn policies that allow self referral to continue. More recently, the Association and its AIM Coalition partners have advocated for the better part of 18 months to encourage the identification of cost savings associated with closing the loophole. APTA agrees with the Administration's proposal on physician self-referral and believes this issue should be addressed as part of any fundamental delivery system reform.

APTA and its AIM partners continue to be gravely concerned about the ongoing misapplication of the IOAS exception to the physician self-referral law, believing this loophole results not only in increased spending, but also in unnecessary use of medical services, and, more important, potentially compromised patient choice and care. Studies published by the New England Journal of Medicine, Health Affairs, and the Government Accountability Office, among others, have highlighted abuses that result from physician self-referral. These ongoing issues serve only to erode the integrity of the Medicare program and undermine patient care.

We strongly urge Congress to follow the recommendations laid out in the Administration's budget and pass legislation to remove physical therapy, advanced diagnostic imaging, anatomic pathology, and radiation therapy from the IOAS exception in the 113th Congress.