ESTIMATING POTENCY SCORES: AN EXERCISE Joyce Morrissey Donohue Ph. D., R. D. US EPA, Office of Water, Office of Science and Technology, Health and Ecological Criteria Division 8/ 26/ 04 i TABLE OF CONTENTS ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 PROCEDURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Figure 1. Decile Distribution of RfD Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Figure 2. Logarithmic Distribution of RfD Values . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Table 1. Scoring Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Table 2. Potency Scores for Chemicals in the Learning Set . . . . . . . . . . . . . . . . . . . 7 Table 3. Potency Scores for Chemicals Not in the Learning Set . . . . . . . . . . . . . . . . 8 RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 APPENDIX I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A1 Figure 3. Logarithmic Distribution of NOAEL Values . . . . . . . . . . . . . . . . . . . . . A2 Figure 4. Logarithmic Distribution of LOAEL Values . . . . . . . . . . . . . . . . . . . . . A3 Figure 5. Logarithmic Distribution of LD50 Values . . . . . . . . . . . . . . . . . . . . . . . . A4 Figure 6. Logarithmic Distribution of Cancer Potency Values . . . . . . . . . . . . . . . A5 1 ESTIMATING POTENCY SCORES: AN EXERCISE.

ABSTRACT The Office of Water ( OW) has developed and tested a potential approach to score or rank the cancer and non cancer potencies of potential drinking water contaminants. This approach uses measured or modeled values of potency [ Reference Dose ( RfD), No Observed Adverse Effect Level ( NOAEL), Lowest Observed Adverse Effect Level ( LOAEL), LD 50 and 10  4 ( one in ten thousand) estimate of cancer risk]. The scoring for each of these parameters was calibrated based on data from a set of 200 known drinking water contaminants and chemicals generally recognized as safe. The distribution of the RfD, NOAEL, LOAEL, LD 50, and 10  4 risk values for this set of chemicals was similar to a normal distribution curve when log transformed ( i. e., arranged by powers of 10). The distribution was used to develop a scoring equation for each potency parameter by equating a value of five to the " most frequent" power of ten in the distribution. Scores were fairly consistent across related parameters for individual chemicals. Agreement across scores was weakest for the weakly toxic chemicals. [ The opinions expressed represent those of the author and do not necessarily reflect the opinions of the U. S. Environmental Protection Agency.]

1.0 BACKGROUND The 1996 Safe Drinking Water Act requires EPA to develop a list of contaminants with the potential for regulation on a five year cycle. This list, known as the Contaminant Candidate List ( CCL), serves as the primary source of the priority contaminants for the EPA's drinking water program. The CCL also identifies the group of contaminants from which the Agency will make regulatory determinations three and one half years after publication of the list.

After publication of the first CCL, EPA requested assistance from the National Research Council ( NRC) of the National Academy of Sciences in providing suggestions for a comprehensive, transparent process to prioritize and select contaminants for inclusion on the CCL. The NRC recommendations were published in 2001. They provide a framework for the process in which potential contaminants are initially selected from a large universe of contaminants of concern with a preliminary screen based on occurrence and health effects information. The CCL is chosen from these screened contaminants considering scored attributes for health effects ( potency and severity) and occurrence ( prevalence, magnitude, persistence, and mobility). The data requirements for the selection of the CCL provide a higher degree of scrutiny than those used for the preliminary screen. This exercise uses the NRC recommended framework to evaluate and score the relative potency of contaminants based on toxicity parameters.

2.0 INTRODUCTION 1 An RfD is an estimate ( with uncertainty spanning perhaps one order of magnitude) of a daily exposure to humans ( including sensitive individuals) that is likely to be without appreciable risk of deleterious effects during a lifetime. It is expressed in mg/ kg/ day. ATSDR lifetime MRLs, WHO TDIs, WHO and FDA ADIs, and nutrient Tolerable Upper Limit Values ( ULs) are roughly equivalent to RfDs.

2 For this exercise cancer potency was evaluated as the concentration in drinking water equivalent to an excess cancer risk of one case in 10,000 ( 10 4). This value is given in the OW Drinking Water Standards and Health Advisory Tables and also is included in all IRIS Summary documents. When the 10 4 risk value is not available, it can be calculated from a cancer slope factor.

2 Potency is a value that indicates the power of a contaminant to cause adverse health effects. In the case of chemicals, that power is apparent in the dose required to cause the most sensitive manifestation of an adverse health effect, or to generate a particular excess cancer risk. Potency for chemicals is reflected in several standard toxicological parameters which are discussed below.

There are a number of approaches that have the potential to be useful in scoring potency. However, no matter which approach is selected, the methods will require calibrating the scores to normalize the scale. There must be some agreement about the types of data that will be used in the scoring algorithm and the magnitude of the potency value that represents a given number on the potency scale. With this in mind, an initial " learning set" of about two hundred chemicals was developed in order to make a preliminary assessment of one calibration approach. The chemicals considered included:

° Regulated chemicals, ° Unregulated chemicals with lifetime health advisories, and ° A small group of chemicals ( nutrients/ food additives) which are generally considered as relatively non toxic and which have toxicity values similar to lifetime health advisories.

The following toxicity parameters were collected for the learning set chemicals, and their numeric distribution across the range of values were examined ( see the footnotes below for definitions of the terms).

° RfD 1 or equivalent ° Cancer potency2 ( 10 4 risk concentration in water) 3 NOAEL is a No Observed Adverse Effect Level. It is the highest dose in a toxicological study or a group of studies that has no observed adverse effect.

4 LOAEL is a Lowest Observed Adverse Effect Level. It is the lowest dose in a toxicological study or a group of studies that causes an adverse health effect.

5 A oral LD 50 is an estimate of the oral dose that will cause the death of 50% of the exposed animals. LD 50 data are based on acute exposures with limited post exposure observations of the animals for mortality, clinical signs, and gross pathology.

6 The 2002 Edition of the Drinking Water Regulations and Health Advisories was used for the RfD and 10 4 risk values.

3 ° NOAEL3 and/ or LOAEL4 associated with the RfD ° Rat oral LD 50 5 values.

Several approaches to characterize the distribution of values for the different toxicity parameters were employed in this exercise. The approaches are described in the Procedure section below.

The toxicity parameters listed above were selected because they were readily available on the Integrated Risk Information System ( IRIS), in OW health advisory documents, or in the Institute of Medicine ( IOM) Dietary Reference Intake ( DRI) documents for nutrients. They also were selected because they are the values that are most likely to be found in readily accessible sources such as IRIS, or to be amenable to evaluation using Quantitative Structure Activity Relationship ( QSAR) programs.

3.0 PROCEDURE The data for the learning set were obtained from the following sources:

° Integrated Risk Information System ( IRIS) ° Office of Water Health Advisory Documents6 ° Registry of Toxic Effects of Chemical Substances ( Mostly LD 50 values) ° Tolerable Upper Levels from the Institute of Medicine Dietary Reference Intakes.

Once the data had been collected, they were graphically distributed across the range in two ways. For the initial evaluation, the range was divided into approximately ten equal units ( deciles). This distribution was found to be highly skewed with a large majority of the values falling in the decile of highest toxicity ( see Figure 1 for an example). Two factors influenced this situation. The first factor is the fact that the range of values was up to twelve orders of magnitude for the parameters evaluated. The second factor is the fact that the set of contaminants contained both toxic chemicals as well as those generally regarded as safe. In general, there are far more toxicological data available in the literature on chemicals considered to be toxic than for 4 0 20 40 60 80 100 120 140 160 0 <= 0.1 > 0.1 0.2 > 0.2 0.3 > 0.3 0.4 > 0.4 0.5 > 0.5 0.6 > 0.6 0.7 > 0.7 0.8 > 0.8 0.9 > 0.9 RfD Frequency those, like the nutrients, that are only weakly toxic. It is difficult to identify standard toxicity values for most chemicals that are generally regarded as safe. As a result, the second distribution evaluated was based on logarithms ( base 10) of the toxicity parameters rounded to the nearest integer. This provided a far better distribution of toxicity values across the range ( see Figure 2 as an example) and was used for subsequent calibration of the data for scoring.

Figure 1. Decile Distribution of RfD Values 5 0 10 20 30 40 50 60 70 80 <=  6  5  4  3  2  1 0 1 2 3 4 More Round( Log10( RfD)) Frequency Figure 2. Logarithmic Distribution of RfD Values 6 The distribution that provided the best spread of the scores ( the log based distribution) was used to establish a scoring equation for potency for each measure of toxicity. This was accomplished by assigning the most frequent ( modal) value in the distribution a score of 5 on a 10 point scale and solving the equation for the variable that would make that distributional value equal a score of 5. For example, in Figure 2, the most frequent value is a rounded logarithm of  2 ( 0.01). The scoring equation for the RfD values was the developed as follows:

5 = 10  ( most frequent rounded log + X) 5 = 10  ( 2 + X) 5 = 10 + 2  X 5 = 12  X 5  12 =  X  7 =  X 7 = X Accordingly the equation for scoring the RfD values is Score = 10  ( rounded log of RfD + 7)

The scoring equations for the other measures of toxicity were derived from the modal rounded logarithm values of their distributions in a similar fashion. The resultant equations are summarized in Table 1. The distributions of the rounded logarithms are attached to this report as an Appendix.

Table 1. Scoring Equations RfD Score = 10  ( Log10 of RfD + 7)

NOAEL = 10  ( Log10 of NOAEL + 4)

LOAEL = 10  ( Log10 of LOAEL + 4)

LD50 = 10  ( Log10 of LD50 + 2)

10 4 cancer risk 1 = 10  ( Log 10 of the 10 4 cancer risk + 6)

1. The 10 4 cancer risk in water was selected as the measure of potency for carcinogens because this is the value given in the Drinking Water Regulations Health Advisory Tables prepared by OW and also is provided in IRIS Summaries.

Scores were restricted to whole number values with a maximum of 10 and a minimum of 1 7 All of the chemicals in the learning set were scored for each toxicity parameter to examine the consistency across scores for the non cancer measures of potency. Some examples of this evaluation are provided in Table 2. Since the mechanisms that lead to the development of cancer involve some biological responses that are unique to tumors, the 10 4 cancer risk values were not included in this comparison. The scores for individual chemicals were compared across the toxicity values, and the agreement between scores was evaluated.

Table 2. Potency Scores for Chemicals in the Learning Set Chemical RfD NOAEL LOAEL LD50 Arsenic ( Sodium Arsenite for LD 50) 7 9 8 6 Calcium ( Calcium chloride for LD 50) 1 ND 4 5 Cyanazine 6 6 6 6 Dioxin ( 2,3,7,8 TCDD) 10 ND 10 4 Heptachlor 8 ND 8 7 Hexazinone 4 5 4 5 Iodine ( Sodium iodide for LD 50) 5 8 8 4 Methyl Ethyl Ketone 3 3 3 5 MethylParathion 7 8 7 7 Naphthalene 5 4 4 5 Paraquat 5 6 6 6 Phenol 4 4 4 5 Phosphorous ( Sodium Hydrogen Phosphate for LD 50) 1 4 ND 4 Vitamin D 6 9 9 ND ND = No data In addition, the scoring equations were applied to chemicals that were not in the learning set using data available in Agency of Toxic Substance and Disease Registry ( ATSDR) Toxicological Profiles. Those results are summarized in Table 3. Scores were evaluated for consistency across parameters. 8 Table 3. Potency Scores for Chemicals Not in the Learning Set Chemical/ Potency Scores RfD equivalent ( mg/ kg/ day) NOAEL ( mg/ kg/ day) LOAEL ( mg/ kg/ day) LD50 ( mg/ kg)

Acrylonitrile 0.04 4.2 14 93 Scores 4 5 5 6 Ethion 0.0004 0.06 0.71 27 Scored 6 7 6 6 Malathion 0.02 2 29 925 Scores 5 6 5 5 Endofulfan 0.002 0.18 ND 1740 Scores 6 7 ND 5 ND = No Data 4.0 RESULTS As mentioned in the Procedure section above, the decile distribution was found to be undesirable in developing an equation for scoring potency because almost all of the chemicals are clustered at one end of the distribution. This does not provide good distributions of scores. With the decile distribution, almost all of the chemicals in the learning set would have a high potency score of 10. Very few chemicals would have lower scores. The distribution based on the rounded Log10 of the toxicity parameter was found to be far superior to the decile distribution because toxicity parameters for different chemicals are spread out across the range and the most frequent Log10 is approximately in the middle of the range making the curve roughly normal. It was for this reason that the Log10 distribution was selected for development of the scoring equation. The distribution of toxicity values is still somewhat skewed toward higher toxicity scores; however, this is a product of a limited number of weakly toxic chemicals that were included in the learning set.

Some distributions for toxicity parameters span a range greater than ten orders of magnitude ( see Figure 2). It was decided that in cases where the range of toxicity parameters was greater then ten, a calculated score less than 1 would be given a score of 1 and anything with a calculated score greater than 10 would be given a score of 10. This is because these chemicals are at the tails of the distributions. Conversely, for the distributions that covered less than 10 orders of magnitude ( see the LD50 distribution in the Appendix), no attempt was made to normalize the scores across a range of ten because the learning set is limited and could have been expanded by searching for chemicals that are more toxic than dioxin ( the most toxic substance in 9 the learning set with an RfD of 1 x 10 9 mg/ kg/ day) and less toxic than phosphorous ( the least toxic chemical in the learning set with an RfD equivalent of 57 mg/ kg/ day derived from the IOM UL).

The distribution for cancer is the most skewed of those examined because there are a greater number of chemicals that are more potent carcinogens than those in the modal grouping than there are those that are less potent. This in not unusual when one takes into account the fact that cancer bioassays are very costly and there is a greater incentive to invest resources in studying chemicals that have a high likelihood of being potent carcinogens than those that are not. No attempt was made to normalize the cancer scores across a range of 10. For the chemicals in the learning set, the lowest cancer potency score is a 3.

When the agreement of non cancer scores across the RfD, NOAEL, LOAEL and LD 50 inputs was evaluated, the scores for any given compound were found to be generally consistent across parameters. There were 216 chemicals in the learning set; 13.5% of those with mutiple noncancer scores had identical scores across all parameters ( see cyanazine in Table 2). For 54.6%, the scores deviated by 1 integer ( see hexazinone in Table 2); 20.5% deviated by 2 integers ( see methyl ethyl ketone in Table 2). There was a 3 integer deviation for 9.7%, and the majority of those were inorganic compounds ( see Arsenic [ sodium arsenate] and iodine [ sodium iodide] in Table 2). Only 1.6% deviated by more than 3 integers ( see dioxin in Table 2). Scores deviated by two integers or less for 88.6 % of the chemicals. The differences between scores for a given compound was greatest for the relatively non toxic chemicals ( see phosphorous/ sodium hydrogen phosphate in Table 2). In almost all cases the NOAEL and LOAEL scores were higher than the RfD score, effectively negating the claim that the inclusion of uncertainty factors in the calculation of the RfD would inflate the potency score. For those chemicals with low uncertainty factors the NOAEL or LOAEL scores were often 3 or more integers higher than the RfD scores ( see calcium chloride and vitamin D in Table 2).

Since most chemicals with RfD values also are likely to have NOAEL, LOAEL, and/ or LD 50 values, a policy decision is needed with regard to how one should select the parameter used to score for a noncancer endpoint. There are a number of options. One could select the lowest or highest of a suite of scores, or develop a hierarchy for choosing between RfD, NOAEL, and LOAEL. It was been suggested by those who worked on this project that the LD 50 only be used when NOAEL and/ or LOAEL values are not available since it is a measure of acute rather than chronic toxicity. When comparing cancer and noncancer scores, the work group suggested that whichever one provides the highest measure of potency be selected.

The scoring equations were easily applied to chemicals not in the learning set. The calculated scores were reasonable based on the toxicity of the chemical ( Table 3) and provide support for the proposed approach to scoring potency. However, the purpose for this exercise was merely to explore a method for scoring potency by utilizing several standard toxicological parameters that are utilized for quantitative risk assessment of chemicals. Possibilities exist for further expanding the exercise by: 10 ° Expanding the learning set ° Examining other approaches to using distributions of the learning set data to calibrate scoring and comparing the results. ° Doing additional testing of the calibrated scoring methodology with chemicals that are not in the learning set.

This exercise did not consider the severity of the cancer or noncancer effect identified by the RfD, LOAEL, or cancer risk. OW is examining possible methods for scoring severity as part of a separate exercise.

ACKNOWLEDGMENT: Greg Blumenthal and Katherine Sullivan of ICF Consulting Inc, Fairfax VA provided technical support for this project in collecting and processing the data. APPENDIX I DISTRIBUTIONS OF THE ROUNDED LOGARITHMS A2 0 5 10 15 20 25 30 35 40 45  5  4  3  2  1 0 1 2 3 4 5 More Round( Log10( NOAEL))

Frequency Figure 3. Logarithmic Distribution of NOAEL Values A3 0 5 10 15 20 25 30 35 40 45 <=  5  4  3  2  1 0 1 2 3 4 5 More Round( Log10( LOAEL))

Frequency Figure 4. Logarithmic Distribution of LOAEL Values A4 Figure 5. Logarithmic Distribution of LD50 Values A5 0 10 20 30 40 50 60 70 80  2  1 0 1 2 3 4 5 6 7 8 More Round( Log10( LD50))

Frequency A6 0 5 10 15 20 25 <= 6  5  4  3  2  1 0 1 2 3 4 More Round( Log10( E 4))

Frequency Figure 6. Logarithmic Distribution of Cancer Potency Values


[Federal Register Volume 81, Number 244 (Tuesday, December 20, 2016)]
[Notices]
[Pages 92889-92890]
[FR Doc No: 2016-30563]

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SECURITIES AND EXCHANGE COMMISSION

[Release No. 34-79558; File No. SR-NYSEMKT-2016-114]

Self-Regulatory Organizations; NYSE MKT LLC; Notice of Filing and
Immediate Effectiveness of Proposed Change Modifying the NYSE Amex
Options Fee Schedule

December 14, 2016.
    Pursuant to Section 19(b)(1) \1\ of the Securities Exchange Act of
1934 (the ``Act'') \2\ and Rule 19b-4 thereunder,\3\ notice is hereby
given that, on December 1, 2016, NYSE MKT LLC (the ``Exchange'' or
``NYSE MKT'') filed with the Securities and Exchange Commission (the
``Commission'') the proposed rule change as described in Items I, II,
and III below, which Items have been prepared by the self-regulatory
organization. The Commission is publishing this notice to solicit
comments on the proposed rule change from interested persons.
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    \1\ 15 U.S.C. 78s(b)(1).
    \2\ 15 U.S.C. 78a.
    \3\ 17 CFR 240.19b-4.
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I. Self-Regulatory Organization's Statement of the Terms of Substance
of the Proposed Rule Change

    The Exchange proposes to modify the NYSE Amex Options Fee Schedule
(``Fee Schedule''). The Exchange proposes to implement the fee change
effective December 1, 2016. The proposed change is available on the
Exchange's Web site at www.nyse.com, at the principal office of the
Exchange, and at the Commission's Public Reference Room.

II. Self-Regulatory Organization's Statement of the Purpose of, and
Statutory Basis for, the Proposed Rule Change

    In its filing with the Commission, the self-regulatory organization
included statements concerning the purpose of, and basis for, the
proposed rule change and discussed any comments it received on the
proposed rule change. The text of those statements may be examined at
the places specified in Item IV below. The Exchange has prepared
summaries, set forth in sections A, B, and C below, of the most
significant parts of such statements.

A. Self-Regulatory Organization's Statement of the Purpose of, and the
Statutory Basis for, the Proposed Rule Change

1. Purpose
    The purpose of this filing is to amend Section I. E. of the Fee
Schedule,\4\ effective December 1, 2016.
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    \4\ See Fee Schedule, Section I. E. (Amex Customer Engagement
(``ACE'') Program--Standard Options), available here, https://www.nyse.com/publicdocs/nyse/markets/amex-options/NYSE_Amex_Options_Fee_Schedule.pdf.
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    Section I. E. of the Fee Schedule describes the Exchange's ACE
Program. The ACE Program features five tiers, expressed as a percentage
of total industry Customer equity and Exchange Traded Fund (``ETF'')
option average daily volume (``CADV'') \5\ and provides two alternative
methods through which Order Flow Providers (each an ``OFP'') may
receive per contract credits for Electronic Customer volume that the
OFP, as agent, submits to the Exchange.
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    \5\ The volume thresholds are based on an OFP's Customer volume
transacted Electronically as a percentage of total industry CADV as
reported by the Options Clearing Corporation (the ``OCC''). See OCC
Monthly Statistics Reports, available here, http://www.theocc.com/webapps/monthly-volume-reports.
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    The Exchange proposes to make the following changes to the ACE
Program:
     First, the Exchange proposes to add a credit tier and re-
designate current Tier 1 as the ``Base Tier.'' \6\ Currently, to
achieve any credit under the ACE Program, an OFP must achieve Tier 2
(which offers an $0.18 per contract credit). To qualify for Tier 2, an
OFP must execute at least 0.75% to 1.00% of CADV or 0.35% over October
2015 CADV. The Exchange proposes a new Tier 1, for which an OFP to
qualify would have to execute at least 0.20% over October 2015 CADV.
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    \6\ The Exchange notes that that the qualification basis for the
proposed Base Tier remains the same as it is under current Tier 1
(i.e., an OFP must execute at least 0.00% to 0.75% of CADV) and
there are still no credits available under this tier.
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     Second, OFPs that qualify for proposed new Tier 1 would be
eligible to receive a $0.14 per contract credit. As with all other
current tiers of the ACE Program, the take liquidity multiplier would
also apply to proposed new Tier 1.\7\
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    \7\ See Fee Schedule, Section I. E., supra note 4 (``In
calculating an OFP's Electronic volume, each Customer order that
takes liquidity will be weighted as 50% greater (i.e., 1.5 times the
contract volume) for determining Customer Electronic ADV and Total
Electronic ADV'').
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     Third, the Exchange proposes that OFPs qualifying for new
Tier 1 would also be eligible for the ACE Initiating Participant
Rebate, which is currently available to OFPs that achieve Tiers 2-5 of
the ACE Program.\8\
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    \8\ See proposed Fee Schedule, Section I. G. at n.2 (``The ACE
Initiating Participant Rebate is applied to each of the first 5,000
Customer contracts of a CUBE Order executed in a CUBE Auction. This
Rebate is in addition to any additional credits set forth above.
Only ATP Holders who qualify for Tiers 1, 2, 3, 4 or 5 of the ACE
Program are eligible to receive the Rebate'').
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     Fourth, the Exchange proposes that OFPs that achieve Tier
2 would receive a $0.19 per contract credit on electronic Customer
Complex Orders. In this regard, the Exchange proposes to define Complex
Order in the Key Terms and Definitions section of the Fee Schedule, as
``. . . any order involving the simultaneous purchase and/or sale of
two or more different option series in the same underlying security,
for the same account, in a ratio that is equal to or greater than one-
to-three (.333) and less than or equal to three-to-one (3.00) and for
the purpose of executing a particular investment strategy, per Rule
900.3NY(e).'' OFPs that achieve Tier 2 would continue to receive a
$0.18 per contract credit on electronic Customer volume (i.e., non-
Complex Customer order flow).
    The Exchange is not proposing any other changes to the alternative
ACE Program Credit Tiers at this time.\9\
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    \9\ OFPs that achieve a qualification level in one tier, and
achieve an alternative qualification level in another tier, will
continue to be paid a credit based on the highest achieved tier. See
Fee Schedule, Section I.E., supra note 4.
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    The proposed additional Tier would not impact the Firm Monthly Fee
Cap of $100,000 per month per Firm, but the Exchange proposes to add
reference to the Base Tier in Section I of the Fee Schedule to add
clarity and transparency to Exchange fees.\10\
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    \10\ See Fee Schedule, Section I. I. (Firm Monthly Fee Cap),
supra note 4. The Monthly Firm Fee Cap decreases if Firms achieve
Tiers 2-5 of the ACE Program (i.e., greater than the Base Tier or
Tier 1).
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    The proposed modifications to the tiers of the ACE Program as well
as the additional rebate for electronic Customer Complex Orders are
designed to further encourage OFPs to direct additional order flow to
the Exchange, which additional volume and liquidity would benefit all
Exchange participants through increased opportunities to trade as well
as enhancing price discovery.
2. Statutory Basis
    The Exchange believes that the proposed rule change is consistent
with Section 6(b) of the Act,\11\ in general, and

furthers the objectives of Sections 6(b)(4) and (5) of the Act,\12\ in
particular, because it provides for the equitable allocation of
reasonable dues, fees, and other charges among its members, issuers and
other persons using its facilities and does not unfairly discriminate
between customers, issuers, brokers or dealers.
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    \11\ 15 U.S.C. 78f(b).
    \12\ 15 U.S.C. 78f(b)(4) and (5).
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    The Exchange believes that the proposed addition of new Tier 1 is
reasonable, equitable, and not unfairly discriminatory because it
provides an alternative means of achieving a rebate, which should
attract more volume and liquidity to the Exchange to the benefit of
market participants through increased opportunities to trade as well as
enhancing price discovery.
    The Exchange also believes the additional credit on Complex Orders
is reasonable, equitable, and not unfairly discriminatory, as it
provides an additional incentive to achieve the ACE Program Tier 2,
which should attract more volume and liquidity to the Exchange to the
benefit of market participants through increased opportunities to trade
as well as enhancing price discovery.
    The Exchange believes that the proposed amendments to the ACE
Program are reasonable, equitable and not unfairly discriminatory
because they would enhance the incentives to OFPs to transact Customer
orders, including Complex Orders, on the Exchange, which would benefit
all market participants by providing more trading opportunities and
tighter spreads, even to those market participants that do not
participate in the ACE Program. Additionally, the Exchange believes the
proposed changes to the ACE Program are consistent with the Act because
they may attract greater volume and liquidity to the Exchange, which
would benefit all market participants by providing tighter quoting and
better prices, all of which perfects the mechanism for a free and open
market and national market system.
    For these reasons, the Exchange believes that the proposal is
consistent with the Act.

B. Self-Regulatory Organization's Statement on Burden on Competition

    In accordance with Section 6(b)(8) of the Act,\13\ the Exchange
does not believe that the proposed rule change would impose any burden
on competition that is not necessary or appropriate in furtherance of
the purposes of the Act.
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    \13\ 15 U.S.C. 78f(b)(8).
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    The Exchange believes the proposed amendments to the ACE Program
are pro-competitive as the proposed new qualification tier and
incentive may encourage OFPs to direct Customer order flow to the
Exchange and any resulting increase in volume and liquidity to the
Exchange would benefit all Exchange participants through increased
opportunities to trade as well as enhancing price discovery.
    The Exchange notes that it operates in a highly competitive market
in which market participants can readily favor competing venues. In
such an environment, the Exchange must continually review, and consider
adjusting, its fees and credits to remain competitive with other
exchanges. Because competitors are free to modify their own fees in
response, and because market participants may readily adjust their
order routing practices, the degree to which fee changes in this market
may impose any burden on competition is extremely limited. For the
reasons described above, the Exchange believes that the proposed rule
change reflects this competitive environment.

C. Self-Regulatory Organization's Statement on Comments on the Proposed
Rule Change Received From Members, Participants, or Others

    No written comments were solicited or received with respect to the
proposed rule change.
    The foregoing rule change is effective upon filing pursuant to
Section 19(b)(3)(A) \14\ of the Act and subparagraph (f)(2) of Rule
19b-4 \15\ thereunder, because it establishes a due, fee, or other
charge imposed by the Exchange.
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    \14\ 15 U.S.C. 78s(b)(3)(A).
    \15\ 17 CFR 240.19b-4(f)(2).
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    At any time within 60 days of the filing of such proposed rule
change, the Commission summarily may temporarily suspend such rule
change if it appears to the Commission that such action is necessary or
appropriate in the public interest, for the protection of investors, or
otherwise in furtherance of the purposes of the Act. If the Commission
takes such action, the Commission shall institute proceedings under
Section 19(b)(2)(B) \16\ of the Act to determine whether the proposed
rule change should be approved or disapproved.
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    \16\ 15 U.S.C. 78s(b)(2)(B).
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IV. Solicitation of Comments

    Interested persons are invited to submit written data, views, and
arguments concerning the foregoing, including whether the proposed rule
change is consistent with the Act. Comments may be submitted by any of
the following methods:

Electronic Comments

     Use the Commission's Internet comment form (http://www.sec.gov/rules/sro.shtml); or
     Send an email to<EMAIL_ADDRESS>Please include
File Number SR-NYSEMKT-2016-114 on the subject line.

Paper Comments

     Send paper comments in triplicate to Secretary, Securities
and Exchange Commission, 100 F Street NE., Washington, DC 20549-1090.

All submissions should refer to File Number SR-NYSEMKT-2016-114. This
file number should be included on the subject line if email is used. To
help the Commission process and review your comments more efficiently,
please use only one method. The Commission will post all comments on
the Commission's Internet Web site (http://www.sec.gov/rules/sro.shtml). Copies of the submission, all subsequent amendments, all
written statements with respect to the proposed rule change that are
filed with the Commission, and all written communications relating to
the proposed rule change between the Commission and any person, other
than those that may be withheld from the public in accordance with the
provisions of 5 U.S.C. 552, will be available for Web site viewing and
printing in the Commission's Public Reference Room, 100 F Street NE.,
Washington, DC 20549 on official business days between the hours of
10:00 a.m. and 3:00 p.m. Copies of such filing also will be available
for inspection and copying at the principal office of the Exchange. All
comments received will be posted without change; the Commission does
not edit personal identifying information from submissions. You should
submit only information that you wish to make available publicly. All
submissions should refer to File Number SR-NYSEMKT-2016-114, and should
be submitted on or before January 10, 2017

    For the Commission, by the Division of Trading and Markets,
pursuant to delegated authority.\17\
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    \17\ 17 CFR 200.30-3(a)(12).
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Robert W. Errett,
Deputy Secretary.
[FR Doc. 2016-30563 Filed 12-19-16; 8:45 am]
 BILLING CODE 8011-01-P


[Federal Register Volume 77, Number 206 (Wednesday, October 24, 2012)]
[Notices]
[Page 65006]
[FR Doc No: 2012-26208]

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DEPARTMENT OF HOMELAND SECURITY

Federal Emergency Management Agency

[Internal Agency Docket No. FEMA-4080-DR; Docket ID FEMA-2012-0002]

Louisiana; Amendment No. 16 to Notice of a Major Disaster
Declaration

AGENCY: Federal Emergency Management Agency, DHS.

ACTION: Notice.

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SUMMARY: This notice amends the notice of a major disaster declaration
for the State of Louisiana (FEMA-4080-DR), dated August 29, 2012, and
related determinations.

DATES: Effective Date: October 17, 2012.

FOR FURTHER INFORMATION CONTACT: Peggy Miller, Office of Response and
Recovery, Federal Emergency Management Agency, 500 C Street SW.,
Washington, DC 20472,<PHONE_NUMBER>.

SUPPLEMENTARY INFORMATION: The notice of a major disaster declaration
for the State of Louisiana is hereby amended to include the following
area among those areas determined to have been adversely affected by
the event declared a major disaster by the President in his declaration
of August 29, 2012.

    The parish of Pointe Coupee for Individual Assistance (already
designated for Public Assistance, including direct federal
assistance).

(The following Catalog of Federal Domestic Assistance Numbers (CFDA)
are to be used for reporting and drawing funds: 97.030, Community
Disaster Loans; 97.031, Cora Brown Fund; 97.032, Crisis Counseling;
97.033, Disaster Legal Services; 97.034, Disaster Unemployment
Assistance (DUA); 97.046, Fire Management Assistance Grant; 97.048,
Disaster Housing Assistance to Individuals and Households in
Presidentially Declared Disaster Areas; 97.049, Presidentially
Declared Disaster Assistance--Disaster Housing Operations for
Individuals and Households; 97.050, Presidentially Declared Disaster
Assistance to Individuals and Households--Other Needs; 97.036,
Disaster Grants--Public Assistance (Presidentially Declared
Disasters); 97.039, Hazard Mitigation Grant.)

W. Craig Fugate,
Administrator, Federal Emergency Management Agency.
[FR Doc. 2012-26208 Filed 10-23-12; 8:45 am]
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