USAFSAM-TP-87-2 AD-A205 864

THE USE OF ARTIFICIAL INTELLIGENCE FOR THEIDENTIFICATION OF BACTERIA

Ferne K. McCleskey, B.S.Donald J. Cosgrove, M.A.Fred W. Hartman, B.A.Janelle Thomas, B.S.

% DTIC

January 1989 ECTE

2 AR 1989D

Final Report for Period December 1985 - March 1988

Approved for public release; distribution is unlimited. I

USAF SCHOOL OF AEROSPACE MEDICiNEHuman Systems Division (AFSC)Brooks Air Force Base, TX 78235-5301

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REPORT DOCUMENTATION PAGE O 7,04-ov-'

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___87714F SUPT XX 1 EKI TITLE (Include Securrty C;assifiration)

The Use of Artificial Intelligence for the Identification of Bacteria

'2 PERSONAL AUTHOR(S)McCleskey, Ferne K.; Cosgrove, Donald J.; Hartman, Fred W.; and Thomas, Janelle'3a T"PE OF REPORT '3b - ME COVERED 14. DATE OF REPORT (Year, .Mon. Day) 1 5 DGE COU N!Final F ROM8512 o 88/03 1989, January 18

:6 SUPIPLEMENTARY -.cYA7ON

S'7 COSAT CODES 1B SUBIECT TERMS (Continue on reverse if necessary rn identify by ok., nber.')FIELD GRO P Sue-GROkR v.Bacteria; identification; artificial intelligence; medical0 13 computer applications; microbiology; laboratory tests.

,9 ASSTRACT, Contrnue on reverse if necessary and dentify by block number)jA method has been investigated to provide more rapid and more efficient identification ofibacteria using artificial intelligence, a process in which a computer can examine a variety;of facts and devise a solution by comparing the facts with a data base. Incorporated intoIthe data base are the names of 564 species of medically important bacteria with 0-90% positivesor negative results of biochemical reactions for the identification of each species. In arapid search of the data base, the computer selects the three most likely organisms with alikelihood 'ndex for each. This application of artificial intelligence eliminates tediousmatching ,ith biochemical charts; it can be used by the less skilled technician.

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THE USE OF ARTIFICIAL INTELLIGENCE FOR THE IDENTIFICATION OF BACTERIA

INTRODUCTION

In the early and mid-1960's a number of investigators working indepen-dently began a search for a way to use existing mathematical models toformulate biochemical data bases for bacterial identification (1,2,3,4).These programs are mathematical manipulations of a data base matrix utiliz-ing some form of Bayesian probability. Bacterial identification throughcomputer manipulation of a large data base could be performed faster andmore accurately than most microbiolugists could achieve with identificationcharts (4,5). Manufacturers of commercial kits and automated and semi-automated equiiment have effectively used these procedures to identify themore commonly isolated organisms.

METHODS AND MATERIALS

In 1985 a cooperative study was undertaken by the Epidemiology Divisionand the Data Services Branch, Technical Services Division, USAF School ofAerospace Medicine, Brooks Air Force Base, Texas to determine the possibil-ity of using artificial intelligence for bacterial identification. Thebacterial identification system uses conventional biochemical tests withdaily readings, for periods ranging from 2 to 7 days (Fig. 1).

The protocols for testing are taken from published data of the Centersfor Disease Control, American Type Culture Collection and Manual of ClinicalMicrobiology, 4th edition. Definitive identification of genus, species, andbiotype is achieved through the comparison to charts representing largenumbers of organisms which have been tested with each biochemical test,providing a percentage positive and a percentage negative reaction for eachtest (Fig. 2).

The data base consists of genus and species names of 564 organisms withbiotypes as appropriate; all possible biochemical reactions 0-90% positiveor negative; flags to alert the microbiologist to unusual or serious patho-gens; recommendations for further tests; and probability percentages forclosely related organisms.

At this point every test result obtained on the unknown is comparedagainst every organism in that particular group. No expert knowledge aboutindividual test results versus particular organisms is applied at thispoint. Every test has equal weight. This is a Bayesian-like procedure, butsince the original population of the unknown is not known, the a prioriprobabilities of occurrence of particular organisms is unknown. Therefore,the nunbers obtained to rank each organism are not probabilities but likeli-hoods. These likelihoods are ranked, thus reducing the search space to thetop three most likely candidates.

... . ......... . ... . . m~ m m , u,,mm mnn n nnnmmmu mn n lul lln

The expert system has now reached the goal state, but with three candi-dates. Since the goal has been reached without using any rules to excludeparticular organisms, the program now backward chains from those candidatesto advise the microbiologist of any inconsistencies between the test resultsand the three candidates under consideration. Subroutines have been codedfor each family group which contain expert knowledge about individualorganisms.

These rules are then applied to the three candidates. Another sub-routine, which is optional, can search the data base on its own and advisethe human expert on situations which are highly unlikely.

After these rules are applied, the decision of the most likely candi-date is left to the microbiologist (Fig. 3).

Gram's stain results and cultural characteristics dictate the specificprotocol for testing. There are 15 specific biochemical protocols or listsused: List #1=Enterobacteriacae; List #2=Vibrionaceae; List #3=Capneicgrarm-negative bacilli and coccobacilli; List #4=gram-negative nonfermentingbacilli; List #5=Hemophilus species; List #6=Fastidious gram-negative rodsand coccobacilli; List #7Streptococcus species; List #8=Corynebacteriumspecies, and related gram-positive bacilli; List #9=gram-negative cocci;List #10=Anaerobes; List #11=Staphylococcus species; List #22=Listeriaspecies and Erysipelothrix; List #13=Lactobacillus species; List #14=Bacillus species; and List #15=Unusual gram-negative bacilli such as Simon-siella and Erwinia. These lists can be shown on the computer format byrequesting the identification program 5 or 11 (Fig. 4).

Artificial intelligence can now provide expert status for bacterialidentification. Expert status is defined as the ability of this system toidentify isolates as well as, or better than, a microbiologist using charts.The computer printout is much more rapid than visual comparison and presentsnLmiericdl likelihood of identification for the three most likely organisms.

The biochemical tests performed for each test list are in the appendix.These biochemical tests are available on the computer format when requestedfrom the program menu. The operator enters the results of each test bypressing P for positive, N for negative, and I for test not performed. Theresults are displayed on the screen with command capability to change erro-neous entry prior to the command for data search and display of the bacte-rial identification. After the three most probable organisms are listed inprobability order, the command (continue) can be given to ascertain thepositivity index identification (Figs 5 and 6).

EDITOR'S NOTE: For the convenience of the reader, figures have been

placed at the end of the report.

2

SUMMARY OF CONCLUSIONS

At this time, artificial intelligence has provided a system which identifies

isolates as well as, or better than, the microbiologist using extensive charts.

The computer identification is much more rapid than visual comparison and

identifies the three organisms having the highest confidence levels. The user

is also advised of unlikely test reactions and given recommendation for further

tests if required.

REFERENCES

1. Mac Lowery, K. J. Ryan. Computers in clinical microbiology. Manual of

Clinical Microbiology, 4th ed., p. 68. Am Soc Microbiol, Washington, D.C.,

1985.

2. Gyllenberg, H. G. A model for identification of microorganisms. J Gen

Microbiol 39:401-405 (1965).

3. Lapage, S. P., S. Boscomb, W. R. Wilcox, and M. A. Curtis. Identification

of bacteria by computer; general aspects and perspectives. J Gen

Microbiol 77:273-290 (1973).

4. Sneath, P. H. A. The application of computers to taxonomy. J Gen Microbiol

17:210-226 (1957).

5. Friedman, R. B., D. Bruce, J. D. Mac Lowery, and V. Brenner. Computer-

assisted identification of bacteria. Am J Clin Path 60:396-403 (1973).

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3

BIOCHEMICAL REACTIONS(THIS FORM IS SUBJECT TO THE PRIVACY ACT OF 1974- USE BLANKET PAS DO FORM 2005)

EL NUMBER DATE NAMEGRAM STAIN G F,-.v7 L

BASE

RESULTS

TCBS AGAR CAMPY AGAR ASS AGAR - CHOCOLATE AGAR 1

, . Is

Carbohydrate Base Enteric OIF SO PR8 + XV (

Lactose /Vp HIA: Spores

Dextrose JNP4 - 250 C - Pseudo Agar

Sucrose M " 370 C Hemolysis

Maltose ,j Pigment (Blood)

Mannitol Lysine Melibiose

Xylose p. , Ornithine Hipourate

Glycerol /V,9 Arginine Starch Hyd

O/F Medium (Dex) Tyrosine Agar ,4'e 5% Suc Agar

Ooen AJe q Acetamide 5% Suc Broth

Closed rv e , Tartrate e Veg Hvphae (25030°C0

Malonate Acetate Motility Iwet preo)

Urea Mucate Serotype A/0Motility 25(37* C Rhamnose Methyl Red

Gelatin Arabinose CDC Blood Agar

Indol RiHID I Dulcitol CNA SuDpl Agar

NitrateiNitrite - Inositol KVBAP Agar

VPIVP So Ale Adonitol BBE Agar

S. Citrate 'r,4 Salicin Thayer-Martin Agar

Nut Agar 25/370 C -//- Trehalose Yersinia Agar

Oxidase ,. Fructose Al Anaerobic

Litmus Milk Sorbitol CO 2 Growth Only

Catalase " Cellibiose ,n X/lgsm .Centrimide Raffinose MelAesculin Arabitol

TSI 6% NaCI

Butt 10% NaCI

Slant M B Milk

H2S (TSI) ONPG

Butt J Tartrate

Paoer . Phenylalanine .

Pseudo F KCN

Pseudo P ONase 25/370 C _ _ _

Lecithinase

Lioase

TSA loigment , 25 C)

AFSC Form 3290, MAR 88 PRVIoUS OITICN WILL 8E USED

Figure 1. After an organism's growth characteristics and reactions have beenevaluated, the technician records the results on AFSC Form 3290,Biochemical Reactions, for later data entry.

5

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MENU FOR GROUP SELECTION

I Enteric

2 Vibrionaceae

3 Gram neg capneic rods

4 Non ferm gram neg

5 Hemophilus

b Fastidious gram neg

7 Gram pos cocci

3 Gram pos rods (non-sporeformers)

9 Gram neg cocci

10 Anaerobic

11 Staphylococcus

12 Listeria - Ervsipelothrix

13 Lactobacillus

14 Large gram pos rods (sporeformer)

15 Unusual gram neg rods

Please enter group number:

Figure 4. After evaluating the organism's cultural characteristics,

the technician selects one or more specific protocols

(lists) from the program menu.

8

EKLM TEST RESULTS

1 O'ICATlVE F ERMENTATIVE a3 LACTOSE4 0E(T ROSE F, 5 SUCROSE N 6 MALTCSE71'MANNITCL N 3 )XYLCSE N 9 GI v'EPDL00 FRUC7CSE P i UREA N4 '2 MOT1LiY N1

',3 GELATIN P 14 INCOL N 15 NITRATE P16 S. CiTRATE N ' 7 NUTRIENT AGAR P 18 EIROTH.6% NaC; P9 OXIDASE N 20 LITMUS MILK P 21 CATALASE P

22 AESCULIN N 23 H2S, PAPER P 24 GROWVTH. 25 C P25 GROW FIH. 350C P 26 GROWTH, 42 C N 27 O'NASE P28 PIGMENT N4 29 TINSOALE. HALO P 30 TCXIGENIC N31VP N

CEC:SICN CCRYNEBAC7EPIUM DIPHT1-IERIAE MIT:C 0 924'C :DIPHTHERIAE 3RAVIS 3 883'

D)IPI-iHETIAE NTERMEDIUS 03 82'

FcR CCRYNESATE:;IUM :iPHTHE7;'AF %117;S :;HECK CXtGENIlC I-'AiC ANC uFPEA

4VARNING! CCRYNF A,'C7'EMiLMj CiPHTI-1cRIAf- IISS EXTREMEIL' SEzzICfuS

OfR C. SIPHTHERAF- 3RAJIS C HE CK' _CXIcENIC -ALO A14C _REA

t/ARNING; C CIPHTHETIIAE fRAVIS IS EXTREM.EL,~S~CS

FOR C. DIPHTHERIAE NTERMED;L'S CHECK OCXIGE-:JIC. - AL,2 ANC U-REA

'NARNJING: C DIPH THEP.IAE 1NTERkME:IUS IS EXTREME' S~n1(uS.

Figure 5. After the technician enters the EKLM Test Results data,the screen displays the biochemical reactions, the threemost probable organisms in probability, frder, and appro-priate warning~s.

EKLM TEST RESULTS

I LACTOSE N2 DEXTROSE P 3 DEXTROSE GAS A4 SUCROSE N MALTOSE P 6 MANNITOL1*Al iCSE ? rLCFO N 9 MALONATE

iG URFA ~ N 11 MOTIUITY P -'IGELATIN 12SC)i3 IN~D N f4 VP N i:5. CITRATE NIS AESUCLIN HYO 17 H2S (KIA1 P 18 LYSINE P'9 ORNITIIINE N 20 ARGININE N4 21 ACETATE N2PHAMNOSE N23 ARABIINOF N 24 DULCITOL 'N25 INOSITOL N26 AONITOL N4 27 SALICIN N125 TREHALOSE P 29 SORBITOL P 30 P AFrINOSIP .N31 ONPG N4 32 J. TARTRATF P 33 PI-ENYLALANINE A4

34KICN N35 DNA s25 C) N 36 YEL PIG (25 C) Nj37 IAELIBIOSE P38 CELLCBIOSE N 39 ARABITOL N40 METHYL RED P 41 SEPOTYPE I

DECISION! S. 1IYPHI 0.9860S. GALLINARUM 0.8538S. CHOLERAESUIS 0.8508

S. GALUNARUM VIOLATES MOTILITY NEGATIVE

WOULD YOU LIKE TO CHECK OTHER TESTS AGAINST THE CANDIDATES' Y

WHAT PERCENT L.EVEL DO YOU WANT TO CHECK THE CANDIDATES? '00

ENjI M,il H' UMEP (IF CANDIDATEP TO BE CHECKED (1,2. OR 31:2JNKNOWN SHOWS POSITIVE FOR MOTILITY BUT CURRENT DATA SHOWS THAT

S. GALLINARUM ONLY HAS A 0.0% CHANCE OF TESTING POSIT!VE

UNKNOWN SHOWS POSITIVE FOR MELIBIOSE BUT CURRENT DATA SHOWS THATS. 5ALLINARUM ONLY HAS A 0.0% CHANOCi OF TESTING POSITIVE

DO YOU WISH TO ENTER ANOTHER UNKNOWN IY/Nl 7

Figure 6. After the technician enters the EKLM Test Results data,the screen aisplays the same type of data as Figure 5,but instead of a Warnings field, prompts the technicianto continue.

9

APPENDIX

BIOCHEMICAL TESTS

11

TESTS LIST #1 TESTS LIST #2 TESTS LIST #3

1 Lactose 1 Fermentative 1 Oxidative2 Dextrose 2 Oxidase 2 Fermentative

3 Dextrose gas 3 Dextrose Gas 3 Dextrose Gas4 Sucrose 4 Lactose 4 MacConkey5 Maltose 5 Dextrose 5 Lactose6 Mannitol 6 Sucrose 6 Dextrose7 Xylose 7 Maltose 7 Sucrose8 Glycerol 8 Mannitol 8 Maltose9 Malonate 9 Xylose 9 Mannitol10 Urea 10 Glycerol 10 Xylose

11 Motility 11 Rhamnose 11 Glycerol12 Gelatin (25"C) 12 Arabinose 12 Fructose13 Indol 13 Cellobiose 13 Urea14 VP 14 Salicin 14 Motility15 S. Citrate 15 Trehalose 15 Gelatin16 Aesuclin Hyd. 16 Arabitol !6 Indo.17 H2S (KIA) 17 Urea 7 Nitrate18 Lysine 18 Motility 18 VP19 Ornithlne 19 Gelatin 19 S. Citrate20 Arginine 20 Indol 20 Nut. Agar21 Acetate 21 Nitrate 21 Oxidase22 Rhamnose 22 Vp 22 Litmus Milk23 Arabinose 23 S. Citrate 23 Catalase24 DulcitoJ. 24 Catalase 24 Esculin25 Inositol 25 Esculin 25 H2S Paper26 Adonitol 26 H2S(KI) 26 Growth (25'C)27 Salicin 27 KCN 27 Growth (3500)28 Trenalose 28 Lysine 28 Growth (420 C)29 Sorbitol 29 Ornithine 29 Lysine30 Raffinose 30 Arginine 30 Ornithine31 ONPG 31 Tyrosine 31 Arginine32 J. Ta--trate 32 J. Tartrate 32 Acetamide33 Phenylalanine 33 Acetate 33 Tartrate34 KCN 34 Mucate 34 Acetate35 DNA (25"C) 35 D'Nase 35 Mucate36 Yel Pig (250C) 36 Phenylalanine 36 D'Nase37 Melibiose 37 ONPG 37 Pseudo agar38 Cellobiose 38 Lipase 38 Requires C0239 Arabitol 39 6% NaCi, growth 39 Res. to Cephalotn:n40 Methyl Red 40 10% NaC1, growth 40 Res. to Nalidexc. Acvd41 Serotype

12

TESTS LIST #4 TESTS LIST #5 TESTS LIST #6

1 MacConkey 1 Ala (porphyrin test) 1 Fermentative

2 SS 2 V factor 2 MacConkey

3 Dextrose 3 Indol 3 Lactose

4 Lactose 4 Urea 4 Dextrose

5 Sucrose 5 Irnitilne 5 Sucrose

6 Maltose 6 hemolysis 6 Maltose

7 Mannitol 7 Catalase 7 Mannitol

8 Xylose 8 Oxidase 8 Xylose

9 Glycerol 9 Lactose 9 Glycerol

10 Fructose 10 Dextrose 10 Fructose

11 Urea 11 Sucrose 11 Esculin

12 Motility 12 Mannitol 12 Urea

13 Gelatin 13 Xylose 13 Motility

14 Indol 14 Trehalose 14 Gelatin

15 Nitrate 15 Nitrate 15 Indol

16 Gas from N03 16 C02 enhanced growth 16 Nitrate

17 Nitrite 17 Serotype 17 VP

18 S. Citrate 18 Beta lactamase 18 S. Citrate

19 Nut. agar 19 Nut. agar

20 Oxidase 20 Oxidase

21 Catalase 21 Litmus milk

22 Litmus milk 22 Catalase

23 pseudosel 23 Pseudosel

24 Esculin 24 H2S Paper

25 H2S, Butt 25 Growth (25°C)

26 H2S, paper 26 Growth (35

0C)

27 Growth, (25'C) 27 Growth (42c)

28 Growth, (35'C) 28 Lysine

29 Growth, (420C) 29 Ornithine

30 Lysine 30 Arginine

31 Arginine 31 Acetamide

32 Urnithine 32 J. Tartrate

33 Tyrosine 33 Acetate

34 Acetamide 34 Mucate

35 Tartrace 35 D'Nase

36 Acetate37 Mucate38 D'Nase

39 Pseudo agari0 Pigment41 VP

13

TESTS LIST #7 TESTS LIST #8 TESTS LIST #9

1 VP 1 Oxidative 1 Oxidative

2 Hippurate 2 Fermentative 2 Fermentative

3 Esculin 3 Lactose 3 Dextrose, Gas

4 Pyrrolidonyl- 4 Dextrose 4 MacConkey

arylamidase 5 Sucrose 5 Lactose5 Alpha-Galactorldase 6 Maltose 6 Dextrose6 Bet-Glucoronidase 7 Mannitol 7 Sucrose7 Beta-Galactosldase 8 Xylose 8 Maltose8 Alkaline phosphatase 9 Glycerol 9 Mannitol9 Leucine Arylamidase 10 Fructose 10 Xylose10 Arginine 11 Urea 11 Glycerol11 Ribase 12 Motility 12 Fructose12 Arabinose 13 Gelatin 13 Urea

13 Mannitol 14 Indol 14 Gelatin14 Sorbitol 15 Nitrate 15 Indol15 Lactose 16 S. Citrate 16 Nitrate

16 Trehalose 17 Nutrient Agar 17 Nitrite

17 Unulin 18 Broth +6% NaCl 18 VP

18 Raffinose 19 Oxidase 19 S. Citrate19 Starch 20 Litmus milk 20 Nutrient Agar20 Glycogen 21 Catalase 21 Oxidase21 Beta hemolysis 22 Aesculin 22 Litmus milk22 Dextran 23 H2S, paper 23 Catalase23 Levan 24 Growth, 250C 24 Acetate

25 Growth, 350 C 25 H2S, Paper

26 Growth, 42"C 26 Growth, 250 C

27 D'Nase 27 Growth, 35 C

28 Pigment 28 Growth, 420 C

29 Tinsdale, halo 29 Tyrosine

30 Toxigenic 30 Tartrate31 VP 31 Mucate

32 D'Nase33 Amylosucrase34 Phenylalanine35 ONPG

36 Serotype

14

TESTS LIST #10 TESTS LIST #11 TESTS LIST #12

1 Indol I Colony, large 1 Fermentative

2 p-nitrophenyl-N 2 Pigment 2 Lactose

-acetyl-B-D- 3 Anaerobic 3 Dextrose

glucosamiflide 4 Aerobic 4j Sucrose

3 p-ritrophenyl-o<-D 5 Coag. 5 Maltose

glucoside 6 Herolysis 6 Mannitol

4 p-nitrophenyl-c(-L 7 Nitrate 7 Xylose

arabinofuranoside 8 VP 8 Glycerol

5 p-nitrophenyl-B-D 9 Phosphate 9 Fructose

glucoside 10 Urease 10 Rhainnose

6 p-nitropflenyl-c<- 11 Arg 11 Urea

fucoside 12 Beta-Glucosidase 12 Motility

7 p-iitropnenyl- 13 Beta-GlucorOflde 13 Gelatin

phospflate 14 Beta-Galactosidase 14 Iridol

8 p-nitrophenyl-c(D- 15 Novobiocin Res. 15 Nitrate

galactoside 16 Tr'ehalose 16 Nitrite

9 O-nitrOphenyl B-D 17 Mannitol 17 VP

galactoside 18 Xylose 18 S. Citrate10 Indoxyl-acetate 19 Cellobiose 19 Nut. Agar

11 Argirline 20 Sucrose 20 Broth + 6.5 NaCi.

12 L-Leucyl-4-rmethoxy- 21 Mannose 21 Oxidase

B-naphthylamide 22 Ribose 22 Litmus milk

13 L-prolirle-B 23 Raffinose 23 Catalase

napthylamide 2'4 Lactose 24 S. aureus Camp

14 L-pyrrolidonyl-B- 25 Aesculin

napthylamide 26 H2S Butt

15 L-Tyrosine-B- 27 H2S paper

napthylamide 28 Growth 25'C

16 L-arginine-3- 29 Growthl 35"C

napthylamifle 30 Growth 420 C

17 L-alanyl-L-slaflyl-B 31 D'Naseriapthylamide 32 Beta hemolysiS

18 L-histodine-B-napthylamide

19 L-phenylalaflife-B-napihylamide

20 L-Glycine-B-napthylamide

21 Catalase

15

TESTS LIST #13 TESTS LIST # 14 TESTS LIST 015

1 Alpha hemolysis 1 Oxidative I Fermentative

2 Gas, Dextrose, Thio 2 Fermentative 2 Gas, Dextrose

3 Anaerobic growth 3 Gas, Dextrose 3 MacConkey

4 Lactose 4 Lactose 4 SS

5 Dextrose 5 Dextrose 5 Lactose

6 Sucrose 6 Sucrose 6 Dextrose

7 Maltose 7 Maltose 7 Sucrose

8 Mannitol 8 Mannitol 8 Maltose

9 Xylose 9 Xylose 9 Mannitol

10 Fructose 10 Glycerol 10 Xylose

11 Rhamnose 11 Fructose 11 Glycerol

12 Arabinose 12 Salicin 12 Fructose

13 Salicin 13 Urea 13 Urea

14 Urea 14 Motility 14 Motility

15 Motility 15 Gelatin 15 Gelatin

16 Indol 16 Indol 16 Indol

17 Nitrate 17 Nitrate 17 Nitrate

18 S. Citrate 18 VP 18 Nitrite

19 Nut. Agar 19 S. Citrate 19 VP

20 Oxidase 20 Nut. Broth 20 S. Citrate

21 Litmus milk, acid 21 Nut. Broth *6% NaCI 21 Nut. Agar

22 Catalase 22 Oxidase 22 Oxidase

23 Aesculin 23 Litmus Milk 23 Litmus Milk

24 KIA, A, slant 24 Catalase 24 Catalase

25 H2S Paper 25 Aesculin 25 Pseudosel

26 Growth 250C 26 KIA, But, acid 26 Aesculin

27 Growth 350C 27 KIAQ, Slant acid 27 KIA, Butt, acid

28 Growth 42C 28 H2S, Butt 28 KIA, Slant, acid

29 Prefer C02 29 H2S, Paper 29 H2S, Butt

30 Growth, 250C 30 H2S, Paper

31 Growth, 350C 31 Growth 25C

32 Growth, 42°C 32 Growth 350C

33 Lysine 33 Growth 42°C

34 Ornithine 34 Lysine

35 Arginine 35 Ornithine

36 D'Nase 36 Arginine

37 Acetamide 37 Tyrosine

38 Tartrate 38 Acetamide

39 Acetate 39 Tartrate

40 Mucate 40 Acetate

41 Starch 41 Mucate

42 Lecithinase 42 D'Nase

43 Meth. Blue Red 43 Amylosucrose

44 Hemolysis 44 Hemolysis45 Pigment

16