THE GENETIC SEQUENCE BINARY FACTOR GROUPING ROUTINES

These routines were written for the purpose of analysing raw data published by the Broad Institute www.broad.mit.edu by the title : MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia.The routine transforms the mean numerical scan values CEL file number column into a large contigious binary field that is sequentially divided into sets of 16-12 bits long arrays. These array bit-patterns are searched for max of reoccurences by left to right bit recombining (truncation) from 16 to 8 bit resulting in an optimized variable length resulting order array that is prooved for max of re-occurences also used in a compression routine gaining factor groups redundancy as high as possible. Field groups are outputed in A-P symbol files and tested for long composite repeating symbol chains and stored produce a series of proto-text definitions that represent the reocurences of the measured numerical values.


GENE MAPS - GENOME SEQUENCE LISTS mouse.zip

Genome (sub)sequences extracted with binary factor grouping from CEL file(s) published under the title: Transformation from commited progenitor to leukemia stem cells initiated by MLL-AF9 Data>> The above listed raw data files scans gene text symbols dictionary at 23.11.2007

  • Download File r3151_13.zip


  • Genome (sub)sequences extracted with binary factor grouping from CEL file(s) published under the title: Classification of Human Lung Carcinomas by mRNA Expression Profiling Reveals Distinct Adenocarcinoma Sub-classes Data(1)>> Data(2)>>


    Genome (sub)sequences extracted with binary factor grouping from CEL file(s) published under the title: The molecular signature of mediastinal large B-cell lymphoma differs from that of other diffuse large B-cell lymphomas and shares features with classical Hodgkin lymphoma Data>>


    Genome (sub)sequences extracted with binary factor grouping from CEL file(s) published under the title: MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Data>> The above listed raw data files scans gene text symbols dictionary at 27.09.2007

  • Download File r3151_10.zip

  • Genome (sub)sequences extracted with binary factor grouping from CEL file(s) published under the title: Gene Expression Correlates of Clinical Prostate Cancer Behavior Data>>


    Genome (sub)sequences extracted with binary factor grouping from CEL file(s) published under the title: A zebrafish bmyb mutation causes genome instability and increased cancer susceptibility Data>>

    These raw CEL data files were published by the reasons stated in the published titles, yet here there were computed using this paticular method that certainly differs.
    Method routines sources are included in routines.zip.

    Step 2 will actually choose numbers, or incomplete sequence(ed) numbers found similar by binary right truncation and compared (tootal occurence count of a single truncation)/(actual number occurence count) to a fixed division result. Changing this constatnt will produce more/less combinations (x6 number sequences presented and grouped by leading nonzero x4 bit factor cell) that will be searched for in each of the 25 quadrant (incomplete symbol sequence(s) collection outputed, quadrant, by quadrant, by Step 2) binary group symbol files. This search is performed regarding the combination notation by the leftmost 4-bit group that is not equal to zero. Only the contigious one are searched for number occurences in the corresponding quadrant of the actual cell array file. Thus produced sequences are recombined (eg sequence xN numbers resulting in 1,2,3,4 then 2,3,4,5...to (N-3),(N-2),(N-1),N ) and regrouped (x4 numbers by leading nonzero x4 bit cell) in the mentioned manner. It is possible to use longer combining/grouping sequences, depending on computer hardware resources. Each of the produced series of combinations (eg 0 1 2 3 for BDAM ABBC AACL AAAM) is computed (each sequence column separetely) for mean (each to each element) decimal distances divided by a choosen division factor (<1,10,100) . Thus decimal distance bands (decimal range groups) are determined for the actual combination group. This process may be used for (only) a specific combination group, and processed sequence lenghts may change. But it also possible to examin results from collections of combination groups. These results were computed in the following computing sequence:

    (1) produce binary file out of mean column screening CEL number values
    (2) process screening CEL file binary data
    (3) produce an all (x3) combinations file out of a conactenated compression dictionary(ies) files
    (4) produce a: (1) binary factor group listing file
    (5) produce a: (2) binary factor matching group numbers file
    (6) copy all binary factor number groups file r13_all.txt to a.txt
    (7) produce a.bat - list of group combinations in order to produce sequence/segment list
    (8) extract number groups having all (1-6 position) matches positioned in compression dictionary (or array segment) file(s)
    (9) extract number groups having only (all 6 position) matches
    (10) extract corresponding number rows from screening CEL file for all (x6 matches) matching number groups
    (11) produce a partial screening segments array file out of a single/multiple screning file(s) group(s) column/row positions
    (12) produce a grouping order x4 numbers (x3-x10 numbers) from number groups listed in the partial screening segments array file (groups)
    (13) produce a list of subgroups for each number group listed in the partial screening segments file (subgroups)
    (14) produce a number range(band) list
    (15) determine FASTA (partial) order(s) out of number (sub) bands based on these (sub) groups

    This picture shows sequence presence in CEL files as found in the displayed CEL file names and listed in mouse.zip:




    The results published under the title:Expression data from skin biopsies in cutaneous T-cell lymphoma

    The ctl.rma2 values for the sequence: FXR1 : fragile X mental retardation, autosomal homolog 1, No information for gene, FXR1 : fragile X mental retardation, autosomal homolog 1, CPSF1 : cleavage and polyadenylation specific factor 1, 160kDa
    Represented by the numbers:

    (column M1): 702.37, 482.98, 2677.51, 33.96 and as a symbolic sequence of 4-bit binary groups: ACLO ABOC AKHF AACB
    (column M2): 773.4, 578.56, 2502.44, 35.58 and as a symbolic sequence of 4-bit binary groups: ADAF ACEC AJMG AACD
    (column M11): 496.85, 467.77, 2385.11, 35.71 and as a symbolic sequence of 4-bit binary groups: ABPA ABND AJFB AACD
    (column M19): 681.94, 639.95, 2539.33, 30.51 and as a symbolic sequence of 4-bit binary groups: ACKJ ACHP AJOL AABO
    (column M22):584.34, 401.07, 1882.06, 53.45 and as a symbolic sequence of 4-bit binary groups: ACEI ABJB AHFK AADF
    (column M7): 479.65, 521.03, 2605.04, 29.14 and as a symbolic sequence of 4-bit binary groups: ABNP ACAJ AKCN AABN
    (column M15): 1318.46, 590.24, 2741.49, 34.78 and as a symbolic sequence of 4-bit binary groups: AFCG ACEO AKLF AACC
    (column M9): 541.67, 664.56, 2719.95, 33.7 and as a symbolic sequence of 4-bit binary groups: ACBN ACJI AKJP AACB

    and

    (column M67): 1196.46, 495.36, 3102.24, 41.93 and as a symbolic sequence of 4-bit binary groups: AEKM ABOP AMBO AACJ
    (column M66): 795.65, 584.15, 2589.91, 33.92 and as a symbolic sequence of 4-bit binary groups: ADBL ACEI AKBN AACB
    (column M64): 814.32, 623.41, 2318.66, 32.45 and as a symbolic sequence of 4-bit binary groups: ADCO ACGP AJAO AACA
    (column M56): 565.32, 665.71, 2032.5, 33.02 and as a symbolic sequence of 4-bit binary groups: ACDF ACJJ AHPA AACB

    where:

    M1 skin_patient_M1 5202764005788164112404.A01.CEL Human skin biopsies Homo sapiens IB 2 total RNA SAPE Gene expression data from 6mm punch biopsies of skin GPL96
    M11 skin_patient_M11 5202764005788164112404.A02.CEL Human skin biopsies Homo sapiens IB 0 total RNA SAPE Gene expression data from 6mm punch biopsies of skin GPL96
    M19 skin_patient_M19 5202764005788164112404.A03.CEL Human skin biopsies Homo sapiens IB 2 total RNA SAPE Gene expression data from 6mm punch biopsies of skin GPL96
    M2 skin_patient_M2 5202764005788164112404.B01.CEL Human skin biopsies Homo sapiens IIB 1 total RNA SAPE Gene expression data from 6mm punch biopsies of skin GPL96

    and

    M67 skin_patient_M67 5202764005788164112404.H08.CEL Human skin biopsies Homo sapiens IB 1 total RNA SAPE Gene expression data from 6mm punch biopsies of skin GPL96
    M66 skin_patient_M66 5202764005788164112404.G08.CEL Human skin biopsies Homo sapiens IB 2 total RNA SAPE Gene expression data from 6mm punch biopsies of skin GPL96
    M64 skin_patient_M64 5202764005788164112404.E08.CEL Human skin biopsies Homo sapiens IA 2 total RNA SAPE Gene expression data from 6mm punch biopsies of skin GPL96
    M56 skin_patient_M56 5202764005788164112404.E07.CEL Human skin biopsies Homo sapiens IB 2 total RNA SAPE Gene expression data from 6mm punch biopsies of skin GPL96


    and compared for factor group similarity to ADAF ACEC AJMG AACD results in:

    M67
    M66
    M64
    M56
    !3:1,1,0,1 ADBF ACCE ACFB AACJ
    !3:1,0,1,1 ADDF ADGC AJON AACC
    !3:1,1,0,1 ADED ACHN ACDP AACP
    !3:1,1,0,1 ADDC ACCH ABGB AACF
    !3:1,1,0,1 ADMD ACKN AELF AACL
    !3:1,1,0,1 ADOC ACHJ ABMG AACH
    !4:1,1,1,1 ADGL ACLC AJJL AACK
    !3:1,1,0,1 ADHC ACKK AGKN AACI
    !3:1,1,0,1 ADPI ACJH ABEK AACC
    !3:0,1,1,1 AFLD ACMK AJAD AACI
    !3:1,1,1,0 ADKP ACAH AJAP AAMJ
    !3:1,1,1,0 ADGL ACNO AJPL AAKP
    !3:1,1,0,1 ADOO ACIN AMDI AACI
    !3:1,1,0,1 ADNO ACBP ABCH AACH
    !3:1,1,0,1 ADPP ACGG ABPF AACK
    !3:1,1,0,1 ADEO ACLM ABCB AACN
    !3:1,1,0,1 ADAD ACAH ACJB AACL
    !4:1,1,1,1 ADNO ACAM AJLF AACO
    !3:1,0,1,1 ADGP ABGI AJGD AACF
    !3:1,1,0,1 ADMG ACHD ACNF AACD
    !3:1,1,0,1 ADFH ACIJ ACBI AACJ
    !3:0,1,1,1 AEDM ACPP AJJC AACM
    !3:1,1,0,1 ADAP ACCG ACJH AACA
    !3:1,0,1,1 ADPG AGKH AJHD AACA
    !3:1,1,0,1 ADED ACHD ACMB AACG
    !3:1,1,0,1 ADBB ACMB ABFP AACF
    !3:1,1,0,1 ADCN ACCC ABFI AACH
    !3:1,1,0,1 ADDD ACIJ ADFP AACK
    !3:1,1,0,1 ADDG ACNC ABOF AACC
    !3:1,1,1,0 ADEM ACOB AJPJ AAFD
    !4:1,1,1,1 ADEO ACOI AJNM AACK
    !3:0,1,1,1 AGLG ACCO AJFN AACG
    !3:1,1,0,1 ADFF ACMK ADCA AACN
    !3:1,1,0,1 ADAB ACJN ANLI AACF
    !3:1,1,0,1 ADLD ACIA ACPB AACB
    !3:1,0,1,1 ADFK AMFJ AJNK AACL
    !3:1,1,0,1 ADGN ACJG ALPJ AACL
    !3:1,0,1,1 ADGN ABKI AJGA AACE
    !3:1,1,0,1 ADEJ ACFC ACMO AACL
    !3:1,1,0,1 ADKP ACIO ADGL AACI
    !3:1,1,0,1 ADPO ACJK ACHL AACN
    !3:1,1,0,1 ADAK ACPL ADNP AACN
    !3:1,1,0,1 ADCP ACOG ACKG AACE
    !3:1,1,0,1 ADBD ACDN ABDF AACO
    !3:1,1,0,1 ADEK ACBJ ADMI AACK
    !3:1,1,0,1 ADLO ACEN ABNF AACN
    !3:1,1,0,1 ADFD ACAD AIGB AACH
    !3:1,1,0,1 ADDD ACOG ABDP AACK
    !4:1,1,1,1 ADGD ACGJ AJHG AACI
    !3:1,1,0,1 ADIP ACGH ACHL AACP
    !3:1,1,0,1 ADCH ACFJ ACAO AACE
    !3:1,1,0,1 ADGD ACGN ADBN AACL
    !3:1,1,0,1 ADLJ ACHM ABKG AACH
    !3:1,1,0,1 ADIP ACKB AEDE AACH
    !3:1,1,0,1 ADAO ACGL ABID AACJ
    !3:0,1,1,1 ACKK ACMB AJEF AACJ
    !3:1,1,1,0 ADFH ACFB AJAL AAHD
    !3:1,1,0,1 ADBE ACFL ADEL AACC
    !3:1,1,0,1 ADEB ACGI AICL AACH


    All symbolic number sequences searched have the significant 4-bit group positions at 1 1 1 2. Comparisons vs pattern sequence at those positions had produced the results. The left graph built by found values in M67 displayes factor matching positions group values.















    Raw data CEL file CL2001030501AA.CEL published under:MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia was searched for number groups (x6) having factor values B B B B C C at positions 1 1 1 1 1 (0-3) having 38 repetitions for the number sequence ABCH ABBJ ABBB ABGP ACEP ACHF. The search produced the following table of CEL file row,column and measured max and mesured average values:

    row
    column
    max
    average
    std
    2
    29
    34
    67
    139
    185
    218
    239
    272
    413
    500
    592
    623
    639
    10
    61
    109
    131
    141
    487
    576
    14
    69
    103
    147
    302
    494
    568
    265
    267
    636
    62
    104
    215
    224
    401
    401
    401
    401
    401
    401
    401
    401
    401
    401
    401
    401
    401
    401
    402
    402
    402
    402
    402
    402
    402
    403
    403
    403
    403
    403
    403
    403
    404
    404
    404
    405
    405
    405
    405
    1771
    3718.3
    1265.5
    952.3
    1082
    1913.5
    2409
    1131
    3865.8
    1380
    1138
    1117.8
    1411.3
    1306
    1523
    870
    1334.5
    1718
    3155.8
    1111.5
    1374
    1248
    1558
    1627.8
    1476.5
    9283.3
    1215.3
    1113
    1131.5
    1028.5
    1474.3
    1182
    995
    942
    1567
    367.8
    629.7
    281
    273
    273.1
    295.2
    273.9
    295.2
    591.3
    281.6
    281.6
    281.8
    295.9
    281.7
    295.2
    273.6
    273.7
    273.7
    281.8
    273.9
    273.5
    273.7
    295.4
    295.9
    295.4
    591.5
    281.2
    295.1
    295.5
    295
    367.9
    367.6
    295.6
    273.6
    295.4
    25 ABGP 367
    20 ACHF 629
    20 ABBJ 281
    20 ABBB 273
    25 ABBB 273
    16 ABCH 295
    25 ABBB 273
    25 ABCH 295
    20 ACEP 591
    25 ABBJ 281
    25 ABBJ 281
    20 ABBJ 281
    16 ABCH 295
    25 ABBJ 281
    25 ABCH 295
    25 ABBB 273
    20 ABBB 273
    25 ABBB 273
    20 ABBJ 281
    16 ABBB 273
    20 ABBB 273
    20 ABBB 273
    25 ABCH 295
    20 ABCH 295
    20 ABCH 295
    16 ACEP 591
    20 ABBJ 281
    25 ABCH 295
    20 ABCH 295
    16 ABCH 295
    20 ABGP 367
    20 ABGP 367
    25 ABCH 295
    25 ABBB 273
    25 ABCH 295

    The rest of binary groups (eg ABEB ABEJ ABAG ABBP ACBO ACPN) and their values are:


    A
    B
    C
    D
    E
    F
    389
    327
    402
    402
    296
    321
    321
    257
    271
    271
    313
    373
    301
    355
    267
    258
    295
    338
    317
    257
    277
    295
    295
    279
    499
    261
    261
    260
    397
    425
    275
    290
    334
    317
    420
    333
    421
    286
    270
    313
    291
    291
    312
    329
    329
    398
    396
    396
    389
    505
    266
    388
    262
    276
    281
    273
    263
    272
    403
    339
    339
    289
    499
    354
    354
    425
    437
    421
    369
    290
    295
    367
    264
    378
    480
    360
    341
    465
    282
    282
    302
    262
    262
    308
    266
    266
    290
    361
    319
    357
    259
    367
    273
    362
    262
    286
    275
    322
    322
    412
    267
    401
    401
    433
    449
    293
    320
    294
    285
    337
    278
    314
    290
    364
    501
    393
    302
    302
    291
    287
    287
    283
    274
    274
    363
    257
    405
    261
    315
    381
    367
    278
    369
    268
    354
    284
    432
    306
    429
    457
    457
    299
    291
    421
    261
    267
    295
    267
    379
    345
    263
    283
    645
    645
    549
    549
    544
    542
    542
    545
    623
    623
    737
    529
    612
    665
    585
    587
    591
    587
    645
    595
    745
    667
    569
    514
    569
    555
    555
    533
    633
    615
    627
    535
    537
    619
    516
    561
    658
    613
    562
    663
    578
    578
    612
    765
    765
    527
    738
    738
    673
    557
    596
    678
    578
    631
    629
    568
    513
    586
    600
    601
    667
    701
    653
    693
    693
    734
    662
    580
    687
    521
    609
    540
    731
    698
    645
    555

    These results display max column number values distribution for sequences extracted from CL2001031627AA.CEL (Classification of Human Lung Carcinomas by mRNA Expression Profiling Reveals Distinct Adenocarcinoma Sub-classes) extracted using the described routines.

    mean column value 20
    (chart of max measured values)
    mean column value 24
    (chart of max measured values)

    mean column value 18
    (chart of max measured values)
    mean column value 19
    (chart of max measured values)

    Chosen (subsequent numbers and number groups) numbers are then populating the values of a large array as shown in the bellow listed (partial) results from CL2001031609AA.CEL published under the above stated title allowing gene identification.

    row
    column
    max
    average
    std
    93
    94
    96
    97
    98
    99
    100
    102
    103
    104
    105
    106
    107
    108
    109
    110
    112
    113
    114
    115
    117
    118
    119
    121

    ...

    228
    229
    230
    231
    232
    233
    234
    235
    236
    237
    238
    240
    241
    242
    243

    ...

    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1

    ...

    3
    3
    3
    3
    3
    3
    3
    3
    3
    3
    3
    3
    3
    3
    3

    ...

    174
    175.8
    142.8
    124.8
    158
    146
    169.3
    146.3
    105.3
    162.5
    159.3
    151.5
    140.5
    145
    107
    152.5
    344
    218.8
    223
    141
    160
    154
    153.3
    135

    ...

    152.8
    170
    156
    298.3
    316.3
    202.3
    176.5
    113.3
    116
    112.8
    139
    190
    217
    141.3
    328

    ...

    25.9
    35.9
    17.4
    25.3
    17.8
    17.8
    21
    16.1
    20.3
    19.4
    23.4
    23.3
    16.6
    16.6
    16.8
    23.7
    45.3
    24.2
    18.7
    27.9
    16
    19.2
    19.3
    23.5

    ...

    23.3
    24.1
    19.5
    25.3
    21.7
    20.7
    21.6
    20.9
    17.3
    17.5
    22.3
    30.7
    37.9
    16.8
    61.5

    ...

    20 AABJ 25
    20 AACD 35
    20 AABB 17
    20 AABJ 25
    16 AABB 17
    20 AABB 17
    20 AABF 21
    20 AABA 16
    20 AABE 20
    16 AABD 19
    20 AABH 23
    20 AABH 23
    20 AABA 16
    25 AABA 16
    25 AABA 16
    20 AABH 23
    25 AACN 45
    20 AABI 24
    25 AABC 18
    25 AABL 27
    25 AABA 16
    25 AABD 19
    20 AABD 19
    25 AABH 23

    ...

    20 AABH 23
    20 AABI 24
    16 AABD 19
    20 AABJ 25
    20 AABF 21
    16 AABE 20
    20 AABF 21
    20 AABE 20
    16 AABB 17
    20 AABB 17
    25 AABG 22
    25 AABO 30
    25 AACF 37
    20 AABA 16
    25 AADN 61

    ...

    This is a chart of sqare rot mean (average) distances between group members of mean column values (red line) vs their actual mesured mean column value (blue line), blue line values (partially) listed in the above table.

    In this example the max of (average) distances between group members of mean column values reaches max average distance for number 17.

    (Partial) grouping results from CL2001030501AA.CEL from MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia.

    row
    column
    max
    average
    std
    336
    337
    338
    339
    340
    341
    342
    344
    345
    346
    347
    348

    ...

    338
    339
    340
    341
    342
    343
    344
    345
    347
    348
    349
    351
    352
    353
    355
    356
    357
    358
    359
    360

    ...

    326
    327
    328
    329
    330
    331
    332
    333
    334
    335
    336
    337
    338
    340
    341
    342
    343
    344
    345
    346
    347
    348
    350
    351
    352
    353
    353
    354
    356
    357
    358
    359
    360
    361
    362
    363
    364
    365
    366
    367
    369

    ...

    3
    3
    3
    3
    3
    3
    3
    3
    3
    3
    3
    3

    ...

    4
    4
    4
    4
    4
    4
    4
    4
    4
    4
    4
    4
    4
    4
    4
    4
    4
    4
    4
    4

    ...

    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6
    6

    ...

    923
    1072
    2108.3
    1066
    2148.3
    2023.5
    876
    2498.8
    1282
    948
    1358.8
    1184

    ...

    3205.5
    1143
    3003
    2536.3
    984
    967
    1258.8
    1354
    1778.3
    960
    1026
    1327
    879
    1132
    979.5
    852
    996.5
    964.3
    955.3
    973.8

    ...

    1528
    1166.5
    1010
    1271.8
    977
    1069
    923.5
    995
    878
    888.8
    1260
    1002
    891.8
    1653
    4049.8
    1077.5
    1093.5
    1150.8
    868.8
    833
    974.3
    1161
    1244
    972.3
    1077.8
    845
    845
    1400
    893.8
    1290
    1220
    1121.8
    1030
    1166
    1205
    824
    1164
    1295.5
    1384
    1499
    2106

    ...

    257.3
    203.9
    403.3
    211.2
    300.7
    326.4
    264.9
    397.9
    409.8
    191.6
    347.7
    246.6

    ...

    413.3
    182.4
    417.5
    603.3
    181.4
    589.7
    280.4
    256.5
    400.2
    216.8
    212.1
    499.7
    162.6
    176.7
    227.4
    188.2
    211.7
    257.7
    258.5
    209.1

    ...

    316.7
    293.3
    173.2
    260.6
    417.7
    226.1
    193.9
    204.2
    219.8
    200.6
    257.4
    278.7
    231.8
    339.9
    445
    264.5
    227
    221.3
    201.1
    217.6
    254.4
    232
    226.5
    176.7
    280.3
    311.2
    311.2
    294.5
    270.2
    318.3
    290.4
    320.7
    214.6
    243.7
    231.1
    169.9
    249.2
    298.1
    276.7
    350.7
    307.5

    ...

    25 ABAB 257
    25 AAML 203
    20 ABJD 403
    25 AAND 211
    20 ABCM 300
    16 ABEG 326
    20 ABAI 264
    16 ABIN 397
    20 ABJJ 409
    20 AALP 191
    16 ABFL 347
    20 AAPG 246

    ...

    20 ABJN 413
    25 AALG 182
    25 ABKB 417
    20 ACFL 603
    25 AALF 181
    25 ACEN 589
    20 ABBI 280
    25 ABAA 256
    20 ABJA 400
    25 AANI 216
    25 AANE 212
    25 ABPD 499
    25 AAKC 162
    16 AALA 176
    20 AAOD 227
    16 AALM 188
    20 AAND 211
    20 ABAB 257
    16 ABAC 258
    20 AANB 209

    ...

    25 ABDM 316
    20 ABCF 293
    25 AAKN 173
    20 ABAE 260
    25 ABKB 417
    25 AAOC 226
    20 AAMB 193
    25 AAMM 204
    25 AANL 219
    20 AAMI 200
    25 ABAB 257
    25 ABBG 278
    20 AAOH 231
    20 ABFD 339
    16 ABLN 445
    20 ABAI 264
    20 AAOD 227
    16 AANN 221
    20 AAMJ 201
    20 AANJ 217
    16 AAPO 254
    20 AAOI 232
    16 AAOC 226
    20 AALA 176
    20 ABBI 280
    16 ABDH 311
    16 ABDH 311
    25 ABCG 294
    20 ABAO 270
    25 ABDO 318
    25 ABCC 290
    20 ABEA 320
    25 AANG 214
    25 AAPD 243
    20 AAOH 231
    25 AAKJ 169
    25 AAPJ 249
    20 ABCK 298
    25 ABBE 276
    25 ABFO 350
    25 ABDD 307

    ...

    This is a chart of sqare rot mean (average) distances between group members of mean column values (red line) vs their actual mesured mean column value (blue line), blue line values (partially) listed in the above table.

    In this example the max of (average) distances between group members of mean column values reaches max average distance for number 257.

    This is the (partial) screening sequence from CL2001030509AA.CEL (leukemia datasets)

    column
    row
    max
    average
    group
    group
    group
    11
    11
    11
    11
    11
    11
    11
    11
    11
    11
    11
    11
    11
    11
    11
    11
    11
    11
    11
    11
    102
    103
    104
    105
    106
    107
    108
    109
    110
    111
    112
    113
    114
    115
    116
    117
    118
    119
    120
    121
    702
    673
    728
    620
    641
    631
    643
    708
    562
    651
    669
    689
    533
    552
    634
    712
    765
    706
    794
    769
    136 AAII
    143 AAIP
    134 AAIG
    132 AAIE
    121 AAHJ
    118 AAHG
    148 AAJE
    126 AAHO
    142 AAIO
    143 AAIP
    139 AAIL
    156 AAJM
    75 AAEL
    95 AAFP
    121 AAHJ
    160 AAKA
    166 AAKG
    96 AAGA
    138 AAIK
    123 AAHL
    AAII AAIP AAIG AAIE



    AAHJ AAHG AAJE AAHO



    AAIO AAIP AAIL AAJM







    AAKG AAGA AAIK AAHL



    AAII AAIP AAIG AAIE AAHJ



















    AAII AAIP AAIG AAIE AAHJ AAHG



















    Where averages and subgroups for the following group(s) are:


    ABFN (349) ABGA (352) ABGB (353) ABGC (354) ABGD (355) ABGE (356) ABGF (357) ABGG (358) ABGH (359) ABGI (360) ABGJ (361) ABGL (363) ABGM (364) ABGN (365) ABGO (366) ABGP (367) ABHA (368) ABHC (370) ABHD (371) ABHE (372) ABHF (373) ABHG (374) ABHH (375) ABHI (376) ABHJ (377) ABHK (378) ABHL (379) ABHN (381) ABHO (382) ABHP (383) ABIA (384) ABIB (385) ABIC (386) ABID (387) ABIE (388) ABIF (389) ABIG (390) ABIH (391) ABII (392) ABIJ (393) ABIK (394) ABIL (395) ABIM (396) ABIN (397) ABIO (398) ABIP (399) ABJA (400) ABJB (401) ABJC (402) ABJD (403) ABJF (405) ABJG (406) ABJH (407) ABJI (408) ABJJ (409) ABJL (411) ABJM (412) ABJO (414)
    ABEA (320) ABEB (321) ABEC (322) ABED (323) ABEE (324) ABEG (326) ABEH (327) ABEI (328) ABEJ (329) ABEK (330) ABEL (331) ABEN (333) ABEO (334) ABEP (335) ABFA (336) ABFB (337) ABFD (339) ABFE (340) ABFF (341) ABFH (343) ABFI (344) ABFJ (345) ABFK (346) ABFO (350)
    ABJP (415) ABKA (416) ABKB (417) ABKC (418) ABKD (419) ABKE (420) ABKF (421) ABKG (422) ABKH (423) ABKI (424) ABKJ (425) ABKK (426) ABKN (429) ABKO (430) ABKP (431) ABLB (433) ABLD (435) ABLF (437) ABLG (438) ABLH (439)
    ABCP (303) ABDA (304) ABDB (305) ABDE (308) ABDF (309) ABDG (310) ABDH (311) ABDI (312) ABDJ (313) ABDK (314) ABDL (315) ABDM (316) ABDN (317) ABDO (318) ABDP (319)
    ABLI (440) ABLJ (441) ABLK (442) ABLL (443) ABLN (445) ABLO (446) ABLP (447) ABMA (448) ABMB (449) ABMC (450) ABMD (451) ABMF (453) ABMG (454) ABMH (455) ABMI (456) ABMJ (457)
    ABCC (290) ABCD (291) ABCE (292) ABCG (294) ABCH (295) ABCI (296) ABCJ (297) ABCK (298) ABCL (299) ABCM (300) ABCN (301) ABCO (302)
    ABMN (461) ABMP (463) ABNA (464) ABNB (465) ABNC (466) ABND (467) ABNG (470) ABNH (471)
    ABBH (279) ABBI (280) ABBJ (281) ABBK (282) ABBL (283) ABBM (284) ABBN (285) ABBO (286) ABBP (287) ABCA (288) ABCB (289)
    ABNJ (473) ABNK (474) ABNM (476) ABNN (477) ABOA (480) ABOB (481)
    ABAN (269) ABAP (271) ABBA (272) ABBB (273) ABBC (274) ABBD (275) ABBE (276) ABBF (277) ABBG (278)
    ABOF (485) ABOH (487) ABOI (488) ABOJ (489) ABOK (490) ABOL (491) ABOM (492) ABOO (494) ABOP (495)
    ABAE (260) ABAF (261) ABAG (262) ABAH (263) ABAI (264) ABAJ (265) ABAK (266) ABAL (267) ABAM (268)
    ABPD (499) ABPE (500) ABPG (502) ABPI (504) ABPJ (505)
    ABAB (257) ABAB (257) ABAC (258) ABAD (259)
    ABPK (506) ABPL (507) ABPM (508) ABPN (509) ABPP (511)


    Where search through the scan CEL file values based on band limits and displayed as averaged (sub) group values CEL (CL2001030511AA.CEL) column values enables FASTA coding:

    155 378 1024 139 AAIK
    156 378 1368 147 AAJC
    157 378 1836 486 ABOG
    158 378 2340 260 ABAD
    159 378 1355 209 AANB
    160 378 1248 180 AALD
    161 378 1770 1222 AEMD

    164 378 2565 304 ABDE
    165 378 1813 197 AAMH
    166 378 2266 534 ACBG
    167 378 4675 445 ABLF
    168 378 2953 496 ABPC
    169 378 1276 245 AAPH
    170 378 2006 275 ABBH
    171 378 3954 506 ABPI
    172 378 8868 1101 AEEO
    173 378 9304 1247 AEOB
    12 138-139
    18 146-147
    199 481-492
    79 257-262
    55 209-210
    37 179-180
    404 1212-1226

    103 300-316
    49 197-201
    219 528-541
    171 427-448
    204 493-503
    73 245-249
    90 274-285
    209 502-507
    387 1098-1107
    411 1245-1254

    Where the following (sub)groups and their average values:

    binary band memebers:6 : ABPJ ABPN ABPF ABPF ABPN ABPJ
    binary band avg:5.050000e+02 upper limit:509 lower limit:501 binary band avg(int):505

    binary band memebers:4 : ABOF ABON ABOE ABOH
    binary band avg:4.872500e+02 upper limit:493 lower limit:484 binary band avg(int):487

    binary band memebers:5 : ABML ABMK ABMH ABMJ ABMD
    binary band avg:4.560000e+02 upper limit:459 lower limit:451 binary band avg(int):456

    Where described by the following data row in the file MLL_AF9.gct from www.broad.mit.edu:

    1415913_at ribosomal protein S13 455.171 505.366 457.024 500.844 487.858 496.07



    To Borce Dzinleski


    These routines were written by Dzinleski Jasenko jasenko@unet.com.mk who is the author of C/C++ based routines for encryption/decryption, large numbers operations, the 123SQL database engine and the simplified mariaBasic interpreter which are undergoing projects. This project is self-financing and any contributions are welcomed.

    This site resulted in years long support from Borce & Dusica Dzinleski and Nada Popstefanova and is devoted to them and especially to my daughter Maria Dzinleska.The author is currently seeking for a developers job and this is his cv.

    IMPLEMENTATION

    RIFF(WAV) COMPRESSION (principia example)

    This is a binary compression implementation on PDA recording device output file (16 bit, 8000 Hz, 128 kbps Wav). This functional example performs loosless wav compression on PDA recording file up to 350 sec gaining an average of 35%.

    15.05.2008 VRM 1.0.0 Download File mar70.zip


    BINARY COMPRESSION 77

    This is a binary compression based on 2-byte long data binary shifting concatenation into dictionary entries that are left truncated (common in ASCII text files). Tested on large text files produces a fast average of 40%.

    04.06.2008 VRM 1.1.0 Download File mar77.zip


    BINARY COMPRESSION 79

    This a binary compression based on right(low) bit truncation of 2-byte data into 8-bit dictionary entries also performing routine princilpe used in the bellow listed routines. It performs fast and efficien data storage.

    06.06.2008 VRM 1.1.15 Download File mar79.zip
    Get it from CNET Download.com!
    Binary Compression 79 at Brothersoft.com


    THE BINARY COMPRESSION ROUTINE

    Binary compression methods are widely used in communications, data storage and numeric analysis. Exploring genetic complexity numeric sequences employ such methods. Some of them are presented on this site together with a command-line Win32 implementation(s) that demonstrates the capability of compression of large ASCII data files and binary files and also slightly modified in numeric data sequence analysis. This binary compression method is based on numeric sequence generated by the following binary formula as presented by the C/C++ syntax: #define op_7(x,y)(((x+y)^y)|(((x&y)!=0)?(x&y)/y:0)) . This numeric sequence represents all numbers from 0-255(8-bit) for 0-127(7-bit) arguments in an x-y matrix manner. When always x=y and x:0-127 it results in all 8-bit odd numbers. When applied on a 2-byte data sequence it results in 14 or less bits long index. Combined together with one 1-bit substracting indicator it will allow compression. Using these arguments as dictionary entries coded by hi/lo/length indicators whose reocurring indexes are stored insted of the input data allows gain of an average 30% compression in large ASCII text files. This numeric sequence formula was generated by another routine written for the purpose of exploring numeric sequences generation.

    This is an compression Win32 command-line tool based on binary compression. This example states the speed and efficiency of this static large ASCII files compression method.

    Purchase Binary Compression 1.3.3 released 04.09.2007 (Price 10$ ,service Protexis.com)
    04.09.2007 VRM 1.3.3 Download File mar.zip Get it from CNET Download.com!




    THE BINARY FACTOR GROUPING COMPRESSION ROUTINE

    This compression example uses binary pattern indexing by 2-byte sequence bit truncation from 16-12 bits in order to gain max of dictionary reoccurences. This compression method is a compression gain vs unoptimized compression speed compromise.
    This example states the corectness of the genetic text complexity display routine since its dictionary covers most of the numeric sequences occurences. Yet this compression example is subject of further development.

    21.09.2007 VRM 1.4.0 Download File mar73.zip


    SECOND IMPLEMENTATION - Binary Text Compression

    This is a fast and efficient compression example that executes fast input data indexing and dictionary reoccurence search based on binary 4x4-bit long data samples. Indexed sequences are checked vs variable data length buffer.
    Thus this compression method gains speed concerning strict 4x4(16) - bit long dictionary patterns. This routine is subject of further development.

    Purchase Binary Text Compression 1.3.3 released 04.09.2007 (Price 10$ ,service Protexis.com)
    04.09.2007 VRM 1.3.3 Download File mar9.zip Get it from CNET Download.com!


    THIRD IMPLEMENTATION - ASCII Text File Fast Sort/Indexing Routine

    This is a fast sorting/indexing example that builds a file position sorting tree as a result of n-depth text file line byte sorting. The sorted sequence tree may expand to further depth levels, this routine uses default depth 6. It exibits fast sorting of a text file up to the size 100K lines/rows.
    E.g.: C:\msort -f "War and Peace NT.txt"

    30.10.2007 VRM 1.3.1 Download File msort3.zip Get it from CNET Download.com!


    THE RANDOM KEYS DISTRIBUTION ENCRYPTION ROUTINE

    This is a strong encryption/decryption routine based on a 4 number keys random seed distribution hash.
    The command line switches to encrypt are
    E.g.: C:\r7 -a <key1 number> -b <key2 number> -c <key3 number> -d <key4 number> -e "filename.txt"
    and the command line switches to decrypt are
    E.g.: C:\r7 -a <key1 number> -b <key2 number> -c <key3 number> -d <key4 number> -f "filename.txt"
    The 4 key numbers following the -a -b -c -d switches should have the values between 10000 and 99999. They are the entry seed values and are used instead of the common password protection method. Cyphering strength is high due to use of hashed number table based on 4 function rundom number distribution. This routine was written by the authors wish to try to improve message privacy while sent across the networks. Division remainders distributions are tested in the following 4 ways for number choice :

    1.1(min)...
    minmv=999;
    for(l=0;l<rsi;++l)
    {

    if(n=0||l==0){n=rs[l][1];continue;}
    if(n==rs[l][1]||n+1==rs[l][1]||n-1==rs[l][1]){n=rs[l][1];}else{


    if(minmv>rs[l][2]){minmv=rs[l][2];minl=l;}
    n=0;

    }

    }
    if(df){printf(" %d",rs[minl][0]%outm);}
    htable[hti_dmin][0]=rs[minl][0]%outm;++hti_dmin;
    ...

    1.2(max)...
    maxmv=0;
    for(l=0;l<rsi;++l)
    {

    if(n=0||l==0){n=rs[l][1];continue;}
    if(n==rs[l][1]||n+1==rs[l][1]||n-1==rs[l][1]){n=rs[l][1];}else{


    if(maxmv<rs[l][2]){maxmv=rs[l][2];maxl=l;}
    n=0;

    }

    }
    if(df){printf(" %d",rs[maxl][0]%outm);}
    htable[hti_dmax][1]=rs[maxl][0]%outm;++hti_dmax;
    ...

    2.1(min)...
    minmv=999;
    for(l=0;l<rsi;++l)
    {

    if(n=0||l==0){n=rs[l][1];continue;}
    if(n==rs[l][1]||n+1==rs[l][1]||n-1==rs[l][1])
    {


    n=rs[l][1];
    if(minmv>rs[l][2]){minmv=rs[l][2];minl=l;}

    }else{n=0;}

    }
    if(df){printf(" %d",rs[minl][0]%outm);}
    htable[hti_rmin][3]=rs[minl][0]%outm;++hti_rmin;
    ...

    2.2(max)...
    maxmv=0;
    for(l=0;l<rsi;++l)
    {

    if(n=0||l==0){n=rs[l][1];continue;}
    if(n==rs[l][1]||n+1==rs[l][1]||n-1==rs[l][1])
    {


    n=rs[l][1];
    if(maxmv<rs[l][2]){maxmv=rs[l][2];maxl=l;}

    }else{n=0;}

    }
    if(df){printf(" %d",rs[maxl][0]%outm);}
    htable[hti_rmax][3]=rs[maxl][0]%outm;++hti_rmax;
    ...

    (1) Each of the entered key numbers resultant distribution series (3-133)*(3-7) according to these criteria are written in a 4 column table
    (2) Each table is hashed according the bellow listed binary criteria
    (3) The 4 resulting tables are then re-hashed using the same binary criteria.

    #define op_A(w,x,y,z)(((((w&0x0000ffff)<<16)|x)&0xffff0000)|((((y&0x0000ffff)<<16)|z)&0x0000ffff))
    #define op_B(w,x,y,z)(((((x&0x0000ffff)<<16)|w)&0xffff0000)|((((z&0x0000ffff)<<16)|y)&0x0000ffff))
    #define op_E(w,x,y,z)(op_A(w,x,y,z)>op_B(w,x,y,z)?op_A(w,x,y,z):op_B(w,x,y,z))

    One out of the 4 functions running inside this encryption was used in the Game of life which is listed for download, and it states the diversity of random number distributions produced.

    Try looping this encryption in the following way:

    Step 1.C:\r7 -a <key1 number> -b <key2 number> -c <key3 number> -d <key4 number> -e "filename.txt"
    Step 2.C:\r7 -a <key5 number> -b <key6 number> -c <key7 number> -d <key8 number> -e "previous_output.mar"
    ...
    ...
    Step n.

    and repeat it in the same manner n times until the desired security level is gained.

    18.12.2007 VRM 1.3.3 Download File r7.zip
    Random Keys Distribution Encryption at Brothersoft.com

    MARIAHASH THE ENCRYPTION ROUTINE

    This is a fast encryption routine using proprietary hashing method. Cyphering strength depends on a large hashing number and password length. Password text must be entered in a password.txt file and should have between 50 and 100 characters.This routine was written by the authors wish to try to improve message privacy while sent across the networks.

    09.06.2007 VRM 1.3.0 Download File 79923.zip
    Get it from CNET Download.com!


    THE 123SQL DATABASE ENGINE

    This is an undergoing project aimed to construct a small portable SQL database engine for PDA's, and this is a functional browsing engine that contains data and sample browsing statements. Data may be imported together with table/column creation. Typically the source data may be spredsheet column TAB delimited export data. Database/table/column creation may be viewed in the included code following the -c switch. Table names and column names and field byte sizes should be specified, but column/field lengths my also vary in size row by row. The engine performs SQL keyword/syntax checking using the syntax/keywords list files included. Object names check and object attributes read is performed in the system database files named 123SQL_db_1.mar and 123SQL_db_2.mar. Database structure allows multiple object browsing. The sorting/searching routines require low machine resources thus meeting most modern PDA specifications and their sources were also published under different names.
    This project was founded on the authors' unique relational database engine structure design. The 123SQL engine requires the following command line syntax:
    E.g.: C:\910791 -d "Sample"
    for attaching and browsing the included database, where Sample is the database name included. When
    E.g.: C:\910791 -c "import_data_file.txt"
    the engine will create a database table and table columns as specified in the included create.txt syntax and import the data from the file name specified after the -c switch. Number of column definitions and TAB delimited fields must match, if specified column length is greater than data length space justification will occur. Supported SQL like data browsing syntax is :

    {select}

    {*|column_name|column_name_1,...column_name_n}

    {from}

    {table_name|table_name_1,...table_name_n}

    [where

    |[column_name=string_litteral|column_name>string_litteral|column_name<string_litteral]

    |[column_name>string_litteral and column_name<string_litteral]

    |[column_name[>|<]string_litteral and column_name=string_litteral]

    |[column_name=string_litteral or column_name=string_litteral or column_name=string_litteral]

    |[column_name>string_litteral and column_name<string_litteral and column_name=string_litteral]

    ]

    The MariaBasic Interpreter


    For the purpose of implementing database methods the mariaBASIC Interpreter was developed and when embeded in the engine will allow storing basic syntax like procedures into the database and executing more complex database and computing tasks.This interpreter allows basic like syntax commands like nesting, statement loops, and conditional executions. The ZIP archive ready for download includes a few .txt files which are sample basic syntax supported nesting example source procedures that executed with command line stating: E.g.: C:\9901 -e "sample.txt".
    These (sample1...5.txt) example sources show the code structure neccessary to supply the program execution and the supported routine code syntax is :

    variable declarations:
    {
    [varname$="literal"]|[varname%=number|0]|[varname&=number|0]|[varname#=number|0]
    }

    ...
    [if|computations|print]
    ...

    block statement(s):
    [if ([varname1=varname2]|[varname1>varname2]|[varname1<varname2]|[varname1>=varname2]|[varname1<=varname2]) then


    ...
    [if|computations|print]
    ...

    end if]
    [for varname1=varname2|number to number

    ...
    [if|computations|print]
    ...

    next varname1]
    [while([varname1=varname2]|[varname1>varname2]|[varname1<varname2]|[varname1>=varname2]|[varname1<=varname2])

    ...
    [if|computations|print]
    ...

    wend]
    ...
    nested block statement(s):

    [if

    [if | (computation(s)|print)]

    ]

    [if

    [if | (computation(s)|print)]

    [while

    [if | (computation(s)|print)]]

    [if | (computation(s)|print)]

    ]

    [if

    [if | (computation(s)|print)]

    [for

    [if | (computation(s)|print)]]

    [if | (computation(s)|print)]

    ]

    [for

    [if | (computation(s)|print)]

    [for

    [if | (computation(s)|print)]]

    [if | (computation(s)|print)]

    ]

    ...
    {end}


    This interpreter although functional is subject of further development and changes will occur. This package does not include all BASIC builtin functions except the standard ones and more are going to get implemented. MariaBasic, when compiled for some PDA's compilers enables a simple but efficient programming PDA tool.

    Jasenko Dzinleski at Brothersoft.com

    Jasenko Dzinleski at Download.com
    Get it from CNET Download.com!
    Jasenko Dzinleski at SourceForge.net
    Jasenko Dzinleski at SourceForge.net
    Jasenko Dzinleski at outYard.com - Find, share, sell and download digital goods
    Jasenko Dzinleski at outYard.com - Find, share, sell and download digital goods
    mariaBASIC at Brothersoft.com

    123SQL at Softpedia.com
    Jasenko Dzinleski at Softpedia.com
    123SQL at WindowsMarketplace.com

    123SQL at TigerDirect.com

    123SQL.zip 15.04.2008 VRM 1.5.0
    Get it from CNET Download.com!
    mariaBasic.zip 09.07.2007 VRM 1.3.7
    Get it from CNET Download.com!

    Purchase mariaBasic Interpreter 1.3.7 released 09.07.2007 (Price 10$,service Protexis.com)


    Here is a pair of routines written in mariaBasic:

    rem
    rem
    rem
    rem mariaBasic Sample code
    rem
    rem example: simple prime check
    rem
    rem

    var1%=0
    var2%=0
    var3%=0
    var5%=0
    var6%=0
    var7#=0
    var8&=0
    var9%=0
    var10%=0
    var11%=0

    var13%=299



    print "start"
    print " "

    for var1%=100 to 299

    print " ",var1%;

    var6%=var1%/2
    var7#=var1%/2
    var7#=var7#-var6%
    var6%=100*var7#

    var10%=0+0

    for var2%=2 to 298

    if var6%=50 then

    var7#=var1%/var2%
    var8&=var1%/var2%
    var7#=var7#-var8&
    var8&=1000000*var7#

    end if

    if var2%>=var1% then

    var2%=var13%+1

    end if

    if var8&>0 then

    var10%=var10%+1

    end if

    if var8&<0 then

    var10%=var10%+1

    end if

    next var2%

    var11%=var1%-2

    if var10%=var11% then

    print "."

    end if

    next var1%

    print " "
    print "end"

    end

    And here is a sample random generator code written in mariaBasic:

    rem
    rem
    rem
    rem mariaBasic interreter sample code
    rem
    rem example: simple random number generator
    rem
    rem


    var1%=1
    var2%=1

    var3%=11111

    var4%=0

    var51&=0
    var52&=0
    var11#=0
    var12#=0

    var4%=var3%/100

    for var1%=3 to 133

    for var2%=3 to 7

    var11#=1000*var2%/var1%
    var12#=var3%/var11#
    var51&=var12#*var4%
    var52&=var12#*var4%/1000
    var52&=var52&*1000
    var52&=var52&-var51&
    var4%=var4%+1

    if var52&>0 then

    print " ",var52&;

    end if
    if var52&<0 then

    var52&=-1*var52&
    print " ",var52&;

    end if

    next var2%

    var4%=var3%/100

    next var1%

    end

    That is equivalent to the following C/C++ code:

    //-----------------------------------------------------
    //
    // mariaRandom Generator
    //
    //
    // copyright Dzinleski Jasenko 2007
    //-----------------------------------------------------
    #include <stdio.h>
    #define seed 11111
    #define outr 133
    #define inr 7
    #define rang 1000

    int main()
    {

    int i,j,k,n;
    double v11,v12;
    long v51,v52;

    printf("\n\n\nThe mariaRandom Generator\n");
    printf("\nWritten by Dzinleski Jasenko July,2007\n");
    printf("OS Win32 VRM 1.0.1\n\n");

    k=seed/100;

    for(i=3;i<outr;++i)
    {

    for(j=3;j<inr;++j)
    {

    v11=1000*j/i;
    v12=seed/v11;
    v51=v12*k;
    v52=v12*k/rang;
    v52=v52*rang;
    v52=v52-v51;
    ++k;

    if(v52>0)
    {

    printf(" %d",v52);

    }

    if(v52<0)
    {

    v52*=-1;
    printf(" %d",v52);

    }

    }

    k=seed/100;

    }


    return(0);

    }

    And here is a game of life using this but yet improoved random number generator
    Download a game of life VRM 1.2.1 at 17.07.2007 .
    Game of life executes via the following command line switches e.g.:r31 -s 31193 -g 50 where the number following -s is the random seed number (mostly over 10000) and the number following the -g switch is the number of generations produced (mostly over 5) .
    Similar but more sofisticated random key seed number distribution is used in THE RANDOM KEYS DISTRIBUTION ENCRYPTION ROUTINE providing strong message file encryption.


    And here is the same game of life using 100x100 cells that outputs the generations data in a graphics BMP file format.
    Download a game of life VRM 1.3.1 at 17.07.2007




    THE FAST (ASCII and Unicode) TEXT FILES SEARCH ROUTINE

    This is a fast text search routine that allows single (or quoted composite) string search throughout an ASCII or Unicode text (text containing) file(s). Unicode search will also allow strings contatining mixtures of different Unicode table(s).
    E.g.:
    1. (ASCII search) msearch3 <ASCII_input_filname.txt> <search_string>
    2. (Unicode search) msearch3 <Unicode_input_filname.txt>
    (search string in Unicode file uarg.txt and search results in Unicode file ures.txt)

    03.07.2008 VRM 1.1.1 Download File msearch3.zip




    THE FAST ASCII TEXT FILES SEARCH ROUTINE

    This is a fast text search routine that allows multi string (up to 10 search strings containing one or more words within) search throughout an ascii text file. So, each search string (quoted) may have one or more words. The -s switch allows any match, while the -e switch allows only exact match.
    E.g.: C:\msearch -s(-e) "package install"+"media"+"component" -f "FreeBSD Handbook.htm"
    E.g.: C:\msearch -s(-e) "network devices installation" -f "FreeBSD Handbook.htm"
    E.g.:C:\msearch -s(-e) "trodes in his hands" -f "book_sd.txt"
    E.g.:C:\msearch -s(-e) "Bezukhov and Natasha"+"Buonaparte Napoleon"+"Pierre" -f "War_and_Peace_NT.txt"
    The program output will display all results along with their line number file positions, the unique and composite sentence search phrase matches together with their total occurence count.

    15.04.2008 VRM 1.3.3 Download File msearch.zip
    Purchase msearch 1.3.3 released 15.04.2008 (Price 10$ ,service Protexis.com) Get it from CNET Download.com!
    msearch at Brothersoft.com




    THE ASCII TEXT FILES SENTENCE CONTEXT SEARCH ROUTINE

    This is a text file complex search routine that allows text search build on the context - sentence words concerning a given subject. This search allows automated search criteria build depending on sentence words contents and user choice. Sentence words files and their sentence links are built during the indexing phase for a given text file. After indexing, the routine will display all sentences for a choosen sentence subject (as enlisted in the words file) and allow detailed context search and all sentences display concerning the choosen context.
    For the indexing type:E.g.: C:\r113 -i "War_and_Peace_NT.txt"
    For the context search type:E.g.: C:\r113 -s(e) "Bagration" -f "War_and_Peace_NT.txt"
    The -s switch enables any match search when d was choosen, and -e switch enables only exact word matching. The included files contain the examples book already indexed. Typically the search word is a name, or a subject that is beeing oftenly described and attributed in the book text. So after viewing/choosing the desired sentence/search combination all text lines containing the choosen words will be displayed. Thus viewing book contents by desired subject details requires smaller amount of time.

    15.04.2008 VRM 1.3.0 Download File r113.zip Get it from CNET Download.com!


    THE FONT IMAGE RECOGNITION ROUTINE

    This routine creates a vector shape sequence file (using -i switch) out of an 100x100 pixels 24 bit colour depth black and white image representing a character truetype image (font) or character freehand drawing. Then using the -c switch the two index files derived from two different images are compared and graphics matching result is displayed.
    For the indexing type:
    E.g.: C:\cr13 -i "Drawing1.bmp" "Drawing1_Index.txt"
    For the comparisson of two different index files type:
    E.g.: C:\cr13 -c "Drawing1_Index.txt" "Drawing2_Index.txt"
    At present the routine builds shape vectors on black/white bitmaps, it does not support different resolution nor colors/color depth.
    But how does it work?

    (1) indexing, creates vector txt file (that might be the meta character file) out of the bmp image file in the following manner:
    - inverts the b/w file matrix (the way human eye sees it),
    - searches for quadrants (10x10 pixels sized) with 40/60% b/w ratio, thus finding character image edges (up to 8 pairs in the same row),
    - creates vectors out of each qadrant,
    - shifts quadrants by (only) few pixels UP since bmp edges do not always REALLY represent character ID curves, repeating vector creation...
    and
    (2) comparison of two vector files:
    - shifts back all X-axis values subtracting them by absolute minX value,
    - computes curve angles out of each quadrant values,
    - computes resultant angles out of quadrant pairs building most real character curves,
    - compares the two vector files angle pairs,
    - computes matching statistics.


    This development is aimed for PDA users using easier ways for text input.
    To Maria Dzinleska

    27.04.2007 VRM 1.0.1 Download File cr13.zip Get it from CNET Download.com!




    THE ROUTINE THAT GENERATES THE PRIME NUMBERS KEY PAIR OUT OF THEIR PRODUCT

    These routines were written during and for the www.rsa.com prime key numbers context that requires finding the exact prime numbers key pair out of a very large (256,512...1024... bits long) product number. The routines were written in java and use the BIGINTEGER java class in order to compute the prime key pair.The starting point routine finds a prime numbers key pair with product_number_bit_length/2 bit length that give sufficient accuracy (near as far as possible) to the product number, the more the precissenes the more the computing time to spend. So the loop that computes the suggested starting prime number pair is limited with the corresponding number of equal product-target significant digits. The remaining procedures consequently perform a very long (all 1's and trailing ZEROS) 111...*10^N substractions from the suggested key pair measuring the distance (difference) from the target product number by subsequent multiplication checks. At the divergency point found and at a certain precissenes (number of equal significant digits) a new key pair may be generated through the first routine. Than the process has to be repeated while gaining more and more equal product-target significant digits.

    23.07.2006 Download File Welcome.zip

    How do these computations compute a very similar or near prime key pair out of a large product key?

    Exmining the bellow listed mariBasic code and its (partial) output shows a few number products appearing at large division loop distances and having a 0000 period between decimal remainder values. Testing those (listed) numbers might proove that most of them are prime numbers. Testing large (200 decimal or more) product keys in this way would take indefinite time. So, the WelcomeQ routine uses a substraction operation on a proposed prime keypair. The routine that generates prime keypairs that have a given decimal target product number matching is based on a binary field seed number modification basing only on target maching numbers as matching loop starting point. The substraction number (having the (decimal) value of eg 1111111111000000000000000) shifts the 1111111111 period to the right by approoving that this way truncated prime keypair product matches more and more decimals to the target product number. Actually there are sets of prime kepairs obtaining a certain decimal matching.Usually it is necessay to switch between different pairs in order to increase the decimal matching of the product. And that is the main iteration of this method sometimes requiring examining and rejecting large number of prime keypairs in order to gain one or more decimal matching more. Gaining a 100 decimals precisenes on a common PC computer thus would not be hard to achieve. These computations generate prime keys having computable decimal matching gain or complete product number matching compared to a given huge product number.

    Brief order and explanation of execution steps:

    (1) generate 5 or more (depending on computing resources) decimal matching places vs known target number prime keypairs (number of generated pairs also depends on computing resources)

    (2) start subtracting by a given number of decimal 1....1x10^X and multiplying each of primes in a keypair observing gain or loss in decimal matching at product number vs target number. Observe matching gain vs number of 1...1 and X in 10^X in the subtraction factor. Thus prime distribution at that number point becomes visible.

    (3) choose a prime probe as a base for generating new sets (depending on computing resources) of prime keypairs gaining usually somewhat less decimal matching places at product number vs target number.

    (4) iterate through the previos steps seeking a point at the prime distribution which indicates the existence of the absolute matching keypair.

    var1%=0
    var2%=1234567
    var3%=0
    var5%=2
    var6%=0
    var7#=0
    var8&=0
    var9%=0
    var10%=10000
    var191%=0
    var111#=0
    var19%=10000
    var11%=17317
    var123%=91127
    var13%=13009
    var145%=98017
    var15%=12251
    var162%=98327
    var17%=33757

    var3%=var2%/2
    while(var5%<var3%)

    var7#=var10%*var2%/var5%
    var8&=var2%/var5%
    var8&=var7#-var8&*var10%

    if var8&=0 then

    print "=";
    print var5%;
    print "@";
    print var7#;
    print " ",var191%
    var191%=0+0

    end if

    var191%=var191%+1
    var5%=var5%+1
    wend

    end


    =205759@6.000063e+04 1
    =205760@6.000034e+04 1
    =205761@6.000005e+04 1
    =246909@5.000089e+04 41148
    =246910@5.000069e+04 1
    =246911@5.000049e+04 1
    =246912@5.000028e+04 1
    =246913@5.000008e+04 1
    =308635@4.000087e+04 61722
    =308636@4.000075e+04 1
    =308637@4.000062e+04 1
    =308638@4.000049e+04 1
    =308639@4.000036e+04 1
    =308640@4.000023e+04 1
    =308641@4.000010e+04 1
    =411509@3.000097e+04 102868
    =411510@3.000090e+04 1
    =411511@3.000083e+04 1
    =411512@3.000075e+04 1
    =411513@3.000068e+04 1
    =411514@3.000061e+04 1
    =411515@3.000053e+04 1
    =411516@3.000046e+04 1
    =411517@3.000039e+04 1
    =411518@3.000032e+04 1
    =411519@3.000024e+04 1
    =411520@3.000017e+04 1
    =411521@3.000010e+04 1
    =411522@3.000002e+04 1

    Dzinleski Jasenko - jasenko@unet.com.mk

    Mailing Address:
    +38922770296
    Dositej Obradovik 15/8
    1000 Skopje Republic of Macedonia


    All published data, executables and sources from this site described above apply to GNU General Public License and can be used, copied, sold, redistributed or used in any other way only by written permission of Jasenko Dzinleski. Copyright (C) from 2001 and later by Jasenko Dzinleski
    This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
    This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
    You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.