CHAPTER 4
APPLICATION OF INSTRUMENTATION
      1. Detection of Biophoton Emission From
      2. Partial Transparent Sample

        As application of the instrumentation, we use partial transparent samples to measure the biophoton emitted from these samples.

          1. Experiment
          2. The background is measured first, then put samples inside container sitting in between two multiplier tubes. The counting curves are shown below:

            Fig.4.1 Counting for Sample 1 Background

             

            Fig.4.1 Counting for Sample 1

             

            Fig.4.3 Counting for Sample 2 Background

             

            Fig.4.4 Counting for Sample 2

            Fig.4.5 Counting for Sample 3 Background
             


            Fig.4.6 Counting for Sample 3

            Fig.4.7 Representation of Axis

            As Fig.4.7 indicates, x axis represents time in nanosecond, perpendicular axis represents number of counts.

          3. Analysis
Experiment data are listed in following tables:

 

 

 

Table 4.1 Background
 
Total counts
Peak counts
Channel no. at peak
Counts/minute
2 PMTS not blocked in between
3580
59
672
2.486
2 PMTS totally blocked
275
9
619
0.191
2 PMTS not blocked in between 

scintillator on

2712
40
608
1.928
 

 

 

 

 

Table 4.2 Sample 1 (lettus leaf)
 
Total counts
Peak counts
Channel no. at peak
Counts/minute
Empty cuvette
1284
17
672
0.915
Cuvette + water
1589
28
670
1.103
Sample in cuvette
1100
23
674
0.938
Sample + water  

in cuvette

1350
29
677
0.766
 

 

 

 

 

 

 

 
 
Table 4.3 Sample 2 (fish eggs)
 
Total counts
Peak counts
Channel no. at peak
Counts/minute
Cuvette holding  

water

3459
58
679
2.402
Sample in water in cuvette
3233
62
663
2.245
 

 

 

 

 

Table 4.4 Sample 3 (half transparent fish)

 

 
Total counts
Peak counts
Channel no. at peak
Counts/minute
Plastic bag with water
15288
274
623
16.987
Sample in plastic bag hlding water
15250
238
636
16.944
  For sample 1, we use lettus leaf to measure its biophoton emission and background.

For sample 2, we use fish eggs to measure their biophoton emission. Compare the background counts and count rate with sample, we can see, the count rate with sample is larger than background at peak but total counts are less than background.

For sample 3, we use live half transparent fish to measure its biophoton emission. Compare the two measurements in Fig.4.3 and Fig.4.4, the count rate with sample is smaller than background. Possible reasons can be:

    1. Absorption of sample
    2. Plastic bag holding water and fish also has some effects on measurement.
    3. Scattering and absorption effect of water.
 
      1. Randomness Analysis of Random Sequences Recorded From Instrumentation Using Neural Network Method
Computers and humans are good at doing different kinds of things. Neural networks are human attempts to simulate and understand what goes on in nervous systems, with the hope of capturing some of the power of these biological systems.

Neural network computations on RNA sequences can be used to demonstrate that data compression is possible in these sequences.[22] According to reference 22, a random sequence would be incompressible, while structured sequences could show varying degrees of compressibility.

        1. Method
        2. As an application of the instrumentation of this paper, we use physical method to get "random" numbers and use recirculate neural network to analyze these numbers for the compressibility. If the numbers are periodically random, they can be compressed. If the numbers are really random, they can not be compressed.

          Comparison is made between computer generated random numbers and our physical instrumentation generated random numbers.

          In experiment, the physical random numbers are gathered by recording time sequences of each count appearing at different channel numbers on Multichannel Analyzer. The channel range then is divided into four equal regions. Each region is assigned one of T, C, A, G which are standard nomenclature for RNA.

          The channel numbers are converted into T / C / A /G determined by their residence at different regions. We have following region assignment:

          Channel 100--649 T

          Channel 650--1199 C

          Channel 1200--1749 A

          Channel 1750--2300 G

          Upon this time a sequence of T C A G has been obtained.

          Using computer program to convert each character into binary number, for example, G--001, C--010, T--110, A--011,

          We get a random sequence of 0 and 1 with 12 inputs and 12 outputs. (Total input 1512 numbers. Total output 1512 numbers in experiment)

          Number of hidden layers and times of learning for physical random sequence and computer generated random sequence at the same reconstruction error are listed in table 4.5:

          Table 4.5 Number of hidden layers and times of learning for computer generated random sequence and instrumentation generated random sequence at Reconstruction Error = 0.01.
         
        number of hidden layers
        times of learning 
        12
        11
        10
        9
        8
        7
        6
        5
        4
        3
        computer generated random sequence
        23810
        590752
        2138239
        3234054
        Critical 

        524624

        physical random sequence by instrumentation
        113254
        159730
        147278
        162335
        201309
        316335
        488851
        486083
        593101
        Critical 

        62511

        Before getting to critical point, the reconstruction error is kept at 0.01. When reaches critical point, the reconstruction error is 0.2580 for instrumentation random sequence and 0.2600 for computer random sequence.
      1. Conclusion
Based on our data in section 4.2.1, we can see that instrumentation random sequence is more compressible than computer generated random sequence. As we see, instrumentation random sequence can be compressed to 3 hidden layers to reach critical point while computer random sequence can only be compressed to 8 hidden layers to reach critical point. The randomness of physical random sequence depends on how the data are gathered and also on how many numbers are gathered.