Landmines & Zombies Taking on Chronic Fa6gue 2 Bachelor of - PowerPoint PPT Presentation

Landmines & Zombies Taking on Chronic Fa6gue

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Bachelor of Engineering & Bachelor of Science (IT) Honours thesis on landmine removal 3

2001 (final year) Afghanistan

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Medical Blood tests OK Wiki Wisdom Thyroid check OK Iron Levels OK Get more sleep Sleep Apnea NEGATIVE Try anything BedOme rouOne Eat carbs “Nothing wrong Gluten free More exercise with you” Lactose free Don’t Nap Dairy free Nap MeditaOon Muscle relaxaOon 6 yrs 8

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TesOng AnOfungal Amino acid Other/Vitamins medicaOons Supplements Vit E Faecal Profiling Nilstat Ornithorne Fish Oil Ampicillin Hair tests Tyrosine Vit C Difflucan Blood tests Ubiquinol BioOn ECC Saliva Glutamine Bile Acid Amphotercin corOsone tesOng Enzymes ProbioOcs Creon Lumbrokinase Mineral Diets Low Supplements starch Iron No starch Cal-Mag Low carb Molybdenum Dairy free Zinc No red meat 10 Sugar free

Date Wellness Notes Diet Medications start this column here: total diet: asparagus, celery, red cpsicum, spinach, almonds, meat, herbal starting taking: 6 parex, 4 teas, eggs for brekky. Exceptions nilstat, 4 amphotercin 10mg 8/09/10 0.35 noted lozenge, probioplex, hydrozole 9/09/10 0.1 " " 10/09/10 0.1 had a glass of wine " " 11/09/10 0.65 had coffee and a shot of whisky at night " " 12/09/10 0.1 " " 13/09/10 0.4 glass of wine at night " " glass of wine at night (my bday!) also didn't have 14/09/10 0.1 lunch. No meat today, only fish (salmon) for dinner. " " 1/2 glass of wine, switched from beef/chicken to fish removed all meat other than fish 15/09/10 0.4 as only meat. Also had first Difflucan tonight. from above diet same plus difflucan today 16/09/10 1 felt 99% normal ! 1/2 glass wine at night " back to same minus difflucan

Date Wellness Notes Diet Medications start this column here: total diet: asparagus, celery, red cpsicum, spinach, almonds, meat, herbal starting taking: 6 parex, 4 teas, eggs for brekky. Exceptions nilstat, 4 amphotercin 10mg 8/09/10 0.35 noted lozenge, probioplex, hydrozole 9/09/10 0.1 " " 10/09/10 0.1 had a glass of wine " " 11/09/10 0.65 had coffee and a shot of whisky at night " " 12/09/10 0.1 " " 13/09/10 0.4 glass of wine at night " " glass of wine at night (my bday!) also didn't have 14/09/10 0.1 lunch. No meat today, only fish (salmon) for dinner. " " 1/2 glass of wine, switched from beef/chicken to fish removed all meat other than fish 15/09/10 0.4 as only meat. Also had first Difflucan tonight. from above diet same plus difflucan today 16/09/10 1 felt 99% normal ! 1/2 glass wine at night " back to same minus difflucan

Minefield Clearance Time Model ln CLEARTIME . ln FRAGS AREA = β + β + β 0 1 2 . ivUXO . ln( AP 1 ) u + β + β + + 3 5 i Effect of statistically significant variables (Transformed model) Constant Fragment Slope Area slope AP 2 =85.2% R (lnTeamHours) (lnTeamHours/lnFrags) (lnTeamHours/sqrtArea) (lnTeamHours/lnAP) Base figure -1.71 0.531 0.00345 0.014 Additional effects Land use Grazing, Irrigation, Road Agricultural 0.00076 Residential 0.023 Significant UXO No Yes 0.15 Hard surface No Yes 0.014 Vegetation Bushes, Grass, None Prickly bushes 0.015 Trees 0.00112 Total ? ? ? 0.014 13

408 days of data 39 Variables - 1 output variable (wellness) - 1 weekend/holiday indicator - 5 dietary indicators - 32 supplements/medicaOons 14

A Personal Wellness Model WELLNESS f ( x ) f ( x ) f ( x ) = β + β + β + β 0 1 1 1 2 2 2 3 3 3 ... f ( x ) + + β n n n MulOple Regression Stepwise procedure to opOmise variable form 15

Coefficient: 7.0% Factor 1 P-value: 0.0000006 Weekends/Holidays OpOmal form: 100% on Day 0 16

Coefficient: 6.9% per 1000mg Factor 2 P-value: 0.0000010 Tyrosine OpOmal form: 100% on Day 0 20

Coefficient: -5.9% Factor 3 P-value: 0.07 Red meat OpOmal form: 33% from Days 0, -1 and -2 23

Results Summary Effect Level of Factor Effect Magnitude 6mescale Certainty High Holidays Good +7% Same day (p=0.00) High Tyrosine Good +7% Same day (p=0.00) Moderate CumulaOve Red meat Bad -6% over ~3 days (p=0.07)

I got a bit excited… 26

What I learned I thought I learned The medical profession is my There are things I can do to only hope help myself Chronic faOgue is too That’s what staOsOcs are for! complicated to analyse The data is too rough Rough and regular is enough It is ‘only’ subjecOve I do know my own body measurement 27

What I learned I thought I learned The medical profession was There are things I can do to my only hope help myself Chronic faOgue was too That’s what staOsOcs are for! complicated to analyse The data is too rough Rough and regular is enough It is ‘only’ subjecOve I do know my own body measurement 28

What I learned I thought I learned The medical profession was There are things I can do to my only hope help myself Chronic faOgue was too That’s what staOsOcs are for! complicated to analyse The data was too rough Rough and regular is enough It was ‘only’ subjecOve I do know my own body measurement 29

Chris Bartley What I learned chris@empirikalsystems.com Perth, Western Australia I thought I learned The medical profession was There are things I can do to my only hope help myself Chronic faOgue was too That’s what staOsOcs are for! complicated to analyse The data was too rough Rough and regular is enough It was ‘only’ subjecOve I do know my own body measurement 30