Composing is Important! Suggested perusing: Clean, Well-lit Sentences: A Guide to Avoiding the Most Common Errors in Grammar and Punctuation by Janis BellSlide 2
Writing is Important! Suggested perusing: Eats Shoots and Leaves: The Zero Tolerance Approach to Punctuation by Lynne TrussSlide 3
Biology Lab Background III/Properties of Light September 15, 2008 Overview of Microscopy Dr. BehonickSlide 4
Topics for now … Biology Lab Background III Experimental Design Types of Data Properties of LightSlide 5
Experimental configurationSlide 6
The Scientific Method Make Observation(s) from http://asweknowit.net/MIDDLE_SCH/DWA_7_scientific_method.htmSlide 7
Types of examinations in vitro "inside the glass" performed outside a living creature in a controlled domain (ex = in a test tube) in vivo "inside the living" performed in/on living tissue of in place life form ex vivo "out of the living" performed in/on living tissue in fake environment outside life form from which it was collected (ex = cell society) in silico p erformed altogether on PC or by PC reproductionSlide 8
Variables variable = what is measured or controlled in an analysis free variable = variable you have control over, what you can pick and control (value(s) you are controlling, otherwise called "controlled variable") subordinate variable = what you measure in test (what is influenced amid investigation, reacts to autonomous variable)Slide 9
Examples impact of various dosages of a medication (IV) on seriousness of sickness indications (DV) impact of various amounts of manure (IV) on how your home plants develop (DV) would need to control different variables like water, soil, size of pot, time in sun, and so forth impact of various water temperatures (IV) on how quick a sugar solid shape will break up (DV) impact of paper towel brand (IV) on how much water can be doused up with one paper towel sheetSlide 10
Cause & Effect change in ward variable (impact) control of free variable (cause)Slide 11
Controlling for Bias blindedness - controlling for cognizant/oblivious inclination in exploration misleading impact = subject getting fake treatment reports change in manifestations (notwithstanding absence of genuine substance treatment) because of desire or conviction that it will work spectator predisposition = mistake in perception/estimation when onlookers overemphasize practices they hope to discover & neglect to notice conduct they don\'t expectSlide 12
Controlling for Bias single-visually impaired study = subjects are blinded however experimenters are not ex = subject does not know whether accepting medication or sugar pill experimentor either can\'t be blinded because of outline of study or doesn\'t should be on the grounds that can\'t present further inclination twofold visually impaired study = both subjects and experimenters are blinded subjects haphazardly doled out to gatherings, experimenters don\'t know assignments expert rundown of gathering assignments kept by outsider until test completed "triple-blind" study = twofold visually impaired study in which individual translating results is likewise blinded (ex = analyst)Slide 13
What is sufficient verification? factual examinations searching for examples in your information in the wake of representing arbitrariness/vulnerability and utilizing this data to draw surmisings about procedure/populace being concentrated on are these outcomes a sufficiently major arrangement for me to mind? are these outcomes because of arbitrary possibility? are these outcomes generalizable?Slide 14
What is sufficient evidence? test size = # of perceptions (or bits of gathered information) that constitute an outcome ex = # of subjects per bunch in trial in case you\'re attempting to put forth a general expression around a populace, greater specimen size more exact proclamationSlide 15
Controls! inner verification for your analysis negative control - demonstrates that a negative result is conceivable in your framework positive control - demonstrates that a positive result is conceivable in your framework "What else could have brought about watched impact?"Slide 16
Baking bread: a story of good controlsSlide 17
Baking bread: a story of good controls does yeast Dani found in the back of the cooler still work? test outline controlsSlide 18
Baking bread: a story of good controls negative control positive control demonstrates a negative result is conceivable demonstrates a positive result is conceivableSlide 19
Baking bread: a story of good controls negative result positive resultSlide 20
Types of DataSlide 21
NOTE dat um = solitary a solitary estimation, result, and so forth dat a = plural a gathering of estimations, results, and so on.Slide 22
Types of Data quantitative information - numerical information size of estimation has size (a few things are greater than others) ex - tallness, cholesterol level subjective information - not numerical information, might be unmitigated or spellbinding size of estimation is an arrangement of unordered classes ex - sorts of trees, sorts of mixesSlide 23
Quantitative Data portrayed as far as numerical amount discrete information - there are just a limited # of qualities conceivable & values can\'t be subdivided and still important (ex - populace information) consistent information - information that can be measured on a continuum (physical estimations are for the most part this kind of information); can have any # esteem and be subdivided and still significant can be shown in outlines, tables, charts, histograms can be examined utilizing insightsSlide 24
Qualitative Data depicted on premise of relative attributes shading shape surface temperature smell taste (for the most part not utilized as a part of exploration science) at times considered "less profitable" by examination researchersSlide 25
Example 1 Qualitative information - privateers convey parrots while ninjas don\'t.Slide 26
Example 1 Quantitative information - there are 6 privateers & 2 ninjas.Slide 27
Example 2 hot fudge sundae subjective information frosty to touch rich surface serving glass is dreary & straightforward quantitative information serving temperature is - 10 o C serving glass is 6 inches in stature cost $6.95Slide 28
Microscopy Data microscopists gather both subjective & quantitative information subjective information shade of example general structure of example state of cells kind of cells present & their area quantitative information how much greater is one example (or one particilar district of an example) versus another? what number of cells are in one a player in an example versus another?Slide 29
from Behonick, et al. (2007) PLoS OneSlide 30
Data Collection precision = closeness of measured quality to a standard worth. exactness is autonomous of accuracy. ex - if in lab you acquire a weight estimation of 3.2 kg for a given substance, yet known weight is 10 kg, then your estimation is not exact (not near known worth) accuracy = the closeness of 2 or more estimations to each other. exactness is free of precision. ex = in the event that you measure 10 kg substance 5 times & get 3.2 kg every time, then your estimation is exceptionally exact however not precise.Slide 31
Accuracy versus AccuracySlide 32
Properties of LightSlide 33
Light voyages @ 186,000 miles/sec ≈ 669,600,000 miles/hour can consider light stream of modest particles/vitality parcels ( photons ) a wave ( light waves ) we\'ll stay with this understandingSlide 34
The thing about waves … they\'re comprised of vitality , not make any difference at the shoreline at the laundromatSlide 35
<cue straightforward symphonious wave animation>Slide 36
Measuring Waves period (T) = time to one complete wave cycle recurrence ( ) = # periods per unit time; measured in Hz ex = # waves that pass a specific point in space amid particular time interim wavelength ( l ) = separation between same point on 2 successive waves (ie - 2 consecutive pinnacles, 2 successive troughs) adequacy (A) = greatest separation from most astounding purpose of top to harmony in 1 wave cycle A measure of time required to finish = TSlide 37
The thing about waves … they\'re comprised of vitality , not make any difference at the shoreline at the laundromat light waves ~ water waves however needn\'t bother with medium to travel through can move through medium or vacuum quickest in vacuum, moderate down in medium vitality in light waves = electrical & attractive fields light = electromagnetic radiationSlide 40
1 m 10 6 nm 10 6 nm 10 –5 nm 1 nm 10 –3 nm 10 3 nm 10 3 m Micro-waves Radio waves Gamma beams X-beams UV Infrared Visible light 380 450 500 550 600 650 700 750 nm Shorter wavelength Longer wavelength Lower vitality Higher vitality EM Spectrum wavelengths 400 – 700 nm constitute unmistakable light for people higher recurrence lower recurrenceSlide 41
EM Resources EM Wave Propagation Tutorial http://micro.magnet.fsu.edu/preliminary/java/electromagnetic/index.html Basic EM Wave Properties Tutorial http://micro.magnet.fsu.edu/groundwork/java/wavebasics/index.htmlSlide 42
transmitted reflected Properties of Light assimilatedSlide 43
Microscopy Techniques target light source transmitted brilliant field stage DICSlide 44
target light source Transmitted Light plentifulness object = pigmented or recolored sample ex = histology examples seen w/brightfield microscopy stage object ex = most natural specimens seen w/stage or DIC microscopySlide 45
Objects and transmitted light wave sufficiency object seen as shading stage object not seenSlide 46
contrast = distinction in shading & light between parts of an article/picture requred to see an item by magnifying instrument can originate from varieties in power (DIC, stage) shading (splendid field, fluorescence)Slide 47
Contrast cells commonly are straightforward (not abundancy objects) stage protests low conversely differentiate producing systems transform stage contrasts into force contrasts so we can see unstained cells utilizing transmitted light ex = DIC microscopy .
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