Change of address
3 months ago in Variety of Life
Discussions on the interface between Science and Society, Politics, Religion, Life, and whatever else I decide to write about.
|  | 
| Honeycomb | 
 Last Fall I posted on a paper "Iridovirus and microsporidian linked to honey bee colony decline." describing an association between a virus a fungus and the honey bee deaths (colony collapse disorder, CCD) occurring all over the world. At the time I raised several concerns and expressed my skepticism regarding the major conclusions.
Last Fall I posted on a paper "Iridovirus and microsporidian linked to honey bee colony decline." describing an association between a virus a fungus and the honey bee deaths (colony collapse disorder, CCD) occurring all over the world. At the time I raised several concerns and expressed my skepticism regarding the major conclusions. The reason for this is a signal-noise problem. In these types of approaches, you want to remove the strong signals, which overwhelm the data set, to identify the weak signals. The problem is that as we get closer to the limit of detection the difference between a bona fide weak signal and garbage (aka noise) becomes negligible. Think of an eye chart. Your eye is the detector and you want to identify letters. At the top, most people can identify the 'E', which has a strong signal because its so damn big. As you go down the chart it becomes more difficult to identify the letters. With my glasses on I can identify all the letters with 100% accuracy. Without my glasses and from a distance of about 18 inches, I got 1 wrong on line 6 (~83% accuracy), 3 wrong on line 7 (~57% accuracy), and all wrong on line 8 (0% accuracy). So as the signal got weaker, smaller sized letters for my eye detector, my accuracy was less. Importantly, I could see there was something there, but what I interpreted the letter to be was wrong. So, if we asked a population of people to identify these letters, there would be a strong detection of 'E' 'F' and 'P' in lines 1 and 2 and poor detection of 'D' 'O' and 'C' in line 8. The noise comes in because if we ask people, our detectors, to come up with answers on line 8, instead of 'D' 'O' and 'C', we may often get 'P' 'Q' and 'O' respectively. This is our noise. There's something there and we know it, but it is below our ability to accurately figure out what it is. All those Iridovirus sequences identified by Bromenshenk et al may be noise.
The reason for this is a signal-noise problem. In these types of approaches, you want to remove the strong signals, which overwhelm the data set, to identify the weak signals. The problem is that as we get closer to the limit of detection the difference between a bona fide weak signal and garbage (aka noise) becomes negligible. Think of an eye chart. Your eye is the detector and you want to identify letters. At the top, most people can identify the 'E', which has a strong signal because its so damn big. As you go down the chart it becomes more difficult to identify the letters. With my glasses on I can identify all the letters with 100% accuracy. Without my glasses and from a distance of about 18 inches, I got 1 wrong on line 6 (~83% accuracy), 3 wrong on line 7 (~57% accuracy), and all wrong on line 8 (0% accuracy). So as the signal got weaker, smaller sized letters for my eye detector, my accuracy was less. Importantly, I could see there was something there, but what I interpreted the letter to be was wrong. So, if we asked a population of people to identify these letters, there would be a strong detection of 'E' 'F' and 'P' in lines 1 and 2 and poor detection of 'D' 'O' and 'C' in line 8. The noise comes in because if we ask people, our detectors, to come up with answers on line 8, instead of 'D' 'O' and 'C', we may often get 'P' 'Q' and 'O' respectively. This is our noise. There's something there and we know it, but it is below our ability to accurately figure out what it is. All those Iridovirus sequences identified by Bromenshenk et al may be noise.|  | 
| Paradise Birdwing, a lepidopteran | 

|  | 
| Fungi live here | 
|  | 
| E. dermatitidis |