Phenotype information

 

An organism's phenotype (Greek phainein, 'to show', and typos, 'type'), is the composite of its observable characteristics or traits, such as morphology, development, biochemical or physiological properties, and behaviors, and products of behavior. A phenotype results from the expression of an organism's genetic code, its genotype, as well as the influence of environmental factors and interactions between the two (G x E = P). When two or more clearly different phenotypes exist in the same population of a species, that species is polymorphic.

 

Modern genotype-phenotype theory was first proposed by Wilhelm Johannsen in 1911 to make clear the difference between an organism's heredity and what that heredity produces. Wilhelm Johannsen first used the terms phenotype and genotype in his paper, Om arvelighed i samfund og i rene linier ("On heredity in society and in pure lines") and in his book Arvelighedslærens Elementer which was rewritten, as Elemente der exakten Erblichkeitslehre, introducing the term gene as coined in opposition to Darwin’s pangene.

 

The genotype-phenotype difference is similar to that proposed by August Weismann, who distinguished between germ plasm (heredity gametes) and somatic cells (body). Multicellular organisms consist of germ cells containing heritable information, and somatic cells that carry out ordinary bodily functions. The germ cells are primarily influenced, neither by environmental contingent, nor by learning, or somatic morphological changes that happen during the lifetime of an organism. Effects are therefore one-way, germ cells produce somatic cells and are not affected by anything somatic cells learn, nor retain any ability an individual acquires. Genetic information cannot pass from soma to germplasm and therefore on to the next generation individual.

[The genotype-phenotype distinction should not be confused with Francis Crick's central dogma of molecular biology, which is a statement about the directionality of molecular sequential information flowing from DNA to protein, and not the reverse. “The Central Dogma states, that once 'information' has passed into protein it cannot get out again. The transfer of information from nucleic acid to nucleic acid, or from nucleic acid to protein may be possible, however transfer from protein to protein, or from protein to nucleic acid is impossible…” 1958]

 

Richard Dawkins in 1978 and in the 1982 book The Extended Phenotype suggested that bird nests and other built structures such as caddisfly larvae cases and beaver dams can be considered as "extended phenotypes". A phenotype that included all effects that a gene has on its surroundings, including other organisms, arguing that "An animal's behavior tends to maximize the survival of the genes 'for' that behavior, whether or not those genes happen to be in the body of the particular animal performing it." The main idea is that phenotype should not be limited to biological processes such as protein biosynthesis or tissue growth but extended to include all effects that a gene has on its environment, inside or outside the body of the individual organism.

 

Three elements of the extended phenotype

 

  • 1.       Architectural constructions

  • 2.       Manipulating other organisms

  • 3.       Remote action on a host

 

[Not to forget the relevance of Richard Dawkins’s 1976 Selfish Gene and the idea of inclusive fitness over individual fitness, and connected to that social-psychological meme concept, as driven parallel to biological evolution, by variation, mutation, competition, and inheritance.]

 

Molecular extensions of phenotype include traits or characteristics which can be made visible by some technical procedure, extending to the idea of "organic molecules" or metabolites that are generated by organisms.

Phenotypic variation (due to underlying heritable genetic variation) is a fundamental prerequisite for evolution by natural selection.

 

The word phenome is sometimes used to refer to a collection of traits, while the simultaneous study of such a collection is referred to as phenomics. Phenomics is an important field of study because it can be used to figure out which genomic variants affect phenotypes which then can be used to explain things like health, disease, and evolutionary fitness.

RGB phenotyping June update

 

Today, imagery has proven to be the single most important data collection method in field phenotyping, due to a fairly mature digital photographic technology consumer product set which includes cameras and software. Moreover, common RGB imagery from consumer grade cameras used in proximal sensing, like the new GoPro Hero6, allow reasonable quality high resolution and high throughput optical sensing, for a total field plots imagery collection with GPS information result. This RGB imagery type has been shown effective, as reported in many 2018 publications, in determining vegetation cover, height, and greenness related indices and pixel fractions which correlate well with yield as they are sensitive to green biomass.

 

Therefore it is clear that to support best terrestrial phenotyping field collection methodology, we include imagery and imagery analysis as a standard practice. That application could start with a single GoPro action camera mounted in nadir view and continuously imaging all four crop rows as exampled on Wolverine PSC. Or multiple cameras could be used in coordination to image from multiple angles, such as our logger triggered three Nikon cameras array on Professor PSC. At minimum, one representative plot image is sufficient for color, shape and intrinsic spatial gridded analysis phenotyping approaches.

 

Regardless, the frequency of images collected is a concern in order to balance the volume of data cost, with the benefit ability to detect phenotyping traits in a significant way. Pictures take 100x or more storage space and processing resource, compared to analog signal based “tri-metric” type methods. Furthermore, compute requirements are raised another 10x if best spectral accuracy is to be preserved using RAW formatting.

 

Beyond the relatively easy acquisition and storage of imagery information, it is the lack of efficient processing that bottlenecks phenotyping imagery based results. Future software is expected to effectively solve many of the current processing problems, however, securing the human expertise to associate the data conversion, correction, projection, radiometric, spatial, and statistical processes steps, in a batched and documented way, is paramount. Currently, multiple software is used to step image data through appropriate analysis in somewhat custom applications, requiring either a dedicated expert collaborator, or substantial investment by the principal investigator. (Similar to how our field phenotyping platforms are custom applications requiring substantial specialized integration and operational input to succeed.)

 

Avenger program has incorporated proximal RGB imagery since 2015 in an expanding effort and sometimes with high data and operational cost. Now with more experience and the new 2018 GoPro product, I expect better collection and easier data upload to servers going forward. However the compute with FIJI, R or MATLAB image libraries, Python-based Agisoft SfM, and GIS-based spatial image analysis with ENVI or Imagine is needed for future data, and data fusion, and will be valuable across our archive.

 

Future image compute will specifically look to Bayesian functional regression analysis where dimensionality is reduced using basis functions, such as harmonic Fourier basis for period type curves, or else B-spline basis piece-wise polynomials defined by knots for non-periodic functions. Functional regression assumes that sample trajectories belong to a finite-dimensional space generated by a truncated basis and have smoothed functional objects as predictor variables rather than sample points, to therefore analyze curves not points.

 

Finally, it is the operation notes, along with images, which play a valuable role in displaying transient field contingent anomalies such as weeds or minor soil artifacts, and document general experimental condition, often they are used to select representative data for analysis or help explain data presented in publication. 

What’s in a picture?

 

Is a picture worth a thousand words?

 

I am thinking more of an environmental image, as a visible band component projected map. Using a pin-hole camera model, where the reflected ray structure converges artificially, to collapse the field of view agricultural target into a digital grid event recording.

 

An image is a pixel map of space. It encodes spatial parameters natively and therefore is more than the sum of its pixel values. Images resolve realistic shape and color features. In-silica volumetric model reconstruction integrates multiple images which resolve the same physical feature from different angles. Note that motion video imagery easily yields many more lower resolution frame captures than does still image photography.

 

RAW imagery format retains the underlying pixel manipulation tables, allowing image re-calculation, while compressed encodings would only retain the typical EXIF camera metadata. RAW imagery is important if the absolute spectral qualities of the visible radiation are warranted. Physical optical distortions and sensor edge effective sensitivities can be accounted, while generally controlling the photographic exposure settings is necessary for image set comparability.

 

A pretty picture looks good to the human aesthetic. For as visual animals, human cognitive perception is typically dominated by a stereo, visible frequency reality element imprinting. However, for deeper path to knowledge and extra-dimensional numeric awareness, compute image technologies are more strictly data-driven, often with the multiple view angles and stacked hyperspectral type frequency components. Be sure to retain a process descriptor and audit chain meta-recording for quality. And so it appears the future of imagery lies with pictures for which people do not look.

 

In field phenotyping, RGB imagery is proving effective in capturing green pixels representing plants. Furthermore, normalized ratios between recorded color patterns are sensitive to traits like plant biomass and nitrogen status. Imaged leaf colorations and orientations reveal plant health, and plants and plant elements, such as fruiting bodies, can be counted using imagery.

 

So what will be seen in a picture, tomorrow?

What’s in a picture?

 

Is a picture worth a thousand words?

 

I am thinking more of an environmental image, as a visible band component projected map. Using a pin-hole camera model, where the reflected ray structure converges artificially, to collapse the field of view agricultural target into a digital grid event recording.

 

An image is a pixel map of space. It encodes spatial parameters natively and therefore is more than the sum of its pixel values. Images resolve realistic shape and color features. In-silica volumetric model reconstruction integrates multiple images which resolve the same physical feature from different angles. Note that motion video imagery easily yields many more lower resolution frame captures than does still image photography.

 

RAW imagery format retains the underlying pixel manipulation tables, allowing image re-calculation, while compressed encodings would only retain the typical EXIF camera metadata. RAW imagery is important if the absolute spectral qualities of the visible radiation are warranted. Physical optical distortions and sensor edge effective sensitivities can be accounted, while generally controlling the photographic exposure settings is necessary for image set comparability.

 

A pretty picture looks good to the human aesthetic. For as visual animals, human cognitive perception is typically dominated by a stereo, visible frequency reality element imprinting. However, for deeper path to knowledge and extra-dimensional numeric awareness, compute image technologies are more strictly data-driven, often with the multiple view angles and stacked hyperspectral type frequency components. Be sure to retain a process descriptor and audit chain meta-recording for quality. And so it appears the future of imagery lies with pictures for which people do not look.

 

In field phenotyping, RGB imagery is proving effective in capturing green pixels representing plants. Furthermore, normalized ratios between recorded color patterns are sensitive to traits like plant biomass and nitrogen status. Imaged leaf colorations and orientations reveal plant health, and plants and plant elements, such as fruiting bodies, can be counted using imagery.

 

So what will be seen in a picture, tomorrow?

USDA in Maricopa, Plant Group team, Avenger phenotyping program

 

LeeAgra AvengerPro modified rig has run as a terrestrial phenotyping platform, in a 2.0 type version enhanced mode, from 2015 to 2019, whereby the basic phenotyping analog 5Hz tri-metric, plant displacement, surface temperature, and multi-bandpass spectral sensing produced by initial program researchers in 2011 to 2014 (engineering by Mr. Strand), was upgraded relative to improved DAC, and power supply, and augmented with FLIR thermographic recording, LiDAR technology, navigational 9-axis recording, ambient micro-meteorological environmental sensing, and including a hodgepodge of consumer-grade video and still imaging. To generate a total of about 200GB of data per hour at a 1.5mph typical travel speed, we included up to a dozen cameras; the single FLIR thermal imager resolving between 1-10Hz thermography has been the biggest data generator. Hyperspectral recordings, wind, and increased quality image capture are wanted. Where a third phase program upgrade is planned with new hardware and best practices to be implemented for an increased production target by spring of 2019. Of special interest to me is coding in LabVIEW for increased sampling, and integrating primary time coordinate structures and other data controls to output specific standard format and support modularized pre-processing function.

 

Base fusion elements in terrestrial phenotyping analog signal “tri-metric” sensing

  1. Plant height, as a function of biological form volumetric displacement

  2. Plant temperature, as a function of target area surface thermal signature

  3. Plant reflectance, as a function of apparent visible and NIR surface reflectivity

 

Plant height – displacement sensors (120-180KHz ultrasonic sonar, with NIR laser ToF)

  • UC2000, ToughSonic14, and Honeywell ultrasonic displacement (with LidarLiteV3)

 

Plant temperature – non-contact thermometers

  • Apogee SI-131 infrared radiometers

 

Plant reflectance – spectral reflectance sensors

  • CropCircle ACS-470, [passive options from Skye or Meter Group]

 

Major data generating and support components around the tri-metric

  • Trimble R6 RTK GPS – precision geopositioning for data geospatial reference

  • VectorNav VN-100 AHRS – precision navigational MEMS device

  • SICK LMS511 LiDAR – ruggedized laser point cloud generator

  • FLIR A600 series thermal imagery – 600x480 grids

  • National Instruments PXIe – robust modular data acquisition system

  • Campbell Scientific environmental logging – voltage and communications recording

  • Dynasys APU and power rectification – clean power generation and supply

 

Good value phenotyping cameras: GoPro Hero6, Garmin VIRB XE, Transcend DrivePro220