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USDA Nodulation Data in GRIN

2007-02-06T06:59:10Z

USDA Nodulation Data in GRIN

Joseph H. Kirkbride, Jr. and John H. Wiersema

After discussions with the Directorate of the International Legume Database and Information Service (ILDIS), a single field was created in their database for rhizobial nodulation data in 1986. Input of nodulation data into the ILDIS database had three phases: 1) entering the nodulation data from Allen and Allen (1981) with each datum referenced to that work, 2) reviewing and entering literature reporting legume nodulation published after 1979, and 3) examining and verifying the original references cited by Allen and Allen (1981) and attaching those references to the data in the ILDIS database. In 1986, Mrs. Ethel K. Allen and the University of Wisconsin Press gave their permission for the nodulation data in the Allens’ book to be incorporated into the ILDIS database, and this phase was completed in 1990. The third phase was completed in early 2001. The second phase is complete up to the present, and current literature is being monitored for new data. The ILDIS Directorate gave their permission for us to use the nodulation data in the ILDIS database.

The USDA Germplasm Resources Information Network (GRIN) contains information on the USDA germplasm holdings. The ILDIS nodulation data are now incorporated into GRIN, and GRIN data are available via the World Wide Web. GRIN was selected to host the data because it could be made available on the WWW and to facilitate it being updated by USDA personnel. An access web page for the nodulation data is now available on the GRIN web site:

http://www.ars-grin.gov/~sbmljw/cgi-bin/taxnodul.pl

Interested users are requested to try out the web page and send reactions and opinions of the overall interface, ease of use, data available, and the formatting of reported data.

The Introductory matters, including explanations of how to use the search page for the rhizobial nodulation data are not yet available. Hopefully they will be linked to this web page in the coming months via a subject bar down the right-hand side of the page. The default is to obtain all types of nodulation data (positive and negative root nodulation and positive stem nodulation); if one type or another is not desired, then uncheck the box(es) for that type(s). Queries can be made based on the traditional legume classification of Polhill, genus, species, infraspecific taxon, or by geographical unit (country or state/province). Geographical queries will be effective only for those taxa for which country/state distribution data exist in GRIN. Currently about 3,500 of the 4,700 accepted taxa, represented by 6,650 nodulation reports in GRIN, also have distribution data. The geographical queries can be either for the actual taxa with nodulation reports of a geographical unit or for the genera of a geographic unit with nodulation reports anywhere in the world.

For example if the GRIN nodulation data is queried for the genus Anadenanthera, a web page is returned with seven taxa listed for which nodulation reports exist. Two are accepted species, A. colubrina and A. peregina, three are infraspecific taxa, A. colubrina var. cebil, A. colubrina var. colubrina, and A. peregina var. falcata, and two taxa are synonyms, A. falcata and A. macrocarpa.

If a user clicks on a taxon name, the GRIN taxonomy page for that name will be displayed, which includes Pohill’s subfamilial classification, place of original publication, infraspecific taxa, common names, economic uses, distribution, references, synonyms, links to other databases, and links to images. Following the taxon name in square brackets are symbols indicating the class(es) of nodulation report(s) known for the taxon, R+, R–, S+, positive and negative root nodulation reports and positive stem nodulation reports, respectively. If a user clicks on a symbol, the complete bibliographic reference will be displayed.

Clicking on the abbreviated journal citation, for example Pesq. Agropecu. Brasil., will open a window with the complete journal name, Pesquisas agropecuaria Brasiliera, Rio de Janeiro.

The first users to contact us have already sent us 54 new nodulation reports. We hope that interested persons will access the USDA nodulation data and send us their comments at joe@... before we finalize the web presentation.

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2006-07-13T03:19:58Z

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How to create biological intelligence (true artificial intelligence)

2006-06-01T01:59:15Z

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How to do experimentation for constructing a bio-nano-electro computer
from geobacter/e.coli bacteria ?

S. B. Khadkikar, Department of Atmospheric and Space Sciences,
University of Pune, Pune, INDIA.

Erach A. Irani, IKB Research Institute, 106 B. Desai Road, Mumbai 400
036. INDIA. [ erach27 [at] yahoo.com ]

Motivation: many attempts to train biology have succeeded. All
attempts to teach computers to learn have failed. Attempts to teach
computers "Artificial Intelligence" have to teach intelligence on
top of adaptive abilities and evolving abilities. Bacteria are
naturally adaptive and evolving so we have to train only intelligence.

1. Mutate the geobacter bacteria with carbon nanotubes so that
they cannot respond as they are used to having carbon nanotubes
sticking on the exterior but grow tentacles like neurons. Brain cells
in mammals do not reproduce. If they reproduce the information is lost
since the wiring connections are lost.
2. Use only the geobacter bacteria mutated above with e.coli.
mutated as above. The carbon nanotubes form transistors on the skin of
the bacteria which will later act as nerves with mutation.
3. Make a mixture of geobacter / e.coli bacteria. The
geobacter bacteria are attracted to electrodes and form colonies on it
(biofilms) using nano-wires of iron compounds called pili. The e.coli.
are used because they are more intelligent presumably and live in the
intestine where time varying food supplies come and keep moving around.
Eventually we are hoping for the bacteria to be a genetic hybrid of
geobacter / E. Coli.

1. Make a grid of 30 * 30 electrodes ( 300 * 300 electrodes if
possible). Dip it into the beaker and make a pattern of
"right-shifting current on the electrode grid", until the bacteria
respond to it. You can check whether the bacteria have responded to it
by seeing if the current take-ups by the bacteria are more or less.
Geobacter bacteria are used in fuel cells (check google.com search on
"geobacter bacteria fuel cells" and also check www.geobacter.org
for a $25 million project on geobacter bacteria).

2. Once the geobacter bacteria respond to right-shift, apply
left-shift, and then both simultaneously, and then "a", "b",
"c", "d" cycling, then finally XOR and Tic-tac-toe. You need
not use a microscope to see if the geobacter bacteria are responding to
the time-varying patterns and predicting which patterns come next ---
just see how much current is delivered to the electrodes.

How to make a computer from geo-bacter bacteria mutated as above ?

1. Once the bacteria respond to patterns as above apply DIFFEerent
neural net training patterns. Read the original parallel distributed
processing books --- "Parallel Distributed Processing by McClelland
and Rumelhart". These bacteria should be able to learn at electronic
speeds since the nano-tubes act as semi-conductors. Thus what a person
learns in 40 years they will learn in seconds or minutes.

2. REWARD FOR THE BACTERIA USING BIO-CHEMICALS

When the bacteria do a complicated task give them a dose of pleasant
bio-chemicals (to them) besides simply food. Computers cannot be
rewarded in a sense that they appreciate (do computers appreciate
anything ?), bacteria can be rewarded.

Bibliography

1. Dr Dobb's AI Newsletter, "Evolving Computation in Bacterial
Collectives",
http://www.ainewsletter.com/newsletters/aix_0512.htm#erach

(www.google.com search for "AI Newsletter Erach Irani")

2. "Intelligent bacteria?",
http://www.world-science.net/exclusives/050418_bactfrm.htm

(www.google.com search for "bacterial intelligence")

3. "Microbial Intelligence",
http://en.wikipedia.org/wiki/Microbial_Intelligence

(www.google.com search for "bacterial intelligence")

4. "Bacterial wisdom, Godel's theorem and creative genomic
webs", Eshel Ben-Jacob,
www.iscid.org/boards/ubb-get_topic-f-1-t-000139.html

(www.google.com search for "bacterial intelligence Eschel")

5.http://iwarrior.uwaterloo.ca/?module=displaystory&story_id=1652&format=html&edition_id=35

(www.google.com search for "25,000 rat neurons flying F22
simulator")

6. "Slime mould used to create first robot run by living cells,
Alok Jha, science correspondent",
http://www.guardian.co.uk/science/story/0,,1709944,00.html

(www.google.com search on making a robot move using a slime cell)

7. "De novo reconstitution of a functional mammalian urinary
bladder by tissue Engineering", Frank Oberpenning, Jun Meng, James J.
Yoo, and Anthony Atala.
http://www.frontier.kyoto-u.ac.jp/ca04/text/hanakin/nbt0299_149.pdf

8. "First Bladders Grown in Lab Transplanted, Breakthrough Shows
Promise for Creating Other Human Organs",

http://www.washingtonpost.com/wp-dyn/content/article/2006/04/03/AR2006040301387.html

8. D.E. Rumelhart, J. L. McClelland, and the PDP Research Group.
"Parallel Distributed Processing: Explorations in the Microstructure
of Cognition, Volume I and II". MIT Press, 1986.
9. Dr Dobb's AI Newsletter, Entire issue is on biological
intelligence, http://www.ainewsletter.com/newsletters/aix_0512.htm

10. Obtaining carbon nanotubes from grass. Zhenhui Kang et al 2005
Nanotechnology 16 1192-1195 doi:10.1088/0957-4484/16/8/036

[Nanotechnology 16 1192] (6.13.05)

11. "Scientist Revs Up Power of Microbial Fuel Cells in Unexpected
Ways",

http://www.geocities.com/erach27/BacterialBioFilmMakeElectricity.txt

12. http://www.geobacter.org/research/nanowires/

13. Information on Geobacter bacteria. http://www.geobacter.org

14. Christopher Voigt. Online internet lecture. Programming sight,
touch, and thought into E.Coli.
http://esmane.physics.lsa.umich.edu/wl/external/ICSB/2005/20051020-umwlap001-03-voigt/real/sld006.htm

(www.google.com search for "programming bacteria")

15. Paras Chopra.
http://www.paraschopra.com/blog/post/biohacking/106/BioIntelligence-Creating-chess-playing-bacteria

BIOGRAPHY

Prof. S. B. Khadkikar, MSc, PhD is an internationally recognized
theoretical physicist and is a retired senior professor from Physics
Research Laboratory, Ahmedabad. He is cited in Marquis Who's Who
(science and engineering, world, Asia) in the years 1996-2006. He has
about 80 publications in renowned international journals, mainly in
nuclear physics.

Dr Erach A. Irani, B.Tech Computer Science (IIT Mumbai, India), M.Tech
Computer Science (University of Minnesota, Minneapolis, USA), PhD
Computer Science (University of Minnesota, Minneapolis) has worked all
his life in Computer Science and now theorizes with Prof S. B.
Khadkikar. He is a US Citizen. He has over 20 publications to his
credit while doing his studies at the University of Minnesota, mainly
in computer science and applications of Computer Science to medicine.

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