C L Patten

Bio: C L Patten is an academic researcher from University of Waterloo. The author has contributed to research in topics: Bacteria & Pseudomonas putida. The author has an hindex of 7, co-authored 8 publications receiving 3529 citations.

Role of Pseudomonas putida Indoleacetic Acid in Development of the Host Plant Root System

Abstract: Many plant-associated bacteria synthesize the phytohormone indoleacetic acid (IAA). While IAA produced by phytopathogenic bacteria, mainly by the indoleacetamide pathway, has been implicated in the induction of plant tumors, it is not clear whether IAA synthesized by beneficial bacteria, usually via the indolepyruvic acid pathway, is involved in plant growth promotion. To determine whether bacterial IAA enhances root development in host plants, the ipdc gene that encodes indolepyruvate decarboxylase, a key enzyme in the indolepyruvic acid pathway, was isolated from the plant growth-promoting bacterium Pseudomonas putida GR12-2 and an IAA-deficient mutant constructed by insertional mutagenesis. The canola seedling primary roots from seeds treated with wild-type P. putida GR12-2 were on average 35 to 50% longer than the roots from seeds treated with the IAA-deficient mutant and the roots from uninoculated seeds. In addition, exposing mung bean cuttings to high levels of IAA by soaking them in a suspension of the wild-type strain stimulated the formation of many, very small, adventitious roots. Formation of fewer roots was stimulated by treatment with the IAA-deficient mutant. These results suggest that bacterial IAA plays a major role in the development of the host plant root system.

Bacterial biosynthesis of indole-3-acetic acid

Abstract: Production of the phytohormone indole-3-acetic acid (IAA) is widespread among bacteria that inhabit the rhizosphere of plants. Several different IAA biosynthesis pathways are used by these bacteria, with a single bacterial strain sometimes containing more than one pathway. The level of expression of IAA depends on the biosynthesis pathway; the location of the genes involved, either on chromosomal or plasmid DNA, and their regulatory sequences; and the presence of enzymes that can convert active, free IAA into an inactive, conjugated form. The role of bacterial IAA in the stimulation of plant growth and phytopathogenesis is considered.

Biochemical and genetic mechanisms used by plant growth promoting bacteria

Abstract: Overview of plant growth-promoting bacteria nitrogen fixation siderophones auxin production regulation of plant ethylene levels binding of bacteria to plants biocontrol mechanisms deliberate environmental release of bacteria.

Regulation of indoleacetic acid production in Pseudomonas putida GR12-2 by tryptophan and the stationary-phase sigma factor RpoS.

Abstract: The phytohormone indole-3-acetic acid (IAA) accumulates in the culture medium of the plant growth-promoting bacterium Pseudomonas putida GR12-2 only when grown in the presence of exogenous tryptoph...

Genetics and molecular biology of Azospirillum

Abstract: Genetic manipulation of Azospirillum spp has facilitated a better understanding of the mode of action of this plant-growth promoting bacterium and should help to improve its ability to stimulate plant growth and development This review considers and discusses Agospirillum plasmids, promoter sequences, the isolation of Azospirillum mutants, the genetic transformation of Azospirillum, the transfer of foreign genes into Azospirillum by conjugation and the Azospirillum genes that have been isolated and characterized The Azospirillum genes that are discussed include genes involved in nitrogen fixation, plant root attachment, phytohormone biosynthesis, tryptophan biosynthesis, carbon metabolism and a few other less well characterized processes

Machine Learning : A Probabilistic Perspective

Abstract: Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Dynamic bayesian networks: representation, inference and learning

Abstract: Dynamic Bayesian Networks: Representation, Inference and Learning by Kevin Patrick Murphy Doctor of Philosophy in Computer Science University of California, Berkeley Professor Stuart Russell, Chair Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and bio-sequence analysis, and KFMs have been used for problems ranging from tracking planes and missiles to predicting the economy. However, HMMs and KFMs are limited in their “expressive power”. Dynamic Bayesian Networks (DBNs) generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete random variable. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linear-Gaussian. In this thesis, I will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in DBNs, and how to learn DBN models from sequential data. In particular, the main novel technical contributions of this thesis are as follows: a way of representing Hierarchical HMMs as DBNs, which enables inference to be done in O(T ) time instead of O(T ), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T ) space instead of O(T ); a simple way of using the junction tree algorithm for online inference in DBNs; new complexity bounds on exact online inference in DBNs; a new deterministic approximate inference algorithm called factored frontier; an analysis of the relationship between the BK algorithm and loopy belief propagation; a way of applying Rao-Blackwellised particle filtering to DBNs in general, and the SLAM (simultaneous localization and mapping) problem in particular; a way of extending the structural EM algorithm to DBNs; and a variety of different applications of DBNs. However, perhaps the main value of the thesis is its catholic presentation of the field of sequential data modelling.

The enhancement of plant growth by free-living bacteria

Abstract: The ways in which plant growth promoting rhizobacteria facilitate the growth of plants are considered and discussed. Both indirect and direct mechanisms of plant growth promotion are dealt with. Th...

Structure and functions of the bacterial microbiota of plants

Abstract: Plants host distinct bacterial communities on and inside various plant organs, of which those associated with roots and the leaf surface are best characterized. The phylogenetic composition of these communities is defined by relatively few bacterial phyla, including Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria. A synthesis of available data suggests a two-step selection process by which the bacterial microbiota of roots is differentiated from the surrounding soil biome. Rhizodeposition appears to fuel an initial substrate-driven community shift in the rhizosphere, which converges with host genotype–dependent finetuning of microbiota profiles in the selection of root endophyte assemblages. Substrate-driven selection also underlies the establishment of phyllosphere communities but takes place solely at the immediate leaf surface. Both the leaf and root microbiota contain bacteria that provide indirect pathogen protection, but root microbiota members appear to serve additional host functions through the acquisition of nutrients from soil for plant growth. Thus, the plant microbiota emerges as a fundamental trait that includes mutualism enabled through diverse biochemical mechanisms, as revealed by studies on plant growth–promoting and plant health–promoting bacteria.

Plant Growth-Promoting Bacteria: Mechanisms and Applications

Abstract: The worldwide increases in both environmental damage and human population pressure have the unfortunate consequence that global food production may soon become insufficient to feed all of the world's people. It is therefore essential that agricultural productivity be significantly increased within the next few decades. To this end, agricultural practice is moving toward a more sustainable and environmentally friendly approach. This includes both the increasing use of transgenic plants and plant growth-promoting bacteria as a part of mainstream agricultural practice. Here, a number of the mechanisms utilized by plant growth-promoting bacteria are discussed and considered. It is envisioned that in the not too distant future, plant growth-promoting bacteria (PGPB) will begin to replace the use of chemicals in agriculture, horticulture, silviculture, and environmental cleanup strategies. While there may not be one simple strategy that can effectively promote the growth of all plants under all conditions, some of the strategies that are discussed already show great promise.