Mark Johnson - US grants

This project is being funded through the Learning & Intelligent Systems Initiative, and is supported in part by the NSF Office of Multidisciplinary Activities in the Directorate for Mathematical & Physical Sciences. Learning in many cognitive domains, including language and vision, involves recognition of complex hierarchical structure that is hidden or only indirectly reflected in the input data. In this project a multi-disciplinary group of applied mathematicians, cognitive scientists, computer scientists, linguists, and neuroscientists will study the learning of compositional structure in language, vision, and planning, and will also probe the neural mechanisms for identifying and exploiting such structure. The research involves three interacting lines of work. The first refines and extends statistical learning models; the second applies these models to language, vision, and planning; and the third develops and applies new experimental and analysis techniques for probing the neural mechanisms that learn and exploit compositional structure. The results of the project should significantly increase our understanding of complex learning, and should have implications for a wide range of topics in education (e.g., learning of complex knowledge structures in science and math) and technology (e.g., automated speech recognition, computer vision, robotics).

9870676 Johnson This Integrative Graduate Education and Research Training (IGERT) award will support the establishment of a broadly- based graduate training program in mathematical, cognitive and computational approaches to understanding how diverse cognitive processes, including language, movement and reasoning, are learned. The basic phenomena to be addressed are of significant commercial as well as scientific importance because, for example, they form the basis for design of speech and pattern recognition software, and for the design of robotic systems. The program is a joint effort of 13 faculty from the Departments of Applied Mathematics, Cognitive and Linguistic Sciences, and Computer Science. In combination with institutional resources, NSF funds will provide stipends for 12 graduate students, 2 postdoctoral students and 9 undergraduate students each year, as well as for related costs of student research training. Graduate students will be required to satisfy existing coursework requirements of their home departments and to take at least three new IGERT advanced topics courses meant to span the three participating disciplines. During the first two years of graduate school, each student will complete an interdisciplinary research project before beginning thesis research. All IGERT graduate trainees and faculty will also participate in a weekly research seminar, biannual retreats, a yearly week-long mini- course lead by a visiting researcher and a national conference to be organized during the second year of the project. Postdoctoral fellows will jointly organize one of the advanced topics courses and will themselves receive additional training to complement that received during their own graduate studies. Undergraduate students will become involved through active involvement in summer research projects in the groups of participating faculty. IGERT is a new, NSF-wide program whose goal is to sponsor the establishment of innovative, researc h-based graduate programs that will train a diverse group of new scientists and engineers to be well-prepared for varied careers in the private and public sectors. IGERT provides an opportunity for the development of new, well-focused multidisciplinary programs that bridge traditional organizational barriers, uniting faculty from several departments or institutions to establish a highly-interactive collaborative environment for both training and research. In its first year, the program will provide support to 17 institutions for new or nascent programs that collectively span all areas of science, engineering and mathematics eligible for support by the NSF.

Statistical language models are of considerable scientific importance, where they provide insight into the importance of various information sources for language understanding, and they have significant technological applications such as speech recognition and machine translation. To date most statistical language models have concentrated on simple phrase-structure and lexical relationships. This research investigates probabilistic models capable of describing richer combinatorial structures also found in human languages. The goal of this research is to understand how humans learn a language and use it to communicate. Because these linguistic processes seem to be inherently computational in nature---they crucially involve the transformation and manipulation of information-conveying representations---this research focuses on developing explicit computational models. Besides the primary scientific goal of understanding the human language learning and processing mechanisms, the research also has important technological applications in areas such as automatic speech recognition, information extraction, summarization and indexing of on-line texts, translation of texts from one language to another by computer, etc. This research is supported by the Methodology, Measurement, and Statistics Program and the Statistics and Probability Program under the Mid-Career Methodological Opportunities Fellowship Announcement.

DESCRIPTION (provided by applicant): The studies outlined in this proposal are based on a unique genetic approach to identify genes that are essential for the function of either the male or female gametes of Arabidopsis thaliana. In particular, the focus of this proposal will be on the critical molecular events that allow the male gamete-bearing structure, the pollen grain, to germinate, invade the ovary and successfully achieve fertilization of an ovule. These experiments take advantage of the quartet mutation, which causes the four products of pollen meiosis to remain fused, without adverse effects on individual pollen grains. T-DNA mutagenesis of quartet using a reporter gene that is expressed only in pollen enables detection of insertions into genes that are essential for either male or female gametes by a rapid and efficient visual screen. Lines carrying these types of insertions can be identified because they will fail to produce individuals that are homozygous for the insertion. This approach offers a unique opportunity to study the function of genes that will not be isolated by traditional genetic screens. In addition, the T-DNA allows direct analysis of mutant pollen phenotypes so that mutations can be isolated that disrupt each of the critical steps in pollen function, allowing a thorough mechanistic study.

Language is one of the most complex aspects of human behavior, and provides the foundation for many kinds of social interaction. The question of how people learn and use language is a subject of extensive research in several behavioral sciences, including cognitive science, psychology, and linguistics. There is a long tradition of using formal approaches to explore answers to this question, and recent work has begun to emphasize the importance of statistical models. With support from the National Science Foundation, Dr. Griffiths at UC Berkeley and Dr. Johnson at Brown University will develop and investigate new methods and models for learning and analyzing natural languages based on Bayesian statistics. In Bayesian statistics, the information about the structure of language provided by linguistic data is combined with a "prior" distribution that constrains the structures under consideration. This approach can make it easier to learn the properties of a language from limited amounts of data, and has a direct connection to theories of human language acquisition that emphasize the role of constraints in learning. This research project aims to integrate the statistical models used for learning and analyzing language with two methods from modern Bayesian statistics: Markov chain Monte Carlo algorithms and nonparametric Bayesian models. These methods make it possible to apply Bayesian inference in complex models of the kind that people typically work with in cognitive science and linguistics. The results of this project will provide new ways of working with traditional models of language, and lead to new models that are potentially of relevance to explaining how people acquire language. By exploring how contemporary statistical methods can be applied to the probabilistic models used in computational linguistics, this project will build closer connections between statistics, linguistics, and cognitive science, and provide opportunities for students to receive training in topics at the intersection of these disciplines.

This award was supported as part of the fiscal year 2006 Mathematical Sciences priority area special competition on Mathematical Social and Behavioral Sciences (MSBS).

Improving our understanding of how children learn language has profound implications for a variety of fields, ranging from nature versus nurture debates to therapies for treating language impairments. Learning a language involves learning a wide variety of different things, including the words of the language and the way they combine to form phrases and sentences, as well as the distribution of sounds of the language. Little is known about the way that all these aspects of language learning interact, and the goal of this research is to identify which aspects are likely to interact and whether this interaction is synergistic. With support from the National Science Foundation's programs in Linguistics and in Human Language & Communication, Dr. Mark Johnson and Dr. Katherine Demuth will use Bayesian statistics to construct a variety of mathematical models of the language acquisition process, and by comparing these to experimental data, identify the most important synergistic interactions involved in language learning.

Fertilization is the central event in the life cycle of all sexual organisms and occurs when male and female gametes fuse. Flowering plants (including major crops like corn, rice, and soybean) undergo double fertilization: one sperm fuses with the egg cell to form an embryo and another sperm fuses with the central cell to produce endosperm, a specialized tissue that provides metabolic support for the embryo. These events lead to formation of seeds, the basis of the human diet. The molecular mechanisms responsible for the fusion of gametes are not well understood in any organism. The hap2 mutation completely blocks the ability of sperm to fertilize female gametes in Arabidopsis thaliana, a flowering plant that is an excellent genetic model organism. The HAP2 gene encodes a protein that is only expressed in sperm cells and is predicted to span the sperm plasma membrane. The hypothesis of this project is that the HAP2 protein on the sperm surface interacts with another protein on the female gamete and that this interaction is required for gamete fusion. The experiments proposed here are designed to define the role of HAP2 in fertilization, determine the topology of HAP2 on sperm membranes, identify egg and central cell-expressed proteins that interact with HAP2 and define HAP2 sequences that are critical for its function. These studies have the potential to uncover molecular mechanisms critical for fertilization in many organisms including humans. This project will provide interdisciplinary training for a graduate student and several undergraduates recruited from Brown University and The Community College of Rhode Island. In addition, novel educational materials and laboratory exercises will be developed and distributed to educators.

Mark A. Johnson
IOS-1021917
HAP2(GCS1): Mechanisms of double fertilization

The molecular mechanisms that allow gamete cells (sperm and egg) to fuse are unknown. Overcoming this gap in our knowledge is an important goal because gamete fusion is central to the life cycle of all sexually reproducing organisms. Flowering plants (including major crops like corn, rice, and soybean) undergo double fertilization: one sperm fuses with the egg cell to form an embryo and another sperm fuses with the central cell to produce endosperm, a specialized tissue that supports the embryo. These cell fusion events lead to formation of seeds, the basis of the human food system. Gaining greater understanding of gamete fusion in flowering plants will lead to technologies that can improve seed crop production and control reproduction of crop plants. The hap2-1 mutant was identified using genetic analysis in the model plant species Arabidopsis thaliana. hap2-1 mutant sperm appear normal, but fail to fuse with either the egg or the central cell. These mutants lack a protein, HAP2(GCS1), that is normally on the surface of sperm cells. The hypothesis that will be tested in this project is that the HAP2(GCS1) protein is directly involved in a process that allows the male and female gametes to fuse. The goals of this project are 1) to identify new proteins that participate with HAP2(GCS1) to mediate gamete fusion, 2) to identify proteins that regulate the function of HAP2(GCS1), and 3) to determine how HAP2(GCS1) and other proteins allow gamete cells to fuse. The results of this project will have broad scientific impacts because HAP2(GCS1) is found in many eukaryotic species and is likely part of a gamete fusion mechanism employed by many plants and animals. This project will provide cutting-edge research training for both female and male undergraduate, graduate, and post-doctoral researchers of diverse backgrounds. This project also includes an outreach component to local elementary and high school students and teachers.

Fertilization and seed production in flowering plants relies on the delivery of two sperm to female gametes by a pollen tube. The success of the long and rapid journey of the pollen tube relies on constant signaling between the pollen tube cell and the cells of the pistil. Pollen tubes must find specific cellular targets, they must burst when they arrive at the target, and their sperm must fuse with female gametes. These events are all essential for seed production, which is central to agricultural production and food security. Our goal is to understand the molecules that mediate exchanges between pollen and pistil. Cellular interactions between pollen and pistil are mediated by extracellular signaling exchanges occurring in a complex environment where multiple pollen tubes are competing for a limited number of ovules. Ovules are the structures where female gametes develop and that mature into seeds (e.g. rice grains and corn kernels) only after successful fertilization by a pollen tube. The United States will be better able to contribute to scientific progress when our scientists represent the full diversity of our nation. This project will provide cutting edge training in biological imaging, genetics and genomics for early-stage students from groups that have not traditionally been represented in US science. These students will join a team of graduate students and a post-doctoral researcher who will also receive training through this project.

This project will use unique molecular tools to define: 1) Pollen components of the signaling system that results in cessation of tube growth and sperm release in the ovule, 2) Novel components of the mechanisms required for gamete fusion, and 3) How gamete fusion triggers a mechanism that prevents multiple pollen tubes from entering a single ovule.This project will take an integrated approach to the study of fertilization in flowering plants. The molecules that must be displayed by the pollen tube for recognition by the female will be discovered. The project will also define novel components of the gamete fusion mechanism and test hypotheses aimed at understanding how gamete fusion blocks attraction of subsequent pollen tubes. The project takes advantage of unique expertise in imaging, genetics, and genomics to understand how pollen-pistil interactions are integrated to maximize seed production. Defining the molecular exchanges between pollen and pistil that maximize reproductive success will transform our ability to generate new crop varieties with resilient pollination systems.

PI: Mark A. Johnson (Brown University)

ERA-CAPS Collaborators: Jörg Becker (Instituto Gulbenkian de Ciência, Oeiras, Portugal), Frederic Berger (Gregor Mendel Institute, Vienna, Austria), Thomas Dresselhaus and Stefanie Sprunck (University of Regensburg, Regensburg, Germany), David Twell (University of Leicester, Leicester, United Kingdom), Marek Mutwil (Max Plank Institute of Molecular Plant Physiology, Potsdam, Germany), and Jose Gutierrez-Marcos (University of Warwick, Warwick, United Kingdom)

The key agricultural products derived from corn, rice, wheat, and tomato all require successful reproduction by fertilization. The ability to increase crop production in the coming years will depend on developing a better understanding of reproductive mechanisms. The goal of this project is to take what has been learned about the molecules that mediate fertilization in a model plant species (Arabidopsis) and extend this knowledge using a comparative genomic approach in seven plant species including corn, rice, and tomato. The expected findings will allow for the identification of specific mechanisms that are targeted by environmental stresses during sexual reproduction in crops and will assist in the selection of stress-resistant cultivars. The outputs of this project will provide a deeper understanding of the evolution of sexual reproduction of economically important plant species. In addition this project will provide cutting edge training in biological imaging, genetics and genomics in the context of a critical crop plant (tomato) for a diverse team of researchers that will include a post-doctoral fellow and an undergraduate researcher. Importantly, this research team will be benefit from participation in this large, multidisciplinary and international effort comprising eight laboratories from five nations.

Research during the past five years has delivered tremendous new insights into gamete physiology and the mechanisms involved in fertilization in Arabidopsis. This progress has established the view that gametes are hyper-differentiated cell types with highly specific transcriptional profiles. Advances in microscopy based on fluorescent reporters and live cell imaging have also transformed research capability and provided insights into the mechanisms involved in gamete delivery, interaction and activation of seed development. Yet, the understanding of the complexity of double fertilization that characterizes flowering plants is far from complete and lacks any knowledge about the origin of mechanisms that predate double fertilization. In this project, emerging models representing key stages in plant evolution will be used to provide insight into the ancestral mechanisms of gamete differentiation and fertilization. Gene co-function networks will be identified from expression atlases for several plant species that include the liverwort Marchantia, the moss Physcomitrella and the extant basal flowering plant Amborella. These will be complemented with co-function networks from Arabidopsis and the economically important crops maize, tomato and rice and used to study the conservation of gene co-function networks governing male and female gametogenesis, pollen tube growth and fertilization mechanisms in flowering plants. Moreover, these investigations will provide novel molecular markers of fertility in crops. It is expected that this project will result in identification of fertilization factors that were lost from ancient angiosperms during the evolution of monocots (grasses) and eudicots and those which have evolved de novo in the angiosperm lineage. All data produced will be freely and continuously shared within the consortium. Specifically, RNAseq datasets will be accessible through a consortium database as well as through publicly available data repositories.

PROJECT SUMMARY Solving the complex problems in human health and modern biology represents a major challenge for those who will lead biomedical research in the near and long-term future. The core disciplines of our training program?molecular biology, cell biology, and biochemistry?have led the way in development of innovations that are driving life sciences research and applications today. It is imperative that US life scientist training programs evolve to meet the demand for a diverse group of leaders who are trained in rigorous and transparent implementation and reporting of quantitative analysis of biological data. We recognize that this demand will require a change in training culture that focuses on a high standard of professional development for trainers and trainees. The objectives of this predoctoral training program are to: (1) Build and sustain an equitable and inclusive training environment for an increasingly diverse group of PhD students. (2) Integrate training in the design and implementation of rigorous and transparently reported experimentation throughout the program. (3) Integrate training in quantitative and computational approaches throughout training program. (4) Integrate career exploration and student professional development throughout the program. Faculty trainers in the Molecular Biology, Cell Biology, and Biochemistry Graduate Program (MCBGP) are accomplished scientists who are drawn from 11 Departments at Brown University and the Warren Alpert Medical School. The mission of the MCBGP is to train the next generation of leaders in biomedical research to probe the molecular mechanisms of cellular and biochemical processes by building and sustaining an equitable and inclusive training environment in which a diverse group of PhD students will successfully gain quantitative, conceptual, technical, and professional skills that will allow them to conduct the rigorous and reproducible research that interdisciplinary life science demands. The MCBGP admits 9-14 students per year based on their research and academic potential. During the first or second year of graduate study, trainees will be selected from the ~20 eligible MCB graduate students for appointment to the training grant on the basis of their potential for success in research. Each year, 4 first-year and 4 second-year predoctoral students will be supported; funds to support 8 trainees per year are requested.