San Diego, CA, United States
San Diego, CA, United States

San Diego State University is a public research university in San Diego, and is the largest and oldest higher education facility in San Diego County. Founded in 1897 as San Diego Normal School, it is the third-oldest university in the 23-member California State University . SDSU has a student body of more than 35,000 and an alumni base of more than 260,000.The Carnegie Foundation has designated San Diego State University a "Research University with high research activity," placing it among the top 200 higher education institutions in the country conducting research. In the 2009–10 academic year, the university obtained $150 million for research, including $26 million from the National Institutes of Health. The university soon expects to be classified as "Doctoral/Research-Extensive." As reported by the Faculty Scholarly Productivity Index released by the Academic Analytics organization of Stony Brook, New York, SDSU is the number one small research university in the United States for four academic years in a row. SDSU sponsors the second highest number of Fulbright Scholars in the state of California, just behind UC Berkeley. Since 2005, the university has produced over 40 Fulbright student scholars.The university generates over $2.4 billion annually for the San Diego economy, while sixty percent of SDSU graduates remain in San Diego, making SDSU a primary educator of the region's work force. Committed to serving the diverse San Diego region, SDSU ranks among the top ten universities nationwide in terms of ethnic and racial diversity among its student body, as well as the number of bachelor's degrees conferred upon minority students.San Diego State University is a member of the Western Association of Schools and Colleges, the American Association of State Colleges and Universities , the National Association of State Universities and Land-Grant Colleges, and the Southwest Border Security Consortium. Wikipedia.


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Patent
San Diego State University | Date: 2015-03-05

The invention provides methods for preparing boronic acids, for example, primary alkyl or alkenyl boronic acids, and alkali metal alkyl trifluoro borate salts, as described herein, wherein the primary alkyl boronic acids and the potassium alkyl trifluoroborate salts can contain one or more unprotected functional groups.


The invention is directed to computer implemented methods, systems, and devices for improving a reconstruction model, e.g. historical precipitation reconstruction model for a given region, by applying a standard multivariate regression analysis to the reconstruction model to obtain a truncated sampling error variance from sampling the first set of empirical orthogonal functions, wherein the reconstruction model is improved when the reconstruction is combined with the quantified minimum sampling error.


Waters E.R.,San Diego State University
Journal of Experimental Botany | Year: 2013

Small heat shock proteins are a diverse, ancient, and important family of proteins. All organisms possess small heat shock proteins (sHSPs), indicating that these proteins evolved very early in the history of life prior to the divergence of the three domains of life (Archaea, Bacteria, and Eukarya). Comparing the structures of sHSPs from diverse organisms across these three domains reveals that despite considerable amino acid divergence, many structural features are conserved. Comparisons of the sHSPs from diverse organisms reveal conserved structural features including an oligomeric form with a β-sandwich that forms a hollow ball. This conservation occurs despite significant divergence in primary sequences. It is well established that sHSPs are molecular chaperones that prevent misfolding and irreversible aggregation of their client proteins. Most notably, the sHSPs are extremely diverse and variable in plants. Some plants have >30 individual sHSPs. Land plants, unlike other groups, possess distinct sHSP subfamilies. Most are highly up-regulated in response to heat and other stressors. Others are selectively expressed in seeds and pollen, and a few are constitutively expressed. As a family, sHSPs have a clear role in thermotolerance, but attributing specific effects to individual proteins has proved challenging. Considerable progress has been made during the last 15 years in understanding the sHSPs. However, answers to many important questions remain elusive, suggesting that the next 15 years will be at least equally rewarding. © 2012 The Author(s).


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: National Robotics Initiative | Award Amount: 612.21K | Year: 2016

This project addresses the creation of innovative decentralized controllers for legged locomotion. Decentralized controllers require only local information to accomplish their function. In the case of legged locomotion, decentralization is desirable for several reasons. For prosthetics, where the purpose is to replace a lost natural limb, it is impractical to wire the user with a profusion of sensors. Therefore the prosthetic device must primarily rely on its own built-in measurements. Another advantage of decentralization is the management of complexity. As robots become more sophisticated, the number of variables that must be monitored for a complete description of the system status becomes so large that top-down controllers are costly or infeasible to implement. The challenge of decentralized control is made substantially more difficult because walking and running are hybrid dynamic behaviors, that is, the dynamics follow a completely different set of rules when, for example, a foot is planted on the ground, compared to when it is swinging in the air. This project will address the substantial analytical difficulties caused by these features. This project will advance the state of the art in advanced lower limb prosthetics, as well as in locomotion for the next generation of legged robots.

This project will investigate the systematic design of decentralized feedback controllers that coordinate low-dimensional subsystems to achieve robust legged locomotion, overcoming the curse of dimensionality in legged robots and enabling cooperative human-machine walking with powered prosthetic legs. The project draws upon robotics, optimization, and feedback control theory to advance two key innovations: (1) creating algorithms to systematically design robust stabilizing decentralized controllers for cooperative subsystems; and (2) transferring the decentralized control framework into practice with an experimental quadruped and a powered prosthetic leg. The problem of creating decentralized nonlinear controllers for robust dynamic walking with interconnected subsystems, coordinated only by a common gait cycle phasing variable, will be formulated in the context linear and bilinear matrix inequalities. The theoretical significance of these algorithms include: (1) they are powerful tools for the design of general nonlinear decentralized feedback control schemes; (2) they explicitly account for underactuation to account for walking motions that are not flat-footed; (3) they provide cooperation between subsystems of complex walking models with high dimensionality and strong interactions; and (4) they provably stabilize full-dimensional hybrid dynamical models of walking robots rather than simplified models. This decentralized control framework is technologically significant because it can be readily transferred into practical high-DOF legged robots, as well as wearable robots for physical rehabilitation.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: ARCTIC NATURAL SCIENCES | Award Amount: 211.36K | Year: 2017

The element, chlorine, is not normally studied in natural environments, except in areas that have been contaminated with toxic compounds like pesticides, industrial solvents or radioactive 36Cl. It is often assumed that chlorine enters non-contaminated ecosystems mostly in the form of chloride (the same negative ion in table salt), and that chloride does not interact with plants or soil microorganisms. However, there is growing evidence that chloride is taken up and transformed by plants and soil microorganisms into complex chlorine-containing organic compounds. In environments where oxygen is scarce, some bacteria can use these chlorinated organic compounds instead of oxygen in a form of anaerobic respiration called organohalide respiration (OHR). In this way, these bacteria can quickly use up energy sources that would otherwise be used to produce methane. This means that an active biological chlorine cycle could reduce the amount of methane that is released into the atmosphere. Methane is a strong greenhouse gas, trapping about 30 times as much heat per molecule as carbon dioxide. The Arctic region has been warming faster than the rest of the planet, and large amounts of organic carbon are stored in Arctic soils. It is thus important to understand how much soil carbon will be lost to the atmosphere in the form of carbon dioxide or methane, since these two gases have different effects on the climate the climate system. This project measures rates of biological chlorine cycling in locations across the Arctic Coastal Plain of northern Alaska, and tests whether organohalide respiration does in fact significantly reduces methane production in these areas. This project could inform models of greenhouse gas emissions, improve understanding of the fate of chlorinated contaminants in Arctic soils, and further the basic science of biological chlorine cycling. The project will involve students at a minority-serving institution (San Diego State University) and a high school teacher, who will lead broader outreach and education efforts.

The proposed research addresses the following two questions: (1) Does OHR inhibit methanogenesis via competition for H2? (2) How does the relative magnitude of Cl cycling and its relationship to CH4 flux change along a coastal-inland gradient in the Arctic Coastal Plain? The experimental approach consists of a field survey that compares CH4 fluxes and indicators of Cl cycling along a gradient of coastal influence from Barrow to the foothills of the Brooks Range, and a laboratory incubation experiment to study the relationships among OHR, methanogenesis, other terminal electron acceptor processes, and H2 availability. Indicators of Cl cycling include sizes and transformation rates of soil Cl pools, metagenomes describing the relative abundance of genes and microbial taxa associated with Cl cycling and other anaerobic processes, and 37Cl and 36Cl isotopic analysis to infer the dominant Cl cycling processes and to constrain long-term cycling rates. The laboratory incubation will follow anaerobic processes (OHR, iron reduction, methanogenesis, acetogenesis, and sulfate reduction) in microcosms varying in Clorg and H2 concentration to establish the thermodynamic hierarchy among these processes and whether competition is alleviated by increased H2.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: RES ON GENDER IN SCI & ENGINE | Award Amount: 665.65K | Year: 2016

One explanation for the underrepresentation of some ethnic minority groups in STEM education and STEM careers is that cultural barriers are perceived to exist that render integration of a cultural identity incompatible with an emerging identity as a scientist. In particular STEM careers appear to be disconnected from being able to serve ones community or give back to the place where one grew up. This research will examine how this communal cultural orientation along with some perceptions of science in general might influence how engaged students are in STEM courses and how interested they are in STEM careers. The researchers propose a mixed methods study including interviews, focus groups, a longitudinal survey study and randomized experimental classroom activities to examine how underrepresented minority students cultural and career purpose orientations influence their perceptions of science careers and whether that perception could be altered through targeted activities. Such activities, if proven effective, might have the potential to be more broadly included in STEM curricula and pedagogy to encourage greater participation of underrepresented groups in STEM. Student participants will be recruited from the California State University, Long Beach campus which is a Hispanic Serving Institution (the Hispanic population on the campus is approximately 33%). About 46% of incoming freshmen indicating an interest in the physical or life sciences are identified as underrepresented minorities.

The theoretical framework includes goal congruity theory (person-environment fit) and interest theory. The main hypothesis is that underrepresented minority students struggle to maintain an interest in and develop a strong identity with science if they do not see a career in science that would allow them to fulfill culturally connected communal purpose goals. The researchers model emphasizes the critical role of goal congruence (or fit) in predicting interest in and motivation for science careers, with mediating roles for science class interest and engagement and science identity.

The researchers will engage three phases of work: interviews and focus groups; longitudinal survey study; and a large randomized experimental classroom study. Interviews will commence in year one and include 100 undergraduate freshman students with declared STEM majors and 50 additional students recruited in year two. Data analysis will be done using qualitative content analysis and descriptive analysis of demographic data. The longitudinal survey phase will involve recruiting a sample of freshmen and sophomore students (approximately 250 of each) followed for the three years of the study. Six categories of variables will be included in the data gathering beginning with background variables and moving on to communal goal endorsement, science and communal goal affordance perceptions, science class interest and engagement, science identity, and science career interest and motivation. Data analysis will follow linear growth curve modeling with multivariate repeated measures. Finally, the randomized experimental study will be administered across six classes over three academic semesters to a total of about 6,000 students in experimental and control groups. The intervention is a writing assignment administered for credit approximately every four weeks in the semester with different foci for experimental and control groups. Data analysis will include multilevel linear regression.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: Chemistry of Life Processes | Award Amount: 145.07K | Year: 2017

With this award the Chemistry of Life Processes Program of the Chemistry Division of NSF are supporting Carl J. Carrano and Frithjof C. Kuppers study of how important trace elements (iron, boron and iodine) are taken up and stored in seaweeds, and how these elements interact with each other so as to affect the plant biology. Seaweeds represent an important resource with a wide range of uses in the food, cosmetic, and fertilizer industries. They are also attracting increasing attention as a source for biofuels that do not compete with terrestrial plants for food production. The project utilizes advanced high resolution synchrotron radiation methods to define the precise locations and chemical states of iron, iodine and boron in two important seaweeds and uses this information to assist in determining the biological importance of interactions between them. This work also provide training for graduate and undergraduate students in a wide range of important techniques. In addition, since San Diego State University has a high minority population (10th largest grantor of minority bachelors degrees in the USA), this research project is thus expected to favorably impact minority training in the sciences. Outreach activities are also in progress.

While in the cytoplasm many metalloproteins are present as a complicated mixture not easily amenable to simple analysis without prior potentially destructive, and certainly disruptive, isolation and purification. Many other metalloproteins are stored, sequestered or localized in discrete locations within the cell/organelles and are also difficult to isolate and study. As the resolution and flux density of synchrotron radiation (SR) increases, it may be possible to pinpoint such systems and analyze and characterize them directly in situ without need for isolation or purification. Thus, one goal of this research is to see what the limits are for SR methods for visualizing the chemical state, localization and potential interactions of trace elements as applied to biological samples given the increasing capabilities of 3rd generation synchrotrons where significant progress (both in terms of brilliance and resolution) has been evident. The project is using utilizing advanced high resolution (micron to submicron) SR methods, i.e. microprobe, 3D tomography, IR/Raman, XANES and EXAFS to define the precise locations and chemical states of iron, iodine and boron in two model marine macroalgae (Ectocarpus siliculosis, Macrocystis pyrifera). The investigators use this information to assist in determining the biological importance of interactions between the elements. Seaweeds represent an important resource with a wide range of uses in the food, cosmetic, and fertilizer industries. They are also attracting increasing attention as a source for biofuels that do not compete with terrestrial plants for food production.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: INFRAST MGMT & EXTREME EVENTS | Award Amount: 449.20K | Year: 2016

Effective evacuation during disastrous events is one of the most challenging issues for many local government agencies in U.S. This research project will develop a prototype integrated wildfire evacuation decision support system and create analytic tools that will be evaluated with evacuation planers and emergency resource managers. Our interdisciplinary research team will collaborate with the Office of Emergency Services (OES) of San Diego County, the San Diego/Imperial Counties Chapter of the American Red Cross, and 2-1-1 San Diego to develop this web-based system. This research will help emergency response agencies better understand public perceptions and needs during disastrous events, and create more effective evacuation plans for local communities. The research framework can be extended to other types of natural disasters (e.g., tsunami, hurricanes, flood hazards) with some modifications to cope with different needs of evacuation plans. The dynamic population density model developed in this project can be applied in urban planning, elections, business marketing, and facility management. The social perception analysis model and public opinion monitors can help other research domains such as traffic incident detection and public campaigns. One of the most valuable components in this project is the establishment of a resident feedback network by connecting registered local volunteers using a mobile phone application and an online forum. The project will also include involvement of graduate students, dissemination through various fora, including a project website and a discussion forum to involve multidisciplinary researchers. Three summer workshop meetings will be organized to facilitate future multidisciplinary collaborations among researchers and government agencies.

Using Big Data-driven techniques, this project will integrate multiple data sources including social media, census survey, geographic information systems (GIS) data layers, volunteer suggestions, and remote sensing data to develop an integrated wildfire evacuation decision support system (IWEDSS). This system will provide key functions for data collection, traffic demand modeling, evacuation operation, and information dissemination. It will offer scientifically-based and data-driven analytic tools for evacuation planers and government agencies to make better decisions that can reduce the evacuation time and potential number of injuries and deaths. The four main goals of this project are to (1) build a dynamic estimated population distribution (density) model in urban areas by integrating multiple data sources and GIS models; (2) design stage-based evacuation plans with population density distributions and develop robust optimization models to account for demand uncertainties; (3) create a public opinion monitor and a resident feedback network to improve evacuation plans by understanding social perception of the disasters in local communities through the real-time analysis of social media and volunteer suggestions; (4) build a web-based geospatial analytics platform and provide interactive decision support tools for decision makers, emergency resource managers, and public officers. This interdisciplinary project will rely upon a convergence among GIScience, cartography, civil engineering, transportation, and social media analytics to facilitate the transformation of traditional static evacuation planning procedures into a dynamic, user-centered, easy-to-use, and data-driven spatial decision support system.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: PROGRAM EVALUATION | Award Amount: 1.10M | Year: 2016

The research supported by this project will study how instructors, administrators, and education researchers take advantage of rich student and student performance data collected by the university. The data will be used in the development of a new statistical model that will identify students in need of help and the sort of help that they need. The system is built upon statistical models that are used in personalized medicine to determine the best medical interventions for an individual patient. The research will be carried out by an interdisciplinary team from statistics and data science, institutional research, instructional technology, and information technology and they will develop a learning analytics methodology to automate the tasks of data collection and processing, data visualizations and summaries, data analysis, and scientific reporting in student success efficacy studies. As part of this development, the concept of individualized treatment effects is introduced as a method to assess the effectiveness of interventions and/or instructional regimes and provide personalized feedback to students.

More specifically the research goal of the project is to develop and test new statistical methods for analyzing large sets of student data. The data sets to be analyzed and tested arise from administrative student data collected by San Diego State University. Additionally, the research will develop new methods of data cleaning for the student information system and learning management system data collected by the university to make the entire analysis procedures more efficient. The technical contribution is to utilize a new random forest of interaction trees machine learning method that enables the analysis of treatment effects for individuals and for subgroups (e.g., testing the success of a pedagogical or other intervention for both individual students and for specific subgroups of students). The results of the statistical analysis will be displayed as dashboards to report the findings for the assessment of intervention strategies in improving student retention and performance.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: SEES Hazards | Award Amount: 520.15K | Year: 2016

The area off the California coast supports productive commercial and recreational fisheries such as squid, sardine, and lobster that are important to human cultures, economies, and livelihoods. Climate change is expected to alter the oceanic system and contribute to changes in fish populations that will directly affect fishers profits and behavior, as well as managers actions in setting limits on harvest. This collaborative Coastal SEES study brings together oceanographers, fisheries scientists, economists, and social scientists to develop a better understanding of interactions among the climate and coastal ocean system, fish populations, fishers and fishing communities, and resource management. The research team includes state and federal scientists, and results will be shared with the resource management community. The project will support interdisciplinary training for undergraduates, graduate students, and postdoctoral scientists, and the investigators will participate in a number of public outreach activities.

This Coastal SEES project assembles a diverse team of oceanographers, fisheries scientists, economists, and social scientists to develop new, integrated understanding of climate effects on coastal fisheries. The investigators will examine how fishing behavior, income, jobs distribution, and livelihood viability will be altered by climate change. The focus is on three key commercially harvested species that are known to respond to environmental change: Pacific sardine, California market squid, and California spiny lobster. Each of these supports an economically important fishery and represents a different type of organism in terms of marine habitat, latitudinal range, and time scale of response. The approach will include global climate model projections, regional ocean modeling, fisheries modeling, economics modeling, plus studies of management scenarios and fisher behavior. The goal will be to help develop sustainable management strategies under future climate scenarios given predicted ecological, social, and economic outcomes for these three fisheries.

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