Rho GTPase Dynamics in the Regulation of Cellular Signaling and Migration

Cell migration is critical to the development and maintenance of higher organisms, and is required for the patterning of the nervous system, for the development of organs, and for responses to wounds or sites of inflammation. Because cell migration is so widely utilized, it must be very tightly controlled, as is apparent when the process goes awry, such as in cancer cell metastasis or chronic inflammation. Growth factors and other cues mediate the activation of a variety of pathways that induce cell migration. Despite these many pathways, a family of proteins called Rho GTPases are universally engaged to cause changes in the cellular cytoskeleton, leading to cell migration. Because Rho GTPases are so critical to cell migration, yet can be used to mediate many different types of cellular responses, they must be precisely controlled. In most cases, it is the timing and placement of Rho GTPase activity that defines cellular behaviors in response to specific signals. However, tools to investigate the spatiotemporal dynamics of Rho GTPase activity in live cells have only recently been developed. I this work, I characterize the spatial and temporal dynamics of the Rho GTPases in cell migration through the development of sensors for Rho GTPase activity for live cell imaging. I establish the spatial and temporal dynamics of RhoA, Rac1, and Cdc42 at the leading edge of migrating cells, and the role of RhoG in its ability to precisely position and activate Rac1 at the leading edge of cells. This work provides the first thorough characterization of the roles of these GTPases at the leading edge relative to one another and the mechanisms by which they are regulated. This work also demonstrates preliminary studies on the roles of these GTPases during leukocyte transendothelial migration. It is critical to gain an understanding of the mechanisms by which cells control cell migration via the Rho GTPases. Aberrant signaling through the GTPases leads to a variety of disease processes. Thus, a better understanding of normal Rho GTPase signaling will provide a framework for understanding how cell migration goes awry and how it can be potentially treated.Doctor of Philosoph

A competitive and reversible deactivation approach to catalysis-based quantitative assays

Catalysis-based signal amplification makes optical assays highly sensitive and widely useful in chemical and biochemical research. However, assays must be fine-tuned to avoid signal saturation, substrate depletion and nonlinear performance. Furthermore, once stopped, such assays cannot be restarted, limiting the dynamic range to two orders of magnitude with respect to analyte concentrations. In addition, abundant analytes are difficult to quantify under catalytic conditions due to rapid signal saturation. Herein, we report an approach in which a catalytic reaction competes with a concomitant inactivation of the catalyst or consumption of a reagent required for signal generation. As such, signal generation proceeds for a limited time, then autonomously and reversibly stalls. In two catalysis-based assays, we demonstrate restarting autonomously stalled reactions, enabling accurate measurement over five orders of magnitude, including analyte levels above substrate concentration. This indicates that the dynamic range of catalysis-based assays can be significantly broadened through competitive and reversible deactivation

Digital Bridges Across Disciplinary, Practical and Pedagogical Divides: An Online Professional Master’s Program in Heritage Resource Management

Growth and diversification in heritage resource management (HRM) archaeology since the 1960s have created new demands for training the next generations of HRM leaders and for addressing persistent and counterproductive divisions between academic and applied archaeologies. The Simon Fraser University Department of Archaeology (SFU) has responded to these demands with an all-new, cohort-based, thesis-focused graduate program created by and for HRM professionals. The program’s target audience is HRM practitioners who hold Bachelor’s credentials, have initiated promising careers in HRM, and desire advanced, research-focused degrees to enable their professional capacity and upward mobility. The SFU program is structured and focused to provide intensive, predominantly online training in the four essential dimensions of HRM: law and policy, ethics and practice, business management, and research design and methods. The program has been successful through initial cohort cycles and in attracting HRM industry interest in collaboration. Industry-academic partnerships in cognate disciplines have proved effective in comparable circumstances but remain underdeveloped as bases for planning and delivering state-of-the-art training in applied archaeology and the broader field of HRM. Critical next steps in program development entail the identification of attributes of HRM futures desired by all or most HRM stakeholders and the collaborative pursuit of those desired futures

Mass Activated Droplet Sorting (MADS) Enables Highâ Throughput Screening of Enzymatic Reactions at Nanoliter Scale

Microfluidic droplet sorting enables the highâ throughput screening and selection of waterâ inâ oil microreactors at speeds and volumes unparalleled by traditional wellâ plate approaches. Most such systems sort using fluorescent reporters on modified substrates or reactions that are rarely industrially relevant. We describe a microfluidic system for highâ throughput sorting of nanoliter droplets based on direct detection using electrospray ionization mass spectrometry (ESIâ MS). Droplets are split, one portion is analyzed by ESIâ MS, and the second portion is sorted based on the MS result. Throughput of 0.7â samplesâ sâ 1 is achieved with 98â % accuracy using a selfâ correcting and adaptive sorting algorithm. We use the system to screen â 15â 000â samples in 6â h and demonstrate its utility by sorting 25â nL droplets containing transaminase expressed in vitro. Labelâ free ESIâ MS droplet screening expands the toolbox for droplet detection and recovery, improving the applicability of droplet sorting to protein engineering, drug discovery, and diagnostic workflows.A microfluidic system for sorting nanoliter droplets based on mass spectrometry is presented. Fully automated, labelâ free sorting at 0.7â samplesâ sâ 1 is achieved with 98â % accuracy. In vitro transcription and translation (ivTT) of a transaminase enzyme in ca.â 25â nL samples is demonstrated and samples are sorted on the basis of enzyme activity.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154315/1/anie201913203.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154315/2/anie201913203-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154315/3/anie201913203_am.pd

Lightcurves of Type Ia Supernovae from Near the Time of Explosion

We present a set of 11 type Ia supernova (SN Ia) lightcurves with dense, pre-maximum sampling. These supernovae (SNe), in galaxies behind the Large Magellanic Cloud (LMC), were discovered by the SuperMACHO survey. The SNe span a redshift range of z = 0.11 - 0.35. Our lightcurves contain some of the earliest pre-maximum observations of SNe Ia to date. We also give a functional model that describes the SN Ia lightcurve shape (in our VR-band). Our function uses the "expanding fireball" model of Goldhaber et al. (1998) to describe the rising lightcurve immediately after explosion but constrains it to smoothly join the remainder of the lightcurve. We fit this model to a composite observed VR-band lightcurve of three SNe between redshifts of 0.135 to 0.165. These SNe have not been K-corrected or adjusted to account for reddening. In this redshift range, the observed VR-band most closely matches the rest frame V-band. Using the best fit to our functional description of the lightcurve, we find the time between explosion and observed VR-band maximum to be 17.6+-1.3(stat)+-0.07(sys) rest-frame days for a SN Ia with a VR-band Delta m_{-10} of 0.52mag. For the redshifts sampled, the observed VR-band time-of-maximum brightness should be the same as the rest-frame V-band maximum to within 1.1 rest-frame days.Comment: 35 pages, 18 figures, 15 tables; Higher quality PDF available at http://ctiokw.ctio.noao.edu/~sm/sm/SNrise/index.html; AJ accepte

Mass Activated Droplet Sorting (MADS) Enables Highâ Throughput Screening of Enzymatic Reactions at Nanoliter Scale

Microfluidic droplet sorting enables the highâ throughput screening and selection of waterâ inâ oil microreactors at speeds and volumes unparalleled by traditional wellâ plate approaches. Most such systems sort using fluorescent reporters on modified substrates or reactions that are rarely industrially relevant. We describe a microfluidic system for highâ throughput sorting of nanoliter droplets based on direct detection using electrospray ionization mass spectrometry (ESIâ MS). Droplets are split, one portion is analyzed by ESIâ MS, and the second portion is sorted based on the MS result. Throughput of 0.7â samplesâ sâ 1 is achieved with 98â % accuracy using a selfâ correcting and adaptive sorting algorithm. We use the system to screen â 15â 000â samples in 6â h and demonstrate its utility by sorting 25â nL droplets containing transaminase expressed in vitro. Labelâ free ESIâ MS droplet screening expands the toolbox for droplet detection and recovery, improving the applicability of droplet sorting to protein engineering, drug discovery, and diagnostic workflows.Ein Mikrofluidiksystem zur Sortierung von Nanolitertröpfchen basierend auf Massenspektrometrie erreicht eine vollautomatische markierungsfreie Sortierung bei 0.7 Probenâ sâ 1 mit 98â % Genauigkeit. Die Inâ vitroâ Transkription und â Translation (ivTT) eines Transaminaseâ Enzyms in Proben von etwa 25â nL wird demonstriert, und die Proben werden nach ihrer Enzymaktivität sortiert.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154446/1/ange201913203-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154446/2/ange201913203.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154446/3/ange201913203_am.pd

Integrating Dynamic Subsurface Habitat Metrics Into Species Distribution Models

Species distribution models (SDMs) have become key tools for describing and predicting species habitats. In the marine domain, environmental data used in modeling species distributions are often remotely sensed, and as such have limited capacity for interpreting the vertical structure of the water column, or are sampled in situ, offering minimal spatial and temporal coverage. Advances in ocean models have improved our capacity to explore subsurface ocean features, yet there has been limited integration of such features in SDMs. Using output from a data-assimilative configuration of the Regional Ocean Modeling System, we examine the effect of including dynamic subsurface variables in SDMs to describe the habitats of four pelagic predators in the California Current System (swordfish Xiphias gladius, blue sharks Prionace glauca, common thresher sharks Alopias vulpinus, and shortfin mako sharks lsurus oxyrinchus). Species data were obtained from the California Drift Gillnet observer program (1997-2017). We used boosted regression trees to explore the incremental improvement enabled by dynamic subsurface variables that quantify the structure and stability of the water column: isothermal layer depth and bulk buoyancy frequency. The inclusion of these dynamic subsurface variables significantly improved model explanatory power for most species. Model predictive performance also significantly improved, but only for species that had strong affiliations with dynamic variables (swordfish and shortfin mako sharks) rather than static variables (blue sharks and common thresher sharks). Geospatial predictions for all species showed the integration of isothermal layer depth and bulk buoyancy frequency contributed value at the mesoscale level (\u3c 100 km) and varied spatially throughout the study domain. These results highlight the utility of including dynamic subsurface variables in SDM development and support the continuing ecological use of biophysical output from ocean circulation models

Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks

The amounts of muscle and fat in a person's body, known as body composition, are correlated with cancer risks, cancer survival, and cardiovascular risk. The current gold standard for measuring body composition requires time-consuming manual segmentation of CT images by an expert reader. In this work, we describe a two-step process to fully automate the analysis of CT body composition using a DenseNet to select the CT slice and U-Net to perform segmentation. We train and test our methods on independent cohorts. Our results show Dice scores (0.95-0.98) and correlation coefficients (R=0.99) that are favorable compared to human readers. These results suggest that fully automated body composition analysis is feasible, which could enable both clinical use and large-scale population studies

Bridging Alone: Religious Conservatism, Marital Homogamy, and Voluntary Association Membership

This study characterizes social insularity of religiously conservative American married couples by examining patterns of voluntary associationmembership. Constructing a dataset of 3938 marital dyads from the second wave of the National Survey of Families and Households, the author investigates whether conservative religious homogamy encourages membership in religious voluntary groups and discourages membership in secular voluntary groups. Results indicate that couples’ shared affiliation with conservative denominations, paired with beliefs in biblical authority and inerrancy, increases the likelihood of religious group membership for husbands and wives and reduces the likelihood of secular group membership for wives, but not for husbands. The social insularity of conservative religious groups appears to be reinforced by homogamy—particularly by wives who share faith with husbands

SciClone: Inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution

The sensitivity of massively-parallel sequencing has confirmed that most cancers are oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine resolution view of this clonal architecture provides insight into tumor heterogeneity, evolution, and treatment response, all of which may have clinical implications. Single tumor analysis already contributes to understanding these phenomena. However, cryptic subclones are frequently revealed by additional patient samples (e.g., collected at relapse or following treatment), indicating that accurately characterizing a tumor requires analyzing multiple samples from the same patient. To address this need, we present SciClone, a computational method that identifies the number and genetic composition of subclones by analyzing the variant allele frequencies of somatic mutations. We use it to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample. By doing so, we can track tumor evolution and identify the spatial origins of cells resisting therapy