ASU Scholarship Showcase

Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of the mice varied from 12 mm³ to 62 mm³, even though mice were inoculated from the same tumor cell line under carefully controlled conditions. We generated hypotheses to explore large variances in final tumor size and tested them with our simple reaction-diffusion model in both a 3-dimensional (3D) finite difference method and a 2-dimensional (2D) level set method. The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail. These differences may reflect morphological and biological variability in tumor growth, as well as errors in the mathematical model, perhaps from an oversimplification of the tumor dynamics or nonidentifiability of parameters. Our results generate parameters that match other experimental in vitro and in vivo measurements. Additionally, we calculate wave speed, which matches with other rat and human measurements.

Background:
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.

Results:
We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.

Conclusions:
The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.

A relatively unexplored issue in cybersecurity science and engineering is whether there exist intrinsic patterns of cyberattacks. Conventional wisdom favors absence of such patterns due to the overwhelming complexity of the modern cyberspace. Surprisingly, through a detailed analysis of an extensive data set that records the time-dependent frequencies of attacks over a relatively wide range of consecutive IP addresses, we successfully uncover intrinsic spatiotemporal patterns underlying cyberattacks, where the term “spatio” refers to the IP address space. In particular, we focus on analyzing macroscopic properties of the attack traffic flows and identify two main patterns with distinct spatiotemporal characteristics: deterministic and stochastic. Strikingly, there are very few sets of major attackers committing almost all the attacks, since their attack “fingerprints” and target selection scheme can be unequivocally identified according to the very limited number of unique spatiotemporal characteristics, each of which only exists on a consecutive IP region and differs significantly from the others. We utilize a number of quantitative measures, including the flux-fluctuation law, the Markov state transition probability matrix, and predictability measures, to characterize the attack patterns in a comprehensive manner. A general finding is that the attack patterns possess high degrees of predictability, potentially paving the way to anticipating and, consequently, mitigating or even preventing large-scale cyberattacks using macroscopic approaches.

Supply-demand processes take place on a large variety of real-world networked systems ranging from power grids and the internet to social networking and urban systems. In a modern infrastructure, supply-demand systems are constantly expanding, leading to constant increase in load requirement for resources and consequently, to problems such as low efficiency, resource scarcity, and partial system failures. Under certain conditions global catastrophe on the scale of the whole system can occur through the dynamical process of cascading failures. We investigate optimization and resilience of time-varying supply-demand systems by constructing network models of such systems, where resources are transported from the supplier sites to users through various links. Here by optimization we mean minimization of the maximum load on links, and system resilience can be characterized using the cascading failure size of users who fail to connect with suppliers.

We consider two representative classes of supply schemes: load driven supply and fix fraction supply. Our findings are: (1) optimized systems are more robust since relatively smaller cascading failures occur when triggered by external perturbation to the links; (2) a large fraction of links can be free of load if resources are directed to transport through the shortest paths; (3) redundant links in the performance of the system can help to reroute the traffic but may undesirably transmit and enlarge the failure size of the system; (4) the patterns of cascading failures depend strongly upon the capacity of links; (5) the specific location of the trigger determines the specific route of cascading failure, but has little effect on the final cascading size; (6) system expansion typically reduces the efficiency; and (7) when the locations of the suppliers are optimized over a long expanding period, fewer suppliers are required. These results hold for heterogeneous networks in general, providing insights into designing optimal and resilient complex supply-demand systems that expand constantly in time.

Most previous works on complete synchronization of chaotic oscillators focused on the one-channel interaction scheme where the oscillators are coupled through only one variable or a symmetric set of variables. Using the standard framework of master-stability function (MSF), we investigate the emergence of complex synchronization behaviors under all possible configurations of two-channel coupling, which include, for example, all possible cross coupling schemes among the dynamical variables. Utilizing the classic Rössler and Lorenz oscillators, we find a rich variety of synchronization phenomena not present in any previously extensively studied, single-channel coupling configurations. For example, in many cases two coupling channels can enhance or even generate synchronization where there is only weak or no synchronization under only one coupling channel, which has been verified in a coupled neuron system. There are also cases where the oscillators are originally synchronized under one coupling channel, but an additional synchronizable coupling channel can, however, destroy synchronization. Direct numerical simulations of actual synchronization dynamics verify the MSF-based predictions. Our extensive computation and heuristic analysis provide an atlas for synchronization of chaotic oscillators coupled through two channels, which can be used as a systematic reference to facilitate further research in this area.

Predicting the timing of a castrate resistant prostate cancer is critical to lowering medical costs and improving the quality of life of advanced prostate cancer patients. We formulate, compare and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). We accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). While these models are simplifications of a previously published model, they fit data with similar accuracy and improve forecasting results. Both models describe the progression of androgen resistance. Although Model 1 is simpler than the more realistic Model 2, it can fit clinical data to a greater precision. However, we found that Model 2 can forecast future PSA levels more accurately. These findings suggest that including more realistic mechanisms of androgen dynamics in a two population model may help androgen resistance timing prediction.

The Quadrangles Av-11 and Av-12 on Vesta are located at the northern rim of the giant Rheasilvia south polar impact basin. The primary geologic units in Av-11 and Av-12 include material from the Rheasilvia impact basin formation, smooth material and different types of impact crater structures (such as bimodal craters, dark and bright crater ray material and dark ejecta material). Av-11 and Av-12 exhibit almost the full range of mass wasting features observed on Vesta, such as slump blocks, spur-and-gully morphologies and landslides within craters. Processes of collapse, slope instability and seismically triggered events force material to slump down crater walls or scarps and produce landslides or rotational slump blocks. The spur-and-gully morphology that is known to form on Mars is also observed on Vesta; however, on Vesta this morphology formed under dry conditions.

A variety of geologic landforms and features are observed within quadrangle Av-13 Tuccia in the southern hemisphere of Vesta. The quadrangle covers parts of the highland Vestalia Terra as well as the floors of the large Rheasilvia and Veneneia impact basins, which results in a substantial elevation difference of more than 40 km between the northern and the southern portions of the quadrangle. Measurements of crater size–frequency distributions within and surrounding the Rheasilvia basin indicate that gravity-driven mass wasting in the interior of the basin has been important, and that the basin has a more ancient formation age than would be expected from the crater density on the basin floor alone. Subsequent to its formation, Rheasilvia was superimposed by several mid-sized impact craters. The most prominent craters are Tuccia, Eusebia, Vibidia, Galeria, and Antonia, whose geology and formation ages are investigated in detail in this work. These impact structures provide a variety of morphologies indicating different sorts of subsequent impact-related or gravity-driven mass wasting processes. Understanding the geologic history of the relatively young craters in the Rheasilvia basin is important in order to understand the even more degraded craters in other regions of Vesta.

In this paper we present a time-stratigraphic scheme and geologic time scale for the protoplanet Vesta, based on global geologic mapping and other analyses of NASA Dawn spacecraft data, complemented by insights gained from laboratory studies of howardite–eucrite–diogenite (HED) meteorites and geophysical modeling. On the basis of prominent impact structures and their associated deposits, we propose a time scale for Vesta that consists of four geologic time periods: Pre-Veneneian, Veneneian, Rheasilvian, and Marcian. The Pre-Veneneian Period covers the time from the formation of Vesta up to the Veneneia impact event, from 4.6 Ga to >2.1 Ga (using the asteroid flux-derived chronology system) or from 4.6 Ga to 3.7 Ga (under the lunar-derived chronology system). The Veneneian Period covers the time span between the Veneneia and Rheasilvia impact events, from >2.1 to 1 Ga (asteroid flux-derived chronology) or from 3.7 to 3.5 Ga (lunar-derived chronology), respectively. The Rheasilvian Period covers the time span between the Rheasilvia and Marcia impact events, and the Marcian Period covers the time between the Marcia impact event until the present. The age of the Marcia impact is still uncertain, but our current best estimates from crater counts of the ejecta blanket suggest an age between ∼120 and 390 Ma, depending upon choice of chronology system used. Regardless, the Marcia impact represents the youngest major geologic event on Vesta. Our proposed four-period geologic time scale for Vesta is, to a first order, comparable to those developed for other airless terrestrial bodies.

We report on a preliminary global geologic map of Vesta, based on data from the Dawn spacecraft’s High-Altitude Mapping Orbit (HAMO) and informed by Low-Altitude Mapping Orbit (LAMO) data. This map is part of an iterative mapping effort; the geologic map has been refined with each improvement in resolution. Vesta has a heavily-cratered surface, with large craters evident in numerous locations. The south pole is dominated by an impact structure identified before Dawn’s arrival. Two large impact structures have been resolved: the younger, larger Rheasilvia structure, and the older, more degraded Veneneia structure. The surface is also characterized by a system of deep, globe-girdling equatorial troughs and ridges, as well as an older system of troughs and ridges to the north. Troughs and ridges are also evident cutting across, and spiraling arcuately from, the Rheasilvia central mound.

However, no volcanic features have been unequivocally identified. Vesta can be divided very broadly into three terrains: heavily-cratered terrain; ridge-and-trough terrain (equatorial and northern); and terrain associated with the Rheasilvia crater. Localized features include bright and dark material and ejecta (some defined specifically by color); lobate deposits; and mass-wasting materials. No obvious volcanic features are evident. Stratigraphy of Vesta’s geologic units suggests a history in which formation of a primary crust was followed by the formation of impact craters, including Veneneia and the associated Saturnalia Fossae unit. Formation of Rheasilvia followed, along with associated structural deformation that shaped the Divalia Fossae ridge-and-trough unit at the equator. Subsequent impacts and mass wasting events subdued impact craters, rims and portions of ridge-and-trough sets, and formed slumps and landslides, especially within crater floors and along crater rims and scarps. Subsequent to the formation of Rheasilvia, discontinuous low-albedo deposits formed or were emplaced; these lie stratigraphically above the equatorial ridges that likely were formed by Rheasilvia. The last features to be formed were craters with bright rays and other surface mantling deposits.

Executed progressively throughout data acquisition, the iterative mapping process provided the team with geologic proto-units in a timely manner. However, interpretation of the resulting map was hampered by the necessity to provide the team with a standard nomenclature and symbology early in the process. With regard to mapping and interpreting units, the mapping process was hindered by the lack of calibrated mineralogic information. Topography and shadow played an important role in discriminating features and terrains, especially in the early stages of data acquisition.