Hebbian analysis of the transformation of medial entorhinal grid-cell inputs to hippocampal place fields.

The discovery of grid cells in the medial entorhinal cortex (MEC) permits the characterization of hippocampal computation in much greater detail than previously possible. The present study addresses how an integrate-and-fire unit driven by grid-cell spike trains may transform the multipeaked, spatial firing pattern of grid cells into the single-peaked activity that is typical of hippocampal place cells. Previous studies have shown that in the absence of network interactions, this transformation can succeed only if the place cell receives inputs from grids with overlapping vertices at the location of the place cell's firing field. In our simulations, the selection of these inputs was accomplished by fast Hebbian plasticity alone. The resulting nonlinear process was acutely sensitive to small input variations. Simulations differing only in the exact spike timing of grid cells produced different field locations for the same place cells. Place fields became concentrated in areas that correlated with the initial trajectory of the animal; the introduction of feedback inhibitory cells reduced this bias. These results suggest distinct roles for plasticity of the perforant path synapses and for competition via feedback inhibition in the formation of place fields in a novel environment. Furthermore, they imply that variability in MEC spiking patterns or in the rat's trajectory is sufficient for generating a distinct population code in a novel environment and suggest that recalling this code in a familiar environment involves additional inputs and/or a different mode of operation of the network.

Dominance of the proximal coordinate frame in determining the locations of hippocampal place cell activity during navigation.

The place-specific activity of hippocampal cells provides downstream structures with information regarding an animal's position within an environment and, perhaps, the location of goals within that environment. In rodents, recent research has suggested that distal cues primarily set the orientation of the spatial representation, whereas the boundaries of the behavioral apparatus determine the locations of place activity. The current study was designed to address possible biases in some previous research that may have minimized the likelihood of observing place activity bound to distal cues. Hippocampal single-unit activity was recorded from six freely moving rats as they were trained to perform a tone-initiated place-preference task on an open-field platform. To investigate whether place activity was bound to the room- or platform-based coordinate frame (or both), the platform was translated within the room at an "early" and at a "late" phase of task acquisition (Shift 1 and Shift 2). At both time points, CA1 and CA3 place cells demonstrated room-associated and/or platform-associated activity, or remapped in response to the platform shift. Shift 1 revealed place activity that reflected an interaction between a dominant platform-based (proximal) coordinate frame and a weaker room-based (distal) frame because many CA1 and CA3 place fields shifted to a location intermediate to the two reference frames. Shift 2 resulted in place activity that became more strongly bound to either the platform- or room-based coordinate frame, suggesting the emergence of two independent spatial frames of reference (with many more cells participating in platform-based than in room-based representations).

The Role of Cell Sterilization in Population Based Studies of Radiogenic Second Cancers Following Radiation Therapy

Advances in radiotherapy have generated increased interest in comparative studies of treatment techniques and their effectiveness. In this respect, pediatric patients are of specific interest because of their sensitivity to radiation induced second cancers. However, due to the rarity of childhood cancers and the long latency of second cancers, large sample sizes are unavailable for the epidemiological study of contemporary radiotherapy treatments. Additionally, when specific treatments are considered, such as proton therapy, sample sizes are further reduced due to the rareness of such treatments. We propose a method to improve statistical power in micro clinical trials. Specifically, we use a more biologically relevant quantity, cancer equivalent dose (DCE), to estimate risk instead of mean absorbed dose (DMA). Our objective was to demonstrate that when DCE is used fewer subjects are needed for clinical trials. Thus, we compared the impact of DCE vs. DMA on sample size in a virtual clinical trial that estimated risk for second cancer (SC) in the thyroid following craniospinal irradiation (CSI) of pediatric patients using protons vs. photons. Dose reconstruction, risk models, and statistical analysis were used to evaluate SC risk from therapeutic and stray radiation from CSI for 18 patients. Absorbed dose was calculated in two ways: with (1) traditional DMA and (2) with DCE. DCE and DMA values were used to estimate relative risk of SC incidence (RRCE and RRMA, respectively) after proton vs. photon CSI. Ratios of RR for proton vs. photon CSI (RRRCE and RRRMA) were then used in comparative estimations of sample size to determine the minimal number of patients needed to maintain 80% statistical power when using DCE vs. DMA. For all patients, we found that protons substantially reduced the risk of developing a second thyroid cancer when compared to photon therapy. Mean RRR values were 0.052±0.014 and 0.087±0.021 for RRRMA and RRRCE, respectively. However, we did not find that use of DCE reduced the number of patents needed for acceptable statistical power (i.e, 80%). In fact, when considerations were made for RRR values that met equipoise requirements and the need for descriptive statistics, the minimum number of patients needed for a micro-clinical trial increased from 17 using DMA to 37 using DCE. Subsequent analyses revealed that for our sample, the most influential factor in determining variations in sample size was the experimental standard deviation of estimates for RRR across the patient sample. Additionally, because the relative uncertainty in dose from proton CSI was so much larger (on the order of 2000 times larger) than the other uncertainty terms, it dominated the uncertainty in RRR. Thus, we found that use of corrections for cell sterilization, in the form of DCE, may be an important and underappreciated consideration in the design of clinical trials and radio-epidemiological studies. In addition, the accurate application of cell sterilization to thyroid dose was sensitive to variations in absorbed dose, especially for proton CSI, which may stem from errors in patient positioning, range calculation, and other aspects of treatment planning and delivery.

drp, A novel cell density regulated protein

Growing cells are continuously processing signals of all varieties and responding to these signals by changes in cellular gene expression. One signal that cells in close proximity relay to each other is cell-cell contact. Non-transformed cells respond to cell-cell contact by arrest of growth and entry into G$\sb0,$ a process known as contact inhibition. Transformed cells do not respond to contact inhibition and continue to grow to high cell density, forming foci when in cell culture and tumors in the living organism. The events surrounding the generation, transduction, and response to cellular contact are poorly understood. In the present study, a novel gene product, drp, is shown to be expressed at high levels in cultured cells at high cell density. This density regulated protein, drp, has an apparent molecular weight of 70 kDa. Northern analysis shows drp to be highly expressed in cardiac and skeletal muscle and least abundant in lung and kidney tissues. By homology to two independently derived sequence tagged sites (STSs) used in the human genome project, drp or a closely related sequence maps to human chromosome 12. Density-dependent increases in drp expression have been demonstrated in six different cell lines including NIH 3T3, Hela and a human teratocarcinoma cell line, PA-1. Cells exhibit increased drp expression both when they are plated at increasing concentrations per unit area, or plated at low density and allowed to grow naturally to higher cell density. Cells at high density can exhibit several phenotypes including growth arrest, accumulation of soluble factors in the media, and increased numbers of cell contacts. Growth arrest by serum starvation or TGF-$\beta$ treatment fails to produce an increase in drp expression. Similarly, treatment of low density cells with conditioned media from high density cells fails to elicit drp expression. These results argue that neither soluble factors accumulated or expressed at high density nor simple exit from the cell cycle is sufficient to produce an increase in drp expression. The expression of drp appears to be uniquely regulated by cell density alone. ^

HETEROGENEITY IN VIRUS EXPRESSION, TUMORIGENICITY, AND IN VITRO GROWTH PROPERTIES OF DRUG-RESISTANT CLONES OF AN RIII MOUSE MAMMARY TUMOR CELL LINE

Four 8-azaguanine (AG)-resistant and 5-bromodeoxyuridine (BUdR)-resistant clones of a mouse mammary adenocarcinoma cell line, RIII 7387, were developed and analyzed for their tumorigenic properties, in vitro characteristics, and virus expression. These characteristics were analyzed for relationships of any of the cellular parameters and the ability of these lines to produce tumors in syngeneic animals.^ The results of this study demonstrated that the parental line consists of a heterogeneous population of cells. Doubling times, saturation densities, and 2-deoxy-D-glucose uptake varied between sublines. In addition, while all sublines were found to express both B-type and C-type viral antigenic markers, levels of the major B-type and C-type viral proteins varied in the subclones. The sublines also differed markedly in their response to the presence of dexamethasone, glutathione, and insulin in the tissue culture medium.^ Variations in retrovirus expression were convirmed by electron microscopy. Budding and extracellular virus particles were seen in the majority of the cell lines. Virus particles in one of the BUdR-resistant lines, BUD9, were found however, only in inclusions and vacuoles. The AG-resistant subline AGE11 was observed to be rich in intracytoplasmic A particles. The examination of these cell lines for the presence of retroviral RNA-dependent DNA polymerase (RT) activity revealed that some B-type RT activity could be found in the culture fluid of most of the cell lines but that little C-type RT activity could be found suggesting that the C-type virus particles expressed by these RIII clones contain a defective RT.^ Tumor clones also varied in their ability to form tumors in syngeneic RIII mice. Tumor incidence ranged from 50% to 100%. The majority of the tumors regressed within 30 days post infection.^ Statistical analysis indicated that while these clones varied in their characteristics, there was no correlation between the ability of these cell lines to form tumors in syngeneic mice and any of the other characteristics examined.^ These studies have confirmed and extended the growing evidence that tumors, regardless of their natural origin, consist of heterogeneous subpopulations of cells which may vary widely in their in vitro growth behavior, their antigenic expression, and their malignant properties. ^

4HPR-X radiation mechanisms of interactions in non-small cell lung cancer (NSCLC) cells in vitro

Lung cancer is the leading cause of cancer death. However, poor survival using conventional therapies fuel the search for more rational interventions. The objective of this study was to design and implement a 4HPR-radiation interaction model in NSCLC, employing a traditional clinical modality (radiation), a relatively new, therapeutically unexplored agent (4HPR) and rationally combining them based on molecular mechanistic findings pertaining to their interactions. To test the hypothesis that 4HPR sensitizes cells to radiation-induced cell death via G2+M accumulation, we designed a working model consisting of H522 adenocarcinoma cells (p53, K-ras mutated) derived from an NSCLC patient; 4HPR at concentrations up to 10 μM; and X radiation up to 6 Gy generated by a patient-dedicated Phillips RT-250 X ray unit at 250 KV, 15 mA, 1.85 Gy/min. We found that 4HPR produced time- and dose-dependent morphological changes, growth inhibition, and DNA damage-inducing enhancement of reactive oxygen species. A transient G2+M accumulation of cells maximal at 24 h of continuous 4HPR exposure was used for irradiation time scheduling. Our data demonstrated enhanced cell death (both apoptotic and necrotic) in irradiated cells pre-treated with 4HPR versus those with either stressor alone. 4HPR's effect of increased NSCLC cells' radioresponse was confirmed by clonogenic assay. To explore these practical findings from a molecular mechanistic perspective, we further investigated and showed that levels of cyclin B1 and p34cdc2 kinase—both components of the mitosis promoting factor (MPF) regulating the G2/M transition—did not change following 4HPR treatment. Likewise, cdc25C phosphatase was not altered. However, enhanced p34cdc2 phosphorylation on its Thr14Tyr15 residues—indicative of its inactivation and increased expression of MPF negative regulators chk1 and wee1 kinases—were supportive of explaining 4HPR-treated cells' accumulation. Hence, p34cdc2 phosphorylation, chk1, and wee1 warrant further evaluation as potential molecular targets for 4HPR-X radiation combination. In summary, we (1) demonstrated that 4HPR not only induces cell death by itself, but also increases NSCLC cells' subsequent radioresponse, indicative of potential clinical applicability, and (2) for the first time, shed light on deciphering 4HPR-X radiation molecular mechanisms of interaction, including the finding of 4HPR's role as a p34cdc2 inactivator via Thr14Tyr15 phosphorylation. ^

Mechanism of damage sensing and cell killing following the inhibition of nucleotide excision repair by fludarabine in quiescent cells

Inhibition of DNA repair by the nucleoside of fludarabine (F-ara-A) induces toxicity in quiescent human cells. The sensing and signaling mechanisms following DNA repair inhibition by F-ara-A are unknown. The central hypothesis of this project was that the mechanistic interaction of a DNA repair initiating agent and a nucleoside analog initiates an apoptotic signal in quiescent cells. The purpose of this research was to identify the sensing and signaling mechanism(s) that respond to DNA repair inhibition by F-ara-A. Lymphocytes were treated with F-ara-A, to accumulate the active triphosphate metabolite and subsequently DNA repair was activated by UV irradiation. Pre-incubation of lymphocytes with 3 μM F-ara-A inhibited DNA repair initiated by 2 J/m2 UV and induced greater than additive apoptosis after 24 h. Blocking the incorporation of F-ara-A nucleotide into repairing DNA using 30 μM aphidicolin considerably lowered the apoptotic response. ^ Wild-type quiescent cells showed a significant loss in viability than did cells lacking functional sensor kinase DNA-PKcs or p53 as measured by colony formation assays. The functional status of ATM did not appear to affect the apoptotic outcome. Immunoprecipitation studies showed an interaction between the catalytic sub-unit of DNA-PK and p53 following DNA repair inhibition. Confocal fluorescence microscopy studies have indicated the localization pattern of p53, DNA-PK and γ-H2AX in the nucleus following DNA damage. Foci formation by γ-H2AX was seen as an early event that is followed by interaction with DNA-PKcs. p53 serine-15 phosphorylation and accumulation were detected 2 h after treatment. Fas/Fas ligand expression increased significantly after repair inhibition and was dependent on the functional status of p53. Blocking the interaction between Fas and Fas ligand by neutralizing antibodies significantly rescued the apoptotic fraction of cells. ^ Collectively, these results suggest that incorporation of the nucleoside analog into repair patches is critical for cytotoxicity and that the DNA damage, while being sensed by DNA-PK, may induce apoptosis by a p53-mediated signaling mechanism. Based on the results, a model is proposed for the sensing of F-ara-A-induced DNA damage that includes γ-H2AX, DNA-PKcs, and p53. Targeting the cellular DNA repair mechanism can be a potential means of producing cytotoxicity in a quiescent population of neoplastic cells. These results also provide mechanistic support for the success of nucleoside analogs with cyclophosphamide or other agents that initiate excision repair processes, in the clinic. ^

Modeling the survival of T-cell lymphocytes using compound branching processes

Radiotherapy has been a method of choice in cancer treatment for a number of years. Mathematical modeling is an important tool in studying the survival behavior of any cell as well as its radiosensitivity. One particular cell under investigation is the normal T-cell, the radiosensitivity of which may be indicative to the patient's tolerance to radiation doses.^ The model derived is a compound branching process with a random initial population of T-cells that is assumed to have compound distribution. T-cells in any generation are assumed to double or die at random lengths of time. This population is assumed to undergo a random number of generations within a period of time. The model is then used to obtain an estimate for the survival probability of T-cells for the data under investigation. This estimate is derived iteratively by applying the likelihood principle. Further assessment of the validity of the model is performed by simulating a number of subjects under this model.^ This study shows that there is a great deal of variation in T-cells survival from one individual to another. These variations can be observed under normal conditions as well as under radiotherapy. The findings are in agreement with a recent study and show that genetic diversity plays a role in determining the survival of T-cells. ^

Genetic Predictors of Clinical Outcomes in Non-Small Cell Lung Cancer Patients

Lung cancer is the leading cause of cancer-related mortality in the US. Emerging evidence has shown that host genetic factors can interact with environmental exposures to influence patient susceptibility to the diseases as well as clinical outcomes, such as survival and recurrence. We aimed to identify genetic prognostic markers for non-small cell lung cancer (NSCLC), a major (85%) subtype of lung cancer, and also in other subgroups. With the fast evolution of genotyping technology, genetic association studies have went through candidate gene approach, to pathway-based approach, to the genome wide association study (GWAS). Even in the era of GWAS, pathway-based approach has its own advantages on studying cancer clinical outcomes: it is cost-effective, requiring a smaller sample size than GWAS easier to identify a validation population and explore gene-gene interactions. In the current study, we adopted pathway-based approach focusing on two critical pathways - miRNA and inflammation pathways. MicroRNAs (miRNA) post-transcriptionally regulate around 30% of human genes. Polymorphisms within miRNA processing pathways and binding sites may influence patients’ prognosis through altered gene regulation. Inflammation plays an important role in cancer initiation and progression, and also has shown to impact patients’ clinical outcomes. We first evaluated 240 single nucleotide polymorphisms (SNPs) in miRNA biogenesis genes and predicted binding sites in NSCLC patients to determine associations with clinical outcomes in early-stage (stage I and II) and late-stage (stage III and IV) lung cancer patients, respectively. First, in 535 early-stage patients, after correcting multiple comparisons, FZD4:rs713065 (hazard ratio [HR]:0.46, 95% confidence interval [CI]:0.32-0.65) showed a significant inverse association with survival in early stage surgery-only patients. SP1:rs17695156 (HR:2.22, 95% CI:1.44-3.41) and DROSHA:rs6886834 (HR:6.38, 95% CI:2.49-16.31) conferred increased risk of progression in the all patients and surgery-only populations, respectively. FAS:rs2234978 was significantly associated with improved survival in all patients (HR:0.59, 95% CI:0.44-0.77) and in the surgery plus chemotherapy populations (HR:0.19, 95% CI:0.07-0.46).. Functional genomics analysis demonstrated that this variant creates a miR-651 binding site resulting in altered miRNA regulation of FAS, providing biological plausibility for the observed association. We then analyzed these associations in 598 late-stage patients. After multiple comparison corrections, no SNPs remained significant in the late stage group, while the top SNP NAT1:rs15561 (HR=1.98, 96%CI=1.32-2.94) conferred a significantly increased risk of death in the chemotherapy subgroup. To test the hypothesis that genetic variants in the inflammation-related pathways may be associated with survival in NSCLC patients, we first conducted a three-stage study. In the discovery phase, we investigated a comprehensive panel of 11,930 inflammation-related SNPs in three independent lung cancer populations. A missense SNP (rs2071554) in HLA-DOB was significantly associated with poor survival in the discovery population (HR: 1.46, 95% CI: 1.02-2.09), internal validation population (HR: 1.51, 95% CI: 1.02-2.25), and external validation (HR: 1.52, 95% CI: 1.01-2.29) population. Rs2900420 in KLRK1 was significantly associated with a reduced risk for death in the discovery (HR: 0.76, 95% CI: 0.60-0.96) and internal validation (HR: 0.77, 95% CI: 0.61-0.99) populations, and the association reached borderline significance in the external validation population (HR: 0.80, 95% CI: 0.63-1.02). We also evaluated these inflammation-related SNPs in NSCLC patients in never smokers. Lung cancer in never smokers has been increasingly recognized as distinct disease from that in ever-smokers. A two-stage study was performed using a discovery population from MD Anderson (411 patients) and a validation population from Mayo Clinic (311 patients). Three SNPs (IL17RA:rs879576, BMP8A:rs698141, and STK:rs290229) that were significantly associated with survival were validated (pCD74:rs1056400 and CD38:rs10805347) were borderline significant (p=0.08) in the Mayo Clinic population. In the combined analysis, IL17RA:rs879576 resulted in a 40% reduction in the risk for death (p=4.1 × 10-5 [p=0.61, heterogeneity test]). We also validated a survival tree created in MD Anderson population in the Mayo Clinic population. In conclusion, our results provided strong evidence that genetic variations in specific pathways that examined (miRNA and inflammation pathways) influenced clinical outcomes in NSCLC patients, and with further functional studies, the novel loci have potential to be translated into clinical use.

αvβ8 Integrin Interacts with RhoGDI1 to Regulate Rac1 and Cdc42 Activation and Drive Glioblastoma Cell Invasion

As an interface between the circulatory and central nervous systems, the neurovascular unit is vital to the development and survival of tumors. The malignant brain cancer glioblastoma multiforme (GBM) displays invasive growth behaviors that are major impediments to surgical resection and targeted therapies. Adhesion and signaling pathways that drive GBM cell invasion remain largely uncharacterized. Here we have utilized human GBM cell lines, primary patient samples, and pre-clinical mouse models to demonstrate that integrin αvβ8 is a major driver of GBM cell invasion. β8 integrin is overexpressed in many human GBM cells, with higher integrin expression correlating with increased invasion and diminished patient survival. Silencing β8 integrin in human GBM cells leads to impaired tumor cell invasion due to hyperactivation of the Rho GTPases Rac1 and Cdc42. β8 integrin associates with Rho GDP Dissociation Inhibitor 1 (RhoGDI1), an intracellular signaling effector that sequesters Rho GTPases in their inactive GDP-bound states. Silencing RhoGDI1 expression or uncoupling αvβ8 integrin-RhoGDI1 protein interactions blocks GBM cell invasion due to Rho GTPase hyperactivation. These data reveal for the first time that αvβ8 integrin, via interactions with RhoGDI1, suppresses activation of Rho proteins to promote GBM cell invasiveness. Hence, targeting the αvβ8 integrin-RhoGDI1 signaling axis may be an effective strategy for blocking GBM cell invasion.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.