Evaluating Spillover Effects in Network-Based Studies In the Presence of Missing Outcomes

Estimating causal effects in the presence of spillover among individuals embedded within a social network is often challenging with missing information. The spillover effect is the effect of an intervention if a participant is not exposed to the intervention themselves but is connected to intervention recipients in the network. In network-based studies, outcomes may be missing due to the administrative end of a study or participants being lost to follow-up due to study dropout, also known as censoring. We propose an inverse probability censoring weighted (IPCW) estimator, which is an extension of an IPW estimator for network-based observational studies to settings where the outcome is subject to possible censoring. We demonstrated that the proposed estimator was consistent and asymptotically normal. We also derived a closed-form estimator of the asymptotic variance estimator. We used the IPCW estimator to quantify the spillover effects in a network-based study of a nonrandomized intervention with censoring of the outcome. A simulation study was conducted to evaluate the finite-sample performance of the IPCW estimators. The simulation study demonstrated that the estimator performed well in finite samples when the sample size and number of connected subnetworks (components) were fairly large. We then employed the method to evaluate the spillover effects of community alerts on self-reported HIV risk behavior among people who inject drugs and their contacts in the Transmission Reduction Intervention Project (TRIP), 2013 to 2015, Athens, Greece. Community alerts were protective not only for the person who received the alert from the study but also among others in the network likely through information shared between participants. In this study, we found that the risk of HIV behavior was reduced by increasing the proportion of a participant's immediate contacts exposed to community alerts

Altered Motoneuron Properties Contribute to Motor Deficits in a Rabbit Hypoxia-Ischemia Model of Cerebral Palsy

Cerebral palsy (CP) is caused by a variety of factors attributed to early brain damage, resulting in permanently impaired motor control, marked by weakness and muscle stiffness. To find out if altered physiology of spinal motoneurons (MNs) could contribute to movement deficits, we performed whole-cell patch-clamp in neonatal rabbit spinal cord slices after developmental injury at 79% gestation. After preterm hypoxia-ischemia (HI), rabbits are born with motor deficits consistent with a spastic phenotype including hypertonia and hyperreflexia. There is a range in severity, thus kits are classified as severely affected, mildly affected, or unaffected based on modified Ashworth scores and other behavioral tests. At postnatal day (P)0–5, we recorded electrophysiological parameters of 40 MNs in transverse spinal cord slices using whole-cell patch-clamp. We found significant differences between groups (severe, mild, unaffected and sham control MNs). Severe HI MNs showed more sustained firing patterns, depolarized resting membrane potential, and fired action potentials at a higher frequency. These properties could contribute to muscle stiffness, a hallmark of spastic CP. Interestingly altered persistent inward currents (PICs) and morphology in severe HI MNs would dampen excitability (depolarized PIC onset and increased dendritic length). In summary, changes we observed in spinal MN physiology likely contribute to the severity of the phenotype, and therapeutic strategies for CP could target the excitability of spinal MNs

Enhancement of Radiation Effect on Cancer Cells by Gold-pHLIP

Previous research has shown that gold nanoparticles can increase the effectiveness of radiation on cancer cells. Improved radiation effectiveness would allow lower radiation doses given to patients, reducing adverse effects; alternatively, it would provide more cancer killing at current radiation doses. Damage from radiation and gold nanoparticles depends in part on the Auger effect, which is very localized; thus, it is important to place the gold nanoparticles on or in the cancer cells. In this work, we use the pH-sensitive, tumor-targeting agent, pH Low-Insertion Peptide (pHLIP), to tether 1.4-nm gold nanoparticles to cancer cells. We find that the conjugation of pHLIP to gold nanoparticles increases gold uptake in cells compared with gold nanoparticles without pHLIP, with the nanoparticles distributed mostly on the cellular membranes. We further find that gold nanoparticles conjugated to pHLIP produce a statistically significant decrease in cell survival with radiation compared with cells without gold nanoparticles and cells with gold alone. In the context of our previous findings demonstrating efficient pHLIP-mediated delivery of gold nanoparticles to tumors, the obtained results serve as a foundation for further preclinical evaluation of dose enhancement

Comparative Study of Tumor Targeting and Biodistribution of pH (Low) Insertion Peptides (pHLIP® Peptides) Conjugated with Different Fluorescent Dyes

Purpose Acidification of extracellular space promotes tumor development, progression, and invasiveness. pH (low) insertion peptides (pHLIP® peptides) belong to the class of pH-sensitive membrane peptides, which target acidic tumors and deliver imaging and/or therapeutic agents to cancer cells within tumors. Procedures Ex vivo fluorescent imaging of tissue and organs collected at various time points after administration of different pHLIP® variants conjugated with fluorescent dyes of various polarity was performed. Methods of multivariate statistical analyses were employed to establish classification between fluorescently labeled pHLIP® variants in multidimensional space of spectral parameters. Results The fluorescently labeled pHLIP® variants were classified based on their biodistribution profile and ability of targeting of primary tumors. Also, submillimeter-sized metastatic lesions in lungs were identified by ex vivo imaging after intravenous administration of fluorescent pHLIP® peptide. Conclusions Different cargo molecules conjugated with pHLIP® peptides can alter biodistribution and tumor targeting. The obtained knowledge is essential for the design of novel pHLIP®-based diagnostic and therapeutic agents targeting primary tumors and metastatic lesions

Statistical Problems in Wireless Sensor Networks.

Wireless sensor networks (WSN) are a new technology with many applications, including environmental monitoring, surveillance, and health care. The dissertation concentrates on two critical aspects of a WSN: network design and information fusion. Our design strategy minimizes the overall network cost, explicitly incorporates sensor capabilities, and maintains coverage and connectivity constraints necessary for successful network operation. A new algorithm for local correction of sensor decisions, Local Vote Decision Fusion, is developed for the problems of target detection, localization, and tracking, and extended to multiple targets. The methodology is tested in simulations and on two case studies - an experiment involving tracking people and a project of tracking zebras. The local correction algorithm is further developed into a general framework for performance improvement for spatially correlated classifiers.Ph.D.StatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/63701/1/nkatenka_1.pd

Inference of Partial Canonical Correlation Networks with Application to Stock Market Portfolio Selection

In recent years, association networks and their applications have received increasing interest. The relationships in a network should ideally be ascertained without any preconceptions about the existence of a connection a priori. This would allow interpretations to be based on the underlying structure rather than on assumptions. Furthermore, a method that discounts outside influence on the relationships is desirable. Partial correlation is one method that meets these criteria, however, this approach is limited to a single attribute. We propose that examining the multi-Attribute partial canonical correlations is a superior strategy for capturing the complex relationships found in real world data. As a motivating application, we choose the problem of stock market portfolio selection. Diversification is a core principle of any sound investment strategy, the purpose being to minimize risk and maximize returns. To create a diverse portfolio, the interrelations between corporations and the industrial sectors that they comprise must be understood. To model these relationships we induce a partial canonical correlation network (PCCN) using recent market data and select portfolios algorithmically based on finding the least dependent but related companies. We compare the risk of portfolios selected from the PCCN, partial correlation networks, and randomly. We find that the PCCN based-Approach results in comparatively less risky portfolios

Quantitative methods in pharmaceutical research and development: concepts and applications

This contributed volume presents an overview of concepts, methods, and applications used in several quantitative areas of drug research, development, and marketing. Chapters bring together the theories and applications of various disciplines, allowing readers to learn more about quantitative fields, and to better recognize the differences between them. Because it provides a thorough overview, this will serve as a self-contained resource for readers interested in the pharmaceutical industry, and the quantitative methods that serve as its foundation. Specific disciplines covered include: Biostatistics Pharmacometrics Genomics Bioinformatics Pharmacoepidemiology Commercial analytics Operational analytics Quantitative Methods in Pharmaceutical Research and Development is ideal for undergraduate students interested in learning about real-world applications of quantitative methods, and the potential career options open to them. It will also be of interest to experts working in these areas

An Adaptive Threshold Method for Anomaly-based Intrusion Detection Systems

Anomaly-based Detection Systems (ADSs) attempt to learn the features of behaviors and events of a system and/or users over a period to build a profile of normal behaviors. There has been a growing interest in ADSs and typically conceived as more powerful systems One of the important factors for ADSs is an ability to distinguish between normal and abnormal behaviors in a given period. However, it is getting complicated due to the dynamic network environment that changes every minute. It is dangerous to distinguish between normal and abnormal behaviors with a fixed threshold in a dynamic environment because it cannot guarantee the threshold is always an indication of normal behaviors. In this paper, we propose an adaptive threshold for a dynamic environment with a trust management scheme for efficiently managing the profiles of normal and abnormal behaviors. Based on the assumption of the statistical analysis-based ADS that normal data instances occur in high probability regions while malicious data instances occur in low probability regions of a stochastic model, we set two adaptive thresholds for normal and abnormal behaviors. The behaviors between the two thresholds are classified as suspicious behaviors, and they are efficiently evaluated with a trust management scheme