ZigBee-assisted ad-hoc networking of multi-interface mobile devices

Wireless ad hoc network is decentralized wireless network, which does not rely on a preexisting infrastructure, such as routers in wired networks or access points in managed (infrastructure) wireless networks. Instead, each node participates in routing by forwarding data for other nodes. The determination of which nodes forward data is made dynamically based on the network connectivity. Node density has a great impact on the performance and efficiency of wireless ad hoc networks by influencing some factors such as capacity, network contention, routing efficiency, delay, and connectivity. On one hand, maintaining stable connectivity is a big challenge for sparsely deployed and highly dynamic ad hoc wireless network. Vehicle ad hoc network (VANET) which consists of highly mobile vehicles with wireless interfaces is one type of such network, especially in rural areas where vehicles traffic are very sparse. One of the most important applications built on top of VANET is the safety application. In VANET safety applications, source vehicles that observe accidents or some other unsafe conditions of the roads generate warning messages about the conditions, and propagate the warning messages to the following vehicles. In this way, the following drivers have the opportunity to do some necessary action before they reach the potential danger zone to avoid accident. The safety application requires timely and accurate warning message detection and delivery. However, recent researches have shown that sparse and highly dynamic vehicle traffic leads network fragmentation, which poses a crucial research challenge for VANET safety application. On the other hand, reducing contention and thus maximizing the network throughput is also a big challenge for densely deployed ad hoc wireless network, especially when many devices are located in a small area and each device has heavy duty message to transmit. The WiFi interface perhaps is the most common interface found in mobile devices for data transfer as it provides good combination of throughout, range and power efficiency. However, the WiFi interface may have to consume a large amount of bandwidth and energy for contention and combating collision, especially when mobile devices located in a small area all have heavy traffic to transmit. Meanwhile, ZigBee is an emerging wireless communication technology which supports low-cost, low-power and short-range wireless communication. Nowadays, it has been common for a mobile device, such as smart phone, PDA and laptop, to have both WiFi and Bluetooth interfaces. As the ZigBee technology becomes more and more mature, it will not be surprising to see the ZigBee interface commonly embedded in mobile devices together with WiFi and Bluetooth interfaces in the near future. The co-existence of the ZigBee and the WiFi interfaces in the same mobile device inspires us to develop new techniques to address the above two issues. Specifically, this thesis presents two systems built based on ZigBee-assisted ad-hoc networking of multi-interface mobile devices. In order to achieve stable connectivity in a sparse and dynamic VANET, the first system integrates a network of static roadside sensors and highly mobile vehicles to improve driving safety. In order to reduce contention in a densely deployed ad hoc wireless network, the second system assists WiFi transmission with ZigBee interface for multi-interface mobile devices. Extensive implementations and experiments have been conducted to demonstrate the effectiveness of our proposed systems

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities

Advancing Precision Medicine: Unveiling Disease Trajectories, Decoding Biomarkers, and Tailoring Individual Treatments

Chronic diseases are not only prevalent but also exert a considerable strain on the healthcare system, individuals, and communities. Nearly half of all Americans suffer from at least one chronic disease, which is still growing. The development of machine learning has brought new directions to chronic disease analysis. Many data scientists have devoted themselves to understanding how a disease progresses over time, which can lead to better patient management, identification of disease stages, and targeted interventions. However, due to the slow progression of chronic disease, symptoms are barely noticed until the disease is advanced, challenging early detection. Meanwhile, chronic diseases often have diverse underlying causes and can manifest differently among patients. Besides the external factors, the development of chronic disease is also influenced by internal signals. The DNA sequence-level differences have been proven responsible for constant predisposition to chronic diseases. Given these challenges, data must be analyzed at various scales, ranging from single nucleotide polymorphisms (SNPs) to individuals and populations, to better understand disease mechanisms and provide precision medicine. Therefore, this research aimed to develop an automated pipeline from building predictive models and estimating individual treatment effects based on the structured data of general electronic health records (EHRs) to identifying genetic variations (e.g., SNPs) associated with diseases to unravel the genetic underpinnings of chronic diseases. First, we used structured EHRs to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. In this step, we employed causal inference methods (constraint-based and functional causal models) for feature selection and utilized Markov chains, attention long short-term memory (LSTM), and Gaussian process (GP). SHapley Additive exPlanations (SHAPs) and local interpretable model-agnostic explanations (LIMEs) further extended the work to identify important clinical features. Next, I developed a novel counterfactual-based method to predict individual treatment effects (ITE) from observational data. To discern a “balanced” representation so that treated and control distributions look similar, we disentangled the doctor’s preference from the covariance and rebuilt the representation of the treated and control groups. We use integral probability metrics to measure distances between distributions. The expected ITE estimation error of a representation was the sum of the standard generalization error of that representation and the distance between the distributions induced. Finally, we performed genome-wide association studies (GWAS) based on the stage information we extracted from our unsupervised disease progression model to identify the biomarkers and explore the genetic correction between the disease and its phenotypes

A nodule on the forearm

Glomus tumors are benign tumors of the skin. Clinically, these tumors can present as solid, painful subcutaneous nodules, frequently seen on the hand (particularly subungual region). Glomangiomyomas are the least common histological type of glomus tumor. In the literature, there are only a few glomangiomyoma cases of the forearm location. We report a patient with a painful nodule, diagnosed as glomangiomyoma. Surgical excision was performed and no recurrence was observed after 5 years' follow-up

Optimized Finite Difference Methods for Seismic Acoustic Wave Modeling

The finite difference (FD) methods are widely used for approximating the partial derivatives in the acoustic/elastic wave equation. Grid dispersion is one of the key numerical problems and will directly influence the accuracy of the result because of the discretization of the partial derivatives in the wave equation. Therefore, it is of great importance to suppress the grid dispersion by optimizing the FD coefficient. Various optimized methods are introduced in this chapter to determine the FD coefficient. Usually, the identical staggered grid finite difference operator is used for all of the first-order spatial derivatives in the first-order wave equation. In this chapter, we introduce a new staggered grid FD scheme which can improve the efficiency while still preserving high accuracy for the first-order acoustic/elastic wave equation modeling. It uses different staggered grid FD operators for different spatial derivatives in the first-order wave equation. The staggered grid FD coefficients of the new FD scheme can be obtained with a linear method. At last, numerical experiments were done to demonstrate the effectiveness of the introduced method

A Novel Model of Working Set Selection for SMO Decomposition Methods

In the process of training Support Vector Machines (SVMs) by decomposition methods, working set selection is an important technique, and some exciting schemes were employed into this field. To improve working set selection, we propose a new model for working set selection in sequential minimal optimization (SMO) decomposition methods. In this model, it selects B as working set without reselection. Some properties are given by simple proof, and experiments demonstrate that the proposed method is in general faster than existing methods.Comment: 8 pages, 12 figures, it was submitted to IEEE International conference of Tools on Artificial Intelligenc

The Hedgehog pathway: role in cell differentiation, polarity and proliferation.

Hedgehog (Hh) is first described as a genetic mutation that has "spiked" phenotype in the cuticles of Drosophila in later 1970s. Since then, Hh signaling has been implicated in regulation of differentiation, proliferation, tissue polarity, stem cell population and carcinogenesis. The first link of Hh signaling to cancer was established through discovery of genetic mutations of Hh receptor gene PTCH1 being responsible for Gorlin syndrome in 1996. It was later shown that Hh signaling is associated with many types of cancer, including skin, leukemia, lung, brain and gastrointestinal cancers. Another important milestone for the Hh research field is the FDA approval for the clinical use of Hh inhibitor Erivedge/Vismodegib for treatment of locally advanced and metastatic basal cell carcinomas. However, recent clinical trials of Hh signaling inhibitors in pancreatic, colon and ovarian cancer all failed, indicating a real need for further understanding of Hh signaling in cancer. In this review, we will summarize recent progress in the Hh signaling mechanism and its role in human cancer

Network effects and strategic effects: essays on multilevel technology adoption

This dissertation analyzes the network effects and strategic effects in technology and certification adoption. The first chapter analyzes the externalities of the Electronic Medical Records (EMR) technology. EMR is a multi-level technology, which is characterized by both network effects and strategic effects. Differentiated adoption levels might yield different external effects to neighboring potential adopters. By using a panel of U.S. hospitals' EMR adoption and applying a set of flexible reduced-form regressions, I find the presence of a complementary effect for the first-level EMR adoption, and a competitive effect for higher-level adoption. Chapter two studies the network effects and strategic effects of EMR technology adoption among hospitals using a dynamic structural model. I estimate this dynamic structural model by applying the methods by Bajari, Benkard and Levin (2007) (BBL), and Pakes, Ostrovsky and Berry (2007) (POB), which provide a two-step algorithm for estimating dynamic games. The primary result is the presence of both competition and complementation in this adoption. Furthermore, I perform some counterfactual experiments. The first experiment is to compare hospitals' adoption behaviors in monopoly and duopoly markets, and I find that network effects and strategic interactions are important in the duopoly market. I then perform a policy experiment to examine the effect of the government's incentive programs for EMR adoption, and I find it would greatly stimulate adoption. The third chapter studies the adoption of LEED (Leadership in Energy & Environmental Design) certification, which is an internationally recognized building certification system that evaluates environmental impact. Competition may affect not only certification adoption but also the adoption of quality standards. LEED offers four levels of certifications to indicate the different standards of environmental impact. By using a detailed dataset of LEED registrations in the U.S. and applying the Multinomial Test of Agglomeration and Dispersion (MTAD) developed by Rysman & Greenstein (2005), we find the allocation of certification levels is more agglomerated than independent random choice would predict