Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV status. Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods. Our approach ranked the highest in the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI) grand challenge: Oropharynx Cancer (OPC) Radiomics Challenge, Human Papilloma Virus (HPV) Status Prediction. Further analysis on the most relevant radiomic features distinguishing HPV positive and negative subjects suggested that HPV positive patients usually have smaller and simpler tumors.

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SummaryTranscriptome deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high-dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step toward linking tumor transcriptomic data with clinical outcomes. An R package, scripts, and data are available: https://github.com/wwylab/DeMixTallmaterials.

We develop a novel method DeMixT for the gene expression deconvolution of three compartments in cancer patient samples: tumor, immune and surrounding stromal cells. In validation studies using mixed cell line and laser-capture microdissection data, DeMixT yielded accurate estimates for both cell proportions and compartment-specific expression profiles.Application to the head and neck cancer data shows DeMixT-based deconvolution provides an important step to link tumor transcriptome data with clinical outcomes. MAIN TEXTHeterogeneity of malignant tumor cells adds confounding complexity to cancer treatment. The evaluation of individual components of tumor samples is complicated by the tumor-stromal-

In this paper, we consider the dual-rate sampled-data state-feedback control problem for an active suspension system of an electric vehicle. In the active suspension system, there exist 2 accelerometers to measure the heave acceleration of the sprung mass and the vertical acceleration of the unsprung mass, respectively. When the 2 accelerations are measured by sampled data under different sampling periods, the difficulty arising from the dual-rate sampled data makes the active suspension stabilization problem challenging but interesting. In this paper, a linear hybrid stabilizer is proposed, which is implemented using dual-rate sampled-data state feedback. In order to deal with the more difficult stabilization problem under different triggering time instants, a coordinate transformation is proposed. A useful technical theorem is proposed in the stability analysis to show that the proposed hybrid controller can guarantee the states of the active suspension system being asymptotically stabilized or at least bounded to arbitrarily small domains. The experiment result is similar to the simulation result and indicates that the proposed active suspension controlling system is effective. KEYWORDS active suspension system, dual-rate, sampled-data control INTRODUCTIONElectric vehicles (EVs) driven by in-wheel motors have potential for energy efficiency and environmental protection. In recent decades, vehicle motion control, energy optimization, and performance benefits 1-4 have gained the attention of researchers. The chassis (sprung mass) of an EV is connected to 4 wheels (unsprung masses) by the suspension system. The in-wheel motors increase the unsprung mass, affecting the ride comfort and handling of the EV. 5 The suspension system has 2 functions, ie, one is to handle the vehicle's roadholding and braking for good active safety and driving pleasure, and the other one is to keep vehicle occupants comfortable and provide a ride quality reasonably well isolated from road profiles. If the suspension system is externally controlled, then it is a semiactive or active suspension.Active suspension is a type of automotive suspension that controls the vertical displacement of the wheels relative to the chassis or vehicle body, as opposed to a passive suspension where the displacement is determined entirely by the road profiles. Such an active suspension system includes an electrically powered actuator and a gas spring that cooperatively provide the support between the sprung and unsprung masses of the vehicle. Active suspension systems for vehicles have more recently been introduced to reduce movement of the sprung vehicle mass by reacting to force inputs from the 1610