A novel integrative risk index of papillary thyroid cancer progression combining genomic alterations and clinical factors.

Although the majority of papillary thyroid cancer (PTC) is indolent, a subset of PTC behaves aggressively despite the best available treatment. A major clinical challenge is to reliably distinguish early on between those patients who need aggressive treatment from those who do not. Using a large cohort of PTC samples obtained from The Cancer Genome Atlas (TCGA), we analyzed the association between disease progression and multiple forms of genomic data, such as transcriptome, somatic mutations, and somatic copy number alterations, and found that genes related to FOXM1 signaling pathway were significantly associated with PTC progression. Integrative genomic modeling was performed, controlling for demographic and clinical characteristics, which included patient age, gender, TNM stages, histological subtypes, and history of other malignancy, using a leave-one-out elastic net model and 10-fold cross validation. For each subject, the model from the remaining subjects was used to determine the risk index, defined as a linear combination of the clinical and genomic variables from the elastic net model, and the stability of the risk index distribution was assessed through 2,000 bootstrap resampling. We developed a novel approach to combine genomic alterations and patient-related clinical factors that delineates the subset of patients who have more aggressive disease from those whose tumors are indolent and likely will require less aggressive treatment and surveillance (p = 4.62 × 10-10, log-rank test). Our results suggest that risk index modeling that combines genomic alterations with current staging systems provides an opportunity for more effective anticipation of disease prognosis and therefore enhanced precision management of PTC

Randomized controlled trial of a home-based action observation intervention to improve walking in Parkinson disease

Published in final edited form as: Arch Phys Med Rehabil. 2016 May ; 97(5): 665–673. doi:10.1016/j.apmr.2015.12.029.OBJECTIVE: To examine the feasibility and efficacy of a home-based gait observation intervention for improving walking in Parkinson disease (PD). DESIGN: Participants were randomly assigned to an intervention or control condition. A baseline walking assessment, a training period at home, and a posttraining assessment were conducted. SETTING: The laboratory and participants' home and community environments. PARTICIPANTS: Nondemented individuals with PD (N=23) experiencing walking difficulty. INTERVENTION: In the gait observation (intervention) condition, participants viewed videos of healthy and parkinsonian gait. In the landscape observation (control) condition, participants viewed videos of moving water. These tasks were completed daily for 8 days. MAIN OUTCOME MEASURES: Spatiotemporal walking variables were assessed using accelerometers in the laboratory (baseline and posttraining assessments) and continuously at home during the training period. Variables included daily activity, walking speed, stride length, stride frequency, leg swing time, and gait asymmetry. Questionnaires including the 39-item Parkinson Disease Questionnaire (PDQ-39) were administered to determine self-reported change in walking, as well as feasibility. RESULTS: At posttraining assessment, only the gait observation group reported significantly improved mobility (PDQ-39). No improvements were seen in accelerometer-derived walking data. Participants found the at-home training tasks and accelerometer feasible to use. CONCLUSIONS: Participants found procedures feasible and reported improved mobility, suggesting that observational training holds promise in the rehabilitation of walking in PD. Observational training alone, however, may not be sufficient to enhance walking in PD. A more challenging and adaptive task, and the use of explicit perceptual learning and practice of actions, may be required to effect change

An Online Discriminative Approach to Background Subtraction

We present a simple, principled approach to detecting foreground objects in video sequences in real-time. Our method is based on an on-line discriminative learning technique that is able to cope with illumination changes due to discontinuous switching, or illumination drifts caused by slower processes such as varying time of the day. Starting from a discriminative learning principle, we derive a training algorithm that, for each pixel, computes a weighted linear combination of selected past observations with time-decay. We present experimental results that show the proposed approach outperforms existing methods on both synthetic sequences and real video data

A heuristic explicit model predictive control framework for Eco-trajectory planning: Theoretical analysis and case study

The trajectory planning problem (TPP) has become increasingly crucial in the research of next-generation transportation systems, but it presents challenges due to the non-linearity of its constraints. One specific case within TPP, namely the Eco-trajectory Planning Problem (EPP), poses even greater computational difficulties due to its nonlinear, high-order, and non-convex objective function. This paper proposes a heuristic explicit predictive model control (heMPC) framework to address the eco-trajectory planning problem in scenarios without lane-changing behavior. The heMPC framework consists of an offline module and an online module. In the offline module, we build an optimal eco-trajectory batch by optimizing a series of simplified EPPs considering different system initial states and terminal states, which is equivalent to the lookup table in the general eMPC framework. The core idea of the offline module is to finish all potential optimization and computing in advance to avoid any form of online optimization in the online module. In the online module, we provide static and dynamic trajectory planning algorithms. Both algorithms greatly improve the computational efficiency of planning and only suffer from a limited extent of optimality losses through a batch-based selection process because any optimization and calculation are pre-computed in the offline module. The latter algorithm is also able to face possible emergencies and prediction errors. Both theoretical analysis and numerical are shown and discussed to test the computational quality and efficiency of the heMPC framework under a mixed-traffic flow environment that incorporates human-driving vehicles (HDV) and connected and automated vehicles (CAV) with different market penetration rates (MPR)

Gold nanorods mediate tumor cell death by compromising membrane integrity

Folate-conjugated gold nanorods targeted to tumor cell surfaces produced severe membrane damage upon near-infrared irradiation. Photoinduced injury to the plasma membrane resulted in a rapid increase in intracellular calcium (shown in green) with subsequent disruption of the actin network, featured prominently by the formation of membrane blebs

Internal Governance and Real Earnings Management

ABSTRACT We examine whether internal governance affects the extent of real earnings management in U.S. corporations. Internal governance refers to the process through which key subordinate executives provide checks and balances in the organization and affect corporate decisions. Using the number of years to retirement to capture key subordinate executives' horizon incentives and using their compensation relative to CEO compensation to capture their influence within the firm, we find that the extent of real earnings management decreases with key subordinate executives' horizon and influence. The results are robust to alternative measures of internal governance and to various approaches used to address potential endogeneity, including a difference-in-differences approach. In cross-sectional analyses, we find that the effect of internal governance is stronger for firms with more complex operations where key subordinate executives' contribution is higher, is enhanced when CEOs are less powerful, is weaker when the capital markets benefit of meeting or beating earnings benchmarks is higher, and is stronger in the post-SOX period. This paper contributes to the literature by examining how internal governance affects the extent of real earnings management and by shedding light on how the members of the management team work together in shaping financial reporting quality. JEL Classifications: G32; M40