In vivo modeling of metastatic human high-grade serous ovarian cancer in mice

Metastasis is responsible for 90% of human cancer mortality, yet it remains a challenge to model human cancer metastasis in vivo. Here we describe mouse models of high-grade serous ovarian cancer, also known as high-grade serous carcinoma (HGSC), the most common and deadliest human ovarian cancer type. Mice genetically engineered to harbor Dicer1 and Pten inactivation and mutant p53 robustly replicate the peritoneal metastases of human HGSC with complete penetrance. Arising from the fallopian tube, tumors spread to the ovary and metastasize throughout the pelvic and peritoneal cavities, invariably inducing hemorrhagic ascites. Widespread and abundant peritoneal metastases ultimately cause mouse deaths (100%). Besides the phenotypic and histopathological similarities, mouse HGSCs also display marked chromosomal instability, impaired DNA repair, and chemosensitivity. Faithfully recapitulating the clinical metastases as well as molecular and genomic features of human HGSC, this murine model will be valuable for elucidating the mechanisms underlying the development and progression of metastatic ovarian cancer and also for evaluating potential therapies

A dynamic control approach for energy-efficient production scheduling on a single machine under time-varying electricity pricing

This paper proposes a dynamic control algorithm to enable an energy-aware single machine scheduling under the time-varying electricity pricing policy, in which price rates remain fixed day-to-day over the season. The key issue is to assign a set of jobs to available time periods where different electricity prices are assigned, while considering requested due dates of jobs so as to minimize total penalty costs for earliness and tardiness of jobs and total energy consumption costs, simultaneously. As the first contribution of this study, we develop a new mixed integer nonlinear programming (MINLP) model that aims at determining job arrival times and resulting earliness and tardiness of jobs and energy consumption costs for machine idle and normal processing. Second, an efficient heuristic approach based on continuous-time variable control models and algorithm is developed. The proposed heuristic adaptively changes job arrival times and due dates, which finally determine production sequence over the time periods of different electricity prices, machine turn-off, and machine idle with minimum energy consumption costs and just-in-time (JIT) penalty. Energy and JIT performance of the proposed approach is examined using real energy and machining parameters of a HAAS machine and compared to those of the metaheuristic approach. For relatively large size data groups, the proposed approach incurs about 4∼11% higher energy consumption costs on average, which are offset by up to 99% lower JIT costs, resulting in 10∼94% lower total costs on average compared to the metaheuristic approach. The proposed time-scaled heuristic algorithm yields extremely short computational time, which enables production managers to flexibly select proper production strategies and to implement them for different production environments

Body and Hand–Object ROI-Based Behavior Recognition Using Deep Learning

Behavior recognition has applications in automatic crime monitoring, automatic sports video analysis, and context awareness of so-called silver robots. In this study, we employ deep learning to recognize behavior based on body and hand–object interaction regions of interest (ROIs). We propose an ROI-based four-stream ensemble convolutional neural network (CNN). Behavior recognition data are mainly composed of images and skeletons. The first stream uses a pre-trained 2D-CNN by converting the 3D skeleton sequence into pose evolution images (PEIs). The second stream inputs the RGB video into the 3D-CNN to extract temporal and spatial features. The most important information in behavior recognition is identification of the person performing the action. Therefore, if the neural network is trained by removing ambient noise and placing the ROI on the person, feature analysis can be performed by focusing on the behavior itself rather than learning the entire region. Therefore, the third stream inputs the RGB video limited to the body-ROI into the 3D-CNN. The fourth stream inputs the RGB video limited to ROIs of hand–object interactions into the 3D-CNN. Finally, because better performance is expected by combining the information of the models trained with attention to these ROIs, better recognition will be possible through late fusion of the four stream scores. The Electronics and Telecommunications Research Institute (ETRI)-Activity3D dataset was used for the experiments. This dataset contains color images, images of skeletons, and depth images of 55 daily behaviors of 50 elderly and 50 young individuals. The experimental results showed that the proposed model improved recognition by at least 4.27% and up to 20.97% compared to other behavior recognition methods

Body and Hand–Object ROI-Based Behavior Recognition Using Deep Learning

Behavior recognition has applications in automatic crime monitoring, automatic sports video analysis, and context awareness of so-called silver robots. In this study, we employ deep learning to recognize behavior based on body and hand–object interaction regions of interest (ROIs). We propose an ROI-based four-stream ensemble convolutional neural network (CNN). Behavior recognition data are mainly composed of images and skeletons. The first stream uses a pre-trained 2D-CNN by converting the 3D skeleton sequence into pose evolution images (PEIs). The second stream inputs the RGB video into the 3D-CNN to extract temporal and spatial features. The most important information in behavior recognition is identification of the person performing the action. Therefore, if the neural network is trained by removing ambient noise and placing the ROI on the person, feature analysis can be performed by focusing on the behavior itself rather than learning the entire region. Therefore, the third stream inputs the RGB video limited to the body-ROI into the 3D-CNN. The fourth stream inputs the RGB video limited to ROIs of hand–object interactions into the 3D-CNN. Finally, because better performance is expected by combining the information of the models trained with attention to these ROIs, better recognition will be possible through late fusion of the four stream scores. The Electronics and Telecommunications Research Institute (ETRI)-Activity3D dataset was used for the experiments. This dataset contains color images, images of skeletons, and depth images of 55 daily behaviors of 50 elderly and 50 young individuals. The experimental results showed that the proposed model improved recognition by at least 4.27% and up to 20.97% compared to other behavior recognition methods

Logistical Simulation Modeling for Planning a Soil Remediation Process

This paper describes the development of a discrete event simulation model using the FlexSim software to support planning for soil remediation at Korean nuclear power plants that are undergoing decommissioning. Soil remediation may be required if site characterization shows that there has been radioactive contamination of soil from plant operations or the decommissioning process. The simulation model was developed using a dry soil separation and soil washing process. Preliminary soil data from the Kori 1 nuclear power plant was used in the model. It was shown that a batch process such as soil washing can be effectively modeled as a discrete event process. Efficient allocation of resources and efficient waste management including volume and classification reduction can be achieved by use of the model for planning the soil remediation process. Cost will be an important criterion in the choice of suitable technologies for soil remediation but is not included in this conceptual model

Oxygen plasma treatment of HKUST-1 for porosity retention upon exposure to moisture

Despite their remarkable properties, metal-organic frameworks (MOFs) present vulnerable structures that are sensitive to moisture; therefore, their application to real field situations is challenging. Herein, an O2 plasma technique was introduced as a new method for the activation and protection of porosity in HKUST-1. In an unprecedented manner, O2 plasma-treated HKUST-1 retains its porosity after a long exposure to moisture as compared to pristine HKUST-1. Porosity retention was examined by N2 adsorption/desorption measurements of non-activated HKUST-1 after exposure to moisture. © 2017 The Royal Society of Chemistry.1

Stepwise Soft Actor–Critic for UAV Autonomous Flight Control

Despite the growing demand for unmanned aerial vehicles (UAVs), the use of conventional UAVs is limited, as most of them require being remotely operated by a person who is not within the vehicle’s field of view. Recently, many studies have introduced reinforcement learning (RL) to address hurdles for the autonomous flight of UAVs. However, most previous studies have assumed overly simplified environments, and thus, they cannot be applied to real-world UAV operation scenarios. To address the limitations of previous studies, we propose a stepwise soft actor–critic (SeSAC) algorithm for efficient learning in a continuous state and action space environment. SeSAC aims to overcome the inefficiency of learning caused by attempting challenging tasks from the beginning. Instead, it starts with easier missions and gradually increases the difficulty level during training, ultimately achieving the final goal. We also control a learning hyperparameter of the soft actor–critic algorithm and implement a positive buffer mechanism during training to enhance learning effectiveness. Our proposed algorithm was verified in a six-degree-of-freedom (DOF) flight environment with high-dimensional state and action spaces. The experimental results demonstrate that the proposed algorithm successfully completed missions in two challenging scenarios, one for disaster management and another for counter-terrorism missions, while surpassing the performance of other baseline approaches

Flexible Strain Sensors Fabricated by Meniscus-Guided Printing of Carbon Nanotube–Polymer Composites

Printed strain sensors have promising potential as a human–machine interface (HMI) for health-monitoring systems, human-friendly wearable interactive systems, and smart robotics. Herein, flexible strain sensors based on carbon nanotube (CNT)–polymer composites were fabricated by meniscus-guided printing using a CNT ink formulated from multiwall nanotubes (MWNTs) and polyvinylpyrrolidone (PVP); the ink was suitable for micropatterning on nonflat (or curved) substrates and even three-dimensional structures. The printed strain sensors exhibit a reproducible response to applied tensile and compressive strains, having gauge factors of 13.07 under tensile strain and 12.87 under compressive strain; they also exhibit high stability during ∼1500 bending cycles. Applied strains induce a contact rearrangement of the MWNTs and a change in the tunneling distance between them, resulting in a change in the resistance (Δ<i>R</i>/<i>R</i>₀) of the sensor. Printed MWNT–PVP sensors were used in gloves for finger movement detection; these can be applied to human motion detection and remote control of robotic equipment. Our results demonstrate that meniscus-guided printing using CNT inks can produce highly flexible, sensitive, and inexpensive HMI devices