EGFR L858R Mutation and Polymorphisms of Genes Related to Estrogen Biosynthesis and Metabolism in Never-Smoking Female Lung Adenocarcinoma Patients

Purpose: To assess whether polymorphisms of genes related to estrogen biosynthesis and metabolism are associated with EGFR mutations. Experimental Design: We studied 617 patients with lung adenocarcinoma, including 302 never-smoking women. On the basis of multiple candidate genes approach, the effects of polymorphisms of CYP17, CYP19A1, ER alpha, and COMT in association with the occurrence of EGFR mutations were evaluated using logistic regression analysis. Results: In female never-smokers, significant associations with EGFR L858R mutation were found for the tetranucleotide (TTTA)(n) repeats in CYP19A1 (odds ratio, 2.6; 95%CI, 1.2-5.7 for 1 or 2 alleles with (TTTA)(n) repeats > 7 compared with both alleles with (TTTA) n repeats <= 7), and the rs2234693 in ERa (OR, 2.1; 95% CI, 1.1-4.0 for C/T and C/C genotypes compared with T/T genotype). The C/C genotype (vs. T/T genotype) of ERa was significantly associated with EGFR L858R mutation (OR, 3.0; 95% CI, 1.1-8.1), in-frame deletion (OR, 2.9; 95% CI, 1.1-7.6) and other mutations (OR, 4.3; 95% CI, 1.3-14.0). The genotype of COMT rs4680 was significantly associated with EGFR L858R mutation in female and male never-smokers showing OR's (95% CI) of 1.8 (1.0-3.2) and 3.6 (1.1-11.3), respectively, for genotypes G/A and G/G compared with genotype A/A. The number of risk alleles of CYP17, CYP19A1, ERa, and COMT was associated with an increasing OR of EGFR L858R mutation in female never-smokers (P = 0.0002 for trend). Conclusions: The L858R mutation of EGFR is associated with polymorphisms of genes related to estrogen biosynthesis and metabolism in never-smoking female lung adenocarcinoma patients. Clin Cancer Res; 17(8); 2149-58. (C) 2011 AACR

Development of IoT module with AI function using STM32 chip

The application of Internet of Things (IoT) has been widely used in our lives with the advancement of related software and hardware technologies. In order to make these IoT modules more intelligent, many IoT modules have begun to incorporate artificial intelligence algorithms. Therefore, this paper develops IoT module with STM32 chip as main controller. This module uses fuzzy analytic hierarchy process (fuzzy-AHP) and adaptive fusion method (AFM) to improve the correctness and self-learning ability of the sensor. In terms of communication, the IoT module has Ethernet, Wi-Fi, LoRa, etc. communication interfaces. We also built a web server on this module, so the IoT module can operate directly in the browser. Finally, we developed a monitoring system. Through this monitoring system, multiple IoT modules can be constructed into a sensor network. This monitoring system can also use same algorithm to correct and isolate data from modules or sensors in the network to make this module more intelligent and applicable in different areas

Laser rangefinder and monocular camera data fusion for human-following algorithm by pmb-2 mobile robot in simulated gazebo environment

The paper presents a human-following algorithm for an autonomous mobile robot, which is equipped with a 2D laser rangefinder (LRF) and a monocular camera. As a rule, quality of a human tracking by a LRF is reduced in cluttered environments. We used a monocular camera to increase a human-tracking reliability. In contradiction with popular human-tracking algorithms that apply only a 2D LRF, our algorithm does not impose any restrictions on a type of human’s clothes, and our approach does not require a human head and an upper body to be located within a monocular camera field of view. Several human trackers and variations of our algorithm were compared in the Gazebo virtual experiments within a free corridor and an office room environment. The virtual experiments demonstrated that our method successfully improved a human-tracking quality being employed with the human-following virtual PMB-2 robot

Rfid-based warehouse management system prototyping using a heterogeneous team of robots

© CLAWAR Association Ltd. Robotic warehouse management is a promising field of research with a high practical applicability. Typically, warehouses are strictly organized and controlled environments with a high potential for rapid robot deployments. To provide system robustness every robotic deployment should be tested in advance using simulations. Such virtual testing environment should correspond well with a real world warehouse environment and be consistent with international and national standards. In this paper, we present a warehouse Gazebo simulation that was constructed in accordance with Government Standards of Russian Federation and international standards. We implemented a simplified Radio Frequency IDentification (RFID) transmitter-receiver mechanism within the Gazebo simulator and tested it in a virtual warehouse environment using a heterogeneous team of TIAGo Base mobile robot and PX4-based UAV. Our constructed virtual warehouse environment and RFID mechanism models are open-source for academic community use

Person-Following Algorithm Based on Laser Range Finder and Monocular Camera Data Fusion for a Wheeled Autonomous Mobile Robot

© 2020, Springer Nature Switzerland AG. Reliable human following is one of the key capabilities of service and personal assisting robots. This paper presents a novel person tracking and following approach for autonomous mobile robots that are equipped with a 2D laser rangefinder (LRF) and a monocular camera. The proposed method does not impose restrictions on a person’s clothes, does not require a head or an upper body to be within a camera field of view and is suitable for low height indoor robots as well. The algorithm is based on a metric that takes into an account parameters obtained directly from LRF and monocular camera data. The algorithm was implemented and tested in the Gazebo simulator. Next, it was integrated into a control system of the TIAGo Base mobile robot and successfully validated in university environment experiments with real people. In addition, this paper proposes a new criterion of algorithm performance estimation, which is a function of false positives number and traveled distances by a person and by a robot. Further this criterion is used to compare performance of the proposed method with the Multiple Instance Learning (MIL) tracker in simulated and in real world environments

Modelling a TurtleBot3 Based Delivery System for a Smart Hospital in Gazebo

The design of a "smart hospital"environment is described in this paper. Mobile ground robots perform transportation tasks between multiple stations located in different rooms while navigating in an environment with moving objects such as humans and other mobile robots. The robot is equipped with a distance sensor (LIDAR), based on the indicators of which objects are detected and the robot is localized. Robots can be assigned tasks to be executed through a centralized interface. Tasks are assigned to a specific robot, and, depending on the type of task, the robot is autonomously directed to the station associated with the task in the building. We are considering the concept of defining possible robot behaviors as a finite set of states with certain transitions. To test the system, a hospital map was constructed in a Gazebo simulation