Acquired resistance to EGFR targeted therapy in non-small cell lung cancer: Mechanisms and therapeutic strategies.

Distributed fiber-optic vibration sensors receive extensive investigation and play a significant role in the sensor panorama. Optical parameters such as light intensity, phase, polarization state, or light frequency will change when external vibration is applied on the sensing fiber. In this paper, various technologies of distributed fiber-optic vibration sensing are reviewed, from interferometric sensing technology, such as Sagnac, Mach-Zehnder, and Michelson, to backscattering-based sensing technology, such as phase-sensitive optical time domain reflectometer, polarization-optical time domain reflectometer, optical frequency domain reflectometer, as well as some combinations of interferometric and backscattering-based techniques. Their operation principles are presented and recent research efforts are also included. Finally, the applications of distributed fiber-optic vibration sensors are summarized, which mainly include structural health monitoring and perimeter security, etc. Overall, distributed fiber-optic vibration sensors possess the advantages of large-scale monitoring, good concealment, excellent flexibility, and immunity to electromagnetic interference, and thus show considerable potential for a variety of practical applications.

https://www.mdpi.com/1424-8220/16/8/1164/pdf

Distributed Fiber-Optic Sensors for Vibration Detection

Phase-sensitive optical time domain reflectometry (Φ-OTDR) is easy to be interfered by ambient noises, and the nonlinear coherent addition of different interferences always makes it difficult to detect real human intrusions and causes high nuisance alarm rates (NARs) in practical applications. In this paper, an effective temporal signal separation and determination method is proposed to improve its detection performance in complicated noisy environments. Unlike the conventional analysis of transverse spatial signals, the time-evolving sensing signal of Φ-OTDR system is at first obtained for each spatial point by accumulating the changing OTDR traces at different moments. Then, its longitudinal temporal signal is decomposed and analyzed by a multi-scale wavelet decomposition method. By selectively recombining the corresponding scale components, it can effectively extract human intrusion signals, and separate the influences of slow change of the system and other environmental interferences. Compared with the conventional differentiation way and fast Fourier transformation denoising method, the SNRs of the detecting signals for the proposed method is always the best, which can be raised by up to ∼35 dB for the best case. Moreover, from the decomposed components, different event signals can be effectively determined by their energy distribution features, and the NAR can be controlled to be less than 2% in the test.

Separation and Determination of the Disturbing Signals in Phase-Sensitive Optical Time Domain Reflectometry (Φ-OTDR)

Small cell lung cancer (SCLC) is characterized by rapid growth and high metastatic capacity. It has strong epidemiologic and biologic links to tobacco carcinogens. Although the majority of SCLCs exhibit neuroendocrine features, an important subset of tumors lacks these properties. Genomic profiling of SCLC reveals genetic instability, almost universal inactivation of the tumor suppressor genes TP53 and RB1, and a high mutation burden. Because of early metastasis, only a small fraction of patients are amenable to curative-intent lung resection, and these individuals require adjuvant platinum-etoposide chemotherapy. Therefore, the vast majority of patients are currently being treated with chemoradiation with or without immunotherapy. In patients with disease confined to the chest, standard therapy includes thoracic radiotherapy and concurrent platinum-etoposide chemotherapy. Patients with metastatic (extensive-stage) disease are treated with a combination of platinum-etoposide chemotherapy plus immunotherapy with an anti-programmed death-ligand 1 monoclonal antibody. Although SCLC is initially very responsive to platinum-based chemotherapy, these responses are transient because of the development of drug resistance. In recent years, the authors have witnessed an accelerating pace of biologic insights into the disease, leading to the redefinition of the SCLC classification scheme. This emerging knowledge of SCLC molecular subtypes has the potential to define unique therapeutic vulnerabilities. Synthesizing these new discoveries with the current knowledge of SCLC biology and clinical management may lead to unprecedented advances in SCLC patient care. Here, the authors present an overview of multimodal clinical approaches in SCLC, with a special focus on illuminating how recent advancements in SCLC research could accelerate clinical development. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.3322/caac.21785

Clinical insights into small cell lung cancer: Tumor heterogeneity, diagnosis, therapy, and future directions

With the rapid development and extensive applications of phase-sensitive optical time-domain reflectometry to long distance pipeline safety monitoring, it is still challenging to find a very efficient way to achieve highly correct recognition and really deep understanding of physical events sensed in a wide dynamic environment, as the vibration signals usually exhibit non-linear and non-stationary characteristics caused by the complicated environments. In this paper, a dynamic time sequence recognition and knowledge mining method based on the hidden Markov models (HMMs) is proposed to solve this problem. First, local structure feature of the signal is extracted in multiple analysis domains in the time sequence order; and then the HMMs are trained, built, and used to mine the temporal evolution information and identify the sequential state process of typical events. The experimental results with real field test data show that the average recognition accuracy of this paper is as high as 98.2% for frequently encountered five typical events along buried pipelines. All the related performance metrics such as precision, recall, and F-score are better than those traditional machine learning methods such, RF, XGB, DT, and BN.

A Dynamic Time Sequence Recognition and Knowledge Mining Method Based on the Hidden Markov Models (HMMs) for Pipeline Safety Monitoring With Φ-OTDR

Real Intrusion Detection for Distributed Fiber Fence in Practical Strong Fluctuated Noisy Backgrounds

Gefitinib/chemotherapy vs chemotherapy in EGFR mutation-positive NSCLC after progression on 1st line gefitinib (IMPRESS study): Final overall survival (OS) analysis

1353PCNS response to osimertinib in Asian-Pacific patients (pts) with T790M-positive advanced NSCLC: data from an open-label Phase II trial (AURA17)

389P Updated analysis from the KEYNOTE-042 China study: 1L pembrolizumab (pembro) vs chemotherapy (chemo) in Chinese patients (pts) with advanced NSCLC with PD-L1 TPS ≥1%

Rigid-elastic Coupling Dynamic Model and Dynamic Characteristics of a Spring-type of Traction Robot

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