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Digital beamforming (DBF) with scan-on-receive (SCORE) technique in elevation is a powerful technique that enables a spaceborne synthetic aperture radar (SAR) to achieve high-resolution wide swath (HRWS) imaging. In the spaceborne DBF-SAR system, sampling signals from each channel are weighted by weights generated by a digital signal processing system in real-time. However, the contradiction between the shortage of spaceborne hardware resources and resource demand of the multichannel real-time signal processing increases the difficulty of system design. In order to solve this problem, a novel weight generator and an improved intermediate frequency (IF) DBF real-time processing architecture are proposed in this article. By taking advantage of the special properties of the SCORE algorithm, the proposed weight generator calculates weights using a linear polynomial algorithm. The simulation result shows that a low-order approximation can achieve high performance. The proposed generator can correct multichannel amplitude and phase error at a low cost on hardware resources. The effectiveness of the proposed method is verified by experiments with a raw data processing instance of an X-band 16 channels DBF-SAR.

Currently, in the spaceborne synthetic aperture radar (SAR) community, the sliding spotlight mode has been widely applied to achieve high-resolution imaging. However, limited by the system pulse repetition frequency (PRF), it is usually impossible to realize a large-scale coverage at the same time. The technique of azimuth multichannel alleviates the PRF restriction by receiving additional spatial samples proportional to the number of Rx channels. A system combining the technique of azimuth multichannel with sliding spotlight SAR is able to achieve ultrahigh-resolution and wide-swath imaging simultaneously. In the advanced multichannel sliding spotlight mode, some problems may occur. First, azimuth beam progressive steering leads to an enlarged Doppler bandwidth over N· PRF, and conventional azimuth multichannel reconstruction algorithms developed for the stripmap mode are, thus, not directly suitable for the sliding spotlight mode. Second, the phase characteristics of multichannel sliding spotlight SAR is different from that of multichannel stripmap SAR due to the time variation of the system Doppler centroid. Third, processing of spaceborne multichannel sliding spotlight SAR experimental data shows that the convolution function used in the full-aperture reconstruction method is closely related to the antenna phase configuration strategy of the phased array antenna system. Therefore, an improved processing frame is required for the multichannel sliding spotlight SAR, reexamining the validity of assumptions, such as the stop-go-stop approximation and that of a noncurved trajectory during the relatively long aperture time. Recently, as an exploratory study, a dual-channel sliding spotlight experiment was conducted with the Gaofen-3 (GF-3) SAR. Two experimental data sets were acquired with different PRFs. In this article, the GF-3 payload and the experiment design are introduced, and a complete multichannel sliding spotlight SAR data processing chain is provided. The first imaging results of spaceborne multichannel sliding spotlight SAR are demonstrated.

Photothermal materials with broadband optical absorption and high conversion efficiency are intensively pursued to date. Here, proposing by the d-d interband transitions, we report an unprecedented high-entropy alloy FeCoNiTiVCrCu nanoparticles that the energy regions below and above the Fermi level (±4 eV) have been fully filled by the 3 d transition metals, which realizes an average absorbance greater than 96% in the entire solar spectrum (wavelength of 250 to 2500 nm). Furthermore, we also calculated the photothermal conversion efficiency and the evaporation rate towards the steam generation. Due to its pronounced full light capture and ultrafast local heating, our high-entropy-alloy nanoparticle-based solar steam generator has over 98% efficiency under one sun irradiation, meanwhile enabling a high evaporation rate of 2.26 kg m -2 h -1 .

Mu He,1 Kun Qing,2,3 Nicholas J Tustison,2 Zach Beaulac,4 Tabitha W King,5 Thomas B Huff,5 Mikell Paige,4 Kranthikiran Earasi,1 Roselove Nunoo-Asare,1 Sarah Struchen,2 Marie Burdick,1 Zhimin Zhang,1 Alan Ropp,2 Grady W Miller,2 James T Patrie,6 Jaime F Mata,2 John P Mugler 3rd,2 Yun Michael Shim1,2 1Department of Medicine, Division of Pulmonary and Critical Care, University of Virginia, Charlottesville, VA, USA; 2Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, USA; 3Radiation Oncology, Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA; 4Department of Chemistry, George Mason University, Fairfax, VA, USA; 5Shared Research Instrumentation Facility, George Mason University, Manassas, VA, USA; 6School of Public Health, University of Virginia, Charlottesville, VA, USACorrespondence: Yun Michael ShimDepartment of Medicine, Division of Pulmonary and Critical Care, University of Virginia, PO Box 800546, Charlottesville, VA, 22908-0546, USAEmail yss6n@virginia.edu

The displaced phase center antennas in azimuth and digital beamforming (DBF) in elevation are two state-of-the-art techniques for achieving high-resolution wide swath (HRWS) imaging in the multichannel synthetic aperture radar (SAR) systems. However, due to the atmospheric turbulence, airborne HRWS-SARs inevitably suffer trajectory disturbances, which will consequently defocus the SAR image. Although various motion compensation (MoCo) methods have been proposed, they are mostly designed for the traditional single-channel SAR and, therefore, ignore the channel-dependent posture error. The posture motion error will introduce residual motion and time-variant channel errors, which not only defocuses the image but also causes azimuth ambiguity and degrades the SNR improvement of the final images of the azimuth multichannel and DBF-SAR, respectively. To solve these problems, a novel MoCo scheme for 2-D multichannel SAR systems is proposed. The posture error is described and calculated in matrix form through the use of quaternions. Then, the posture error is transformed into the translational motion error for each receiving channel and is subsequently compensated precisely. To address the residual aperture-variant motion error, a modified aperture-dependent MoCo is integrated into the proposed scheme. Simulations and airborne experiments, including processing the data of a C-band SAR system with four azimuth channels and an X-band DBF-SAR system with 16 elevation channels, have been implemented to validate the effectiveness of the proposed MoCo scheme.

Inflammation is the phenotypic form of various diseases. Recent development in molecular imaging provides new insights into the diagnostic and therapeutic evaluation of different inflammatory diseases as well as diseases involving inflammation such as cancer. While conventional imaging techniques used in the clinical setting provide only indirect measures of inflammation such as increased perfusion and altered endothelial permeability, optical imaging is able to report molecular information on diseased tissue and cells. Optical imaging is a quick, noninvasive, nonionizing, and easy-to-use diagnostic technology which has been successfully applied for preclinical research. Further development of optical imaging technology such as optoacoustic imaging overcomes the limitations of mere fluorescence imaging, thereby enabling pilot clinical applications in humans. By means of endogenous and exogenous contrast agents, sites of inflammation can be accurately visualized in vivo. This allows for early disease detection and specific disease characterization, enabling more rapid and targeted therapeutic interventions. In this review, we summarize currently available optical imaging techniques used to detect inflammation, including optical coherence tomography (OCT), bioluminescence, fluorescence, optoacoustics, and Raman spectroscopy. We discuss advantages and disadvantages of the different in vivo imaging applications with a special focus on targeting inflammation including immune cell tracking.

Recently most of the state-of-the-art semantic segmentation methods have focused on context modeling for more accurate prediction. As real-world images often contain multiple objects and stuff, image features may have complex and multi-modal distributions. However, existing methods do not fully consider sch complex distributions, having limited capability for context modeling. Towards addressing this problem, this paper proposes a second-order encoding network (SoENet) trainable end-to-end for harvesting complex contextual knowledge. At the core of SoENet is an encoding module which can capture second-order statistics in individual feature subspaces. Specifically, we divide the entire feature space into a set of subspaces (clusters) represented by codewords, in each of which a covariance matrix is computed for second-order statistical modeling. The covariance matrices of all subspaces are concatenated to form a 3D tensor, which is then subject to convolutions and nonlinear activations and finally used for scaling of input features. In this way, we can encode the context which involves the complex distribution into learning process in an end-to-end manner. The proposed SoENet is evaluated on four commonly used challenging benchmarks, i.e., PASCAL Context, PASCAL VOC 2012, ADE20K and Cityscapes. The experiments show that our network significantly outperforms its counterparts and is competitive compared to state-of-the-art methods.

Physical space appeared with the birth of the earth. With the emergence of social space, thinking space, and cyberspace (STCs), things in physical space are continuously enriched. Consequently, the PhiNet, an abstracted network concept such as relationships between things, is becoming more complex. Physical space plays a fundamental role in promoting the connection and development with the other three spaces. We think that PhiNet of Things (PoT) is a unified description of pure physical space and the evolving physical space affected by STCs. While the Internet of Things (IoT) is described from a cyber view, in this article, the definition of PoT is put forward from a natural view. Besides, the evolution of PoT is identified by the time sequence in which the four spaces appeared. At each stage, research is carried out from two perspectives of things and PhiNet, and two specific examples are presented to illustrate the change process of things and the related PhiNet. In addition, two applications are listed to explain the usability of PoT in the current development stage. Finally, the article gives the possible future development direction of PoT. The purpose of this article is to illustrate the fundamental role of physical space in the continuous development through time sequence.

Chronic granulomatous disease (CGD) results from deficiency of nicotinamide adenine dinucleotide phosphate(NADPH) oxidase and impaired reactive oxygen species (ROS) generation. This leads to impaired killing of Aspergillus and, independently, a pathologic hyperinflammatory response to the organism. We hypothesized that neutrophil-derived ROS inhibit the inflammatory response to Aspergillus and that acute lung injury in CGD is due to failure of this regulation. Mice with gp91phox deficiency, the most common CGD mutation, had more severe lung injury, increased neutrophilinfiltration, and increased lung tumor necrosis factor (TNF) after Aspergillus challenge compared with wild-types. Neutrophils were surprisingly the predominant source of TNF in gp91phox-deficient lungs. TNF neutralization inhibited neutrophil recruitment in gp91phox-deficient mice and protected from lung injury. We propose that, in normal hosts, Aspergillus stimulates TNF-dependent neutrophil recruitment to the lungs and neutrophil-derived ROS limit inflammation. In CGD, in contrast, recruited neutrophils are the dominant source of TNF, promoting further neutrophil recruitment in a pathologic positive-feedback cycle, resulting in progressive lung injury.

In recent times, there have been attempts to leverage artificial intelligence (AI) techniques in a broad range of cyber security applications. Therefore, this paper surveys the existing literature (comprising 54 papers mainly published between 2016 and 2020) on the applications of AI in user access authentication, network situation awareness, dangerous behavior monitoring, and abnormal traffic identification. This paper also identifies a number of limitations and challenges, and based on the findings, a conceptual human-in-the-loop intelligence cyber security model is presented.

Arrhythmia is a kind of cardiovascular disease that seriously threats human health. Intelligent analysis of electrocardiogram (ECG) is an effective method for the early prevention and precise treatment to arrhythmia. In clinical ECG waveforms, it is common to see the multi-label phenomenon that one patient would be labeled with multiple types of arrhythmia. However, the current research is mainly to use the multiclass methods to solve the multi-label problem, ignoring the correlations between diseases and causing information loss. Therefore, this article aims: 1) to propose a multi-label feature selection method based on ECG (MS-ECG) and design an evaluation criterion of ECG features based on kernelized fuzzy rough sets so as to choose the optimal feature subset and optimize ECG feature space and 2) to propose the multi-label classification algorithm of arrhythmia based on ECG (MC-ECG) by establishing a multiobjective optimization model. This algorithm based on sparsity constraint explores the correlations between arrhythmia diseases and analyzes the mapping relationship between ECG features and arrhythmia diseases, so that one ECG signal would be automatically and accurately given multiple labels. Through sufficient experiments to prove the feasibility of our methods, we obtain the selected feature subset composed of 23 ECG features by MS-ECG. For the six evaluation criterions of MC-ECG, average precision is 0.8462, hamming loss is 0.1041, ranking loss is 0.1313, one-error is 0.2023, coverage is 0.4015, and micro-F1 is 0.6088. The outcome presents optimal to the current algorithms.

A large density difference of a basin-type insulator in gas-insulated metal-enclosed switchgear (GIS) weakens its mechanical strength, which will threaten the safe and stable operation of GIS. In this article, an ultrasonic nondestructive testing method for density uniformity of GIS basin-type insulators was proposed. First, seven standard samples with different Al2O3 content were tested by an ultrasonic testing system, and then a mapping relationship between density and ultrasonic velocity was established. Second, the uncertainty of the testing system was obtained, and the ultrasonic velocities of 192 positions in a 126 kV three-phase basin-type insulator were measured. Finally, the density distribution of the insulator was obtained based on the mapping equation of velocity-density, and a contour plot of density was established by the cubic spline interpolation. The results show that there was a positive linear correlation between the density of standard samples and the ultrasonic velocity, and the determination coefficient was 0.992. Notably, the expanded uncertainty of ultrasonic propagation time for detecting 45 mm-thick EP/Al2O3 composites was only $0.0112~\mu \text{s}$ . The ultrasonic propagation time of detection positions in the insulator was from 31.0634 to $31.4708~\mu \text{s}$ and the difference was greater than $0.0112~\mu \text{s}$ , which illustrated that the testing system meets the requirements for detecting the density of the insulator. According to the contour plot of density, the density distribution of the insulator was shown clearly and efficiently. The density was higher at the bottom edge of the insulator and the upper part of the insert, whereas the density was smaller at the lateral upper part of the insulator and the lower part of the insert. Specifically, the maximum density of the insulator was 2.36 g/cm3 and the minimum density was 2.25 g/cm3. The feasibility of nondestructive testing density by the ultrasonic method is verified, which provides a new method for density evaluation of GIS insulators.

Raman spectroscopy can provide structural fingerprints to identify molecules by means of spectral library searching. However, it is difficult to share the spectral library between different Raman spectrometers because of the nonlinear displacement in Raman shift. In this study, we propose a Raman spectra Standardization method using Variable Penalty dynamic time warping (RS-VPdtw), which can synchronize the nonlinear displacement between spectra acquired with different spectrometers. We have compared the standardization performance of RS-VPdtw and MWFFT on the spectra of 13 real samples acquired with 6 different spectrometers. The mean spectral similarity of RS-VPdtw and MWFFT increased from 0.79 to 0.97 and 0.91 respectively. Results show that RS-VPdtw is significantly better than MWFFT in Raman spectra standardization. The Raman spectra acquired with different spectrometers can be standardized by RS-VPdtw to search the same spectral library, which can avoid the time-consuming and labor-intensive reestablishment of spectral libraries for different spectrometers. This means that RS-VPdtw is a promising and valuable method to solve the spectra standardization problem in large-scale applications of Raman spectroscopy.

With Moore's law approaching its physical limits, low-temperature computing technology is ushering in unprecedented development opportunities. Rapid single-flux-quantum (RSFQ) circuit technology is currently the most mature superconducting integrated circuit technology. Based on the current fabrication process, we propose an 8-bit bit-parallel RSFQ microprocessor. The proposed microprocessor processes 8-bit data each clock cycle. Ten different instructions are executed. The microprocessor mainly consists of an on-chip instruction memory, two data registers, an instruction decoder, an 8-bit bit-parallel arithmetic logic unit, and a program counter. The microprocessor contains 7702 JJs (based on the Open Dataset of CONNECT Cell Library for AIST ADP2) without considering splitters, Josephson transmission lines, and passive transmission lines. We perform a logic-level simulation of the proposed microprocessor. The simulation results show correct operation with a target frequency of 16.7 GHz.

In this article, we propose a novel architecture for single-flux-quantum (SFQ) circuits. It is compatible to the existing synchronous and asynchronous timing schemes and reduces the hardware cost for implementing the pipeline blocking and flushing, which are the two methods to improve the performance of SFQ microprocessors. We simulate two crucial elements, and the functions of the elements are verified successfully. The proposed architecture simplifies the logic design of the circuits, and it can be considered as a general architecture for SFQ circuits because of the compelling characteristics.

Super-resolution optical microscopy is a useful tool to investigate physical and biological characteristics on the sub-100-nanometer scale. Here, we develop pulsed saturated absorption competition ...

Background Serum miRNA was once found as potential disease survival index,thus we investigated the role of miRNA in predicting prognosis in loco-regionally advanced NPC patients treated with CCRT. Methods This study included two phases: (i) We enrolled 3 NPC patients with recurrence or distant metastasis (experimental group, EG) and 3 NPC patients in clinical remission (control group, CG),who were treated with CCRT within 5 years. The paired serum was collected before and after treatment and biomarkers were discovered by LNA-TaqMan Human MicroRNA Arrays. (ii) we used the bioinformatic analysis, marker selection and an independent validation by qRT-PCR to analyse the serums of 29 NPC patients with recurrent disease or distant metastasis and 19 NPC patients in clinical remission treated with CCRT. Using the Kaplan-Meier method, log-rank test and Cox regression model to estimate the accuracy of the miRNAs to predict PFS and OS, and identified factors significantly associated with prognosis, respectively. Results Using fold change≥2.0 or ≤ 0.5 and p ≤ 0.05 as cutoff levels, we identified 1 up-regulated and 6 down-regulated miRNAs, 1 up-regulated and 9 down-regulated miRNAs in EG versus CG before and after CCRT, respectively. After these down-regulated miRNAs were dealed with bioinformatics analysis and normalization, only 5 different miRNAs were significantly reduced, which there were no significant difference in the expression of miRNA-26b, miRNA-29a and miRNA-125b before CCRT, and the expression of miRNA-143 and miRNA-29b after CCRT in the serum samples of 48 NPC patients. Based on this, we calculated a risk score with the expression of miRNA-26b、miRNA-29a、miRNA-125b、miRNA-29b、miRNA-143 and then classified patients as high or low risk group. Cox regression model suggested that combining miRNA-29a and miRNA-125b before CCRT with miRNA-26b after CCRT was independent prognostic factors for PFS (HR = 3.149, 95%CI:1.018–9.115, p = 0.034), whereas combining the former two is independent for OS (HR = 5.146, 95%CI:1.674–15.817, p = 0.04). Conclusions For loco-regionally advanced NPC patients treated with CCRT, especially high-risk patients- serum miRNAs, such as miRNA-29a, miRNA-125b and miRNA-26b etc., play an important role in predicting prognosis factors of PFS and OS, which will contribute to the strategic direction for future research.

Chemotherapy resistance has become a hold back and major clinical challenge in osteosarcoma cancer. The alteration and subcellular distribution of apurinic/apyrimidinic endonuclease 1 (APE1) has been reported to be involved in chemotherapy resistance in many cancers. Here, we report that the cytoplasmic distribution of APE1 plays a key role in the sensitivity of combination platinum chemotherapy in osteosarcoma. Interestingly, the prevalence of cisplatin-induced DNA damage and apoptosis in low cytoplasmic APE1 osteosarcoma cell lines was higher than in high expression of cytoplasmic APE1 cell lines. Overexpression of cytoplasmic APE1 protected the osteosarcoma cells from CDDP-induced apoptosis. In addition, clinical data also show that the level of cytoplasmic APE1 was negatively associated with sensitivity to combination chemotherapy of cisplatin in osteosarcoma patients. Our findings suggest that cytoplasmic APE1 plays a significant role in chemotherapy resistance. This role is a supplement to the extranuclear function of APE1, and cytoplasmic APE1 expression level could be a promising predictor of platinum treatment prognosis for osteosarcoma patients.

The application of optical microscopy in four-dimensional (spatial and temporal) super-resolution imaging poses challenges because of the requirement of a long acquisition time or high illumination intensity. In this paper, we introduce simultaneous two-angle axial ratiometry (STARII) for

We report a widefield super-resolution (SR) fluorescence microscopy technique termed total internal reflection fluorescence pattern-illuminated Fourier ptychographic microscopy (TIRF-piFPM). It employs pattern-illuminated Fourier ptychography (piFP) under the total internal reflection fluorescence (TIRF) mode, providing a lateral resolution of ∼100 nm, reducing the background level, and correcting unknown optical aberrations. Like the total internal reflection fluorescence structure illuminated microscopy (TIRF-SIM), the illumination field of TIRF-piFPM is modulated by sinusoidal patterns generated by evanescent wave interference. It differs from TIRF-SIM in that TIRF-piFPM reconstructs the SR image by FP iteration. To demonstrate the performance of TIRF-piFPM, we compare it with TIRF-SIM by conducting simulations and experiments and prove that TIRF-piFPM can provide a better result than TIRF-SIM in terms of its robustness to noise and aberration correction ability. In addition, dynamic changes of mitochondria in U2OS cells are captured with a temporal resolution of 2 s, demonstrating its live-cell imaging capability. The addvantages may enable TIRF-piFPM to serve as an alternative to SR fluorescence microscopes in the TIRF family, with promising applications in biological and biomedical imaging.

The identification of lymph node metastases is important for the diagnosis, treatment and prognosis of patients with lung cancer. We found DDX49 was associated with the lymph node metastases in lung cancer by the Akt/β‐catenin pathway. Transcriptome sequencing, bioinformatics analysis, quantitative RT‐PCR, cell transfection and the Cancer Genome Atlas (TCGA) data set were used to identify DDX49 responsible for lymph node metastases. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to explore the possible molecular mechanism in experimental cell. The DDX49 gene was correlated significantly with lymph node metastases of lung cancer. The knockdown of DDX49 inhibited the cell proliferation and migration in PC‐9 and H460 cells. The mechanism research found downexpression of DDX49 decreased the Akt/β‐catenin pathway in lung cancer cell. In vivo experiments showed that DDX49 promoted the proliferation and metastases of lung cancer cells by increasing the Akt/β‐catenin pathway. These findings suggested that DDX49 may be useful as a novel biomarker of lymph node metastases and therapeutic target for lung cancer metastasis.

Here, we propose a novel imaging method, which is called image scanning difference microscopy (ISDM), for superresolution imaging. In ISDM, we implement a detector array composed of 19 avalanche photodiodes (APD) rather than single-point detector in standard confocal microscopy for reconstructing superresolved images with higher signal-to-noise ratio (SNR). Combining with our former proposed fluorescence emission difference (FED) method, we have achieved a lateral resolution of 111 nm (∼λ/6) without the damage of image quality, the highest FED resolution to the best of our knowledge. With its simple setup and remarkable performance, we believe that ISDM can become a versatile observation tool in biology and other fundamental studies. LAY DESCRIPTION: Fluorescence emission difference (FED) microscopy is a really simple and generalisable superresolved fluorescence microscopy method based on PSF engineering and difference algorithm recently. Compared to stimulated-emission-depletion fluorescence microscopy (STED), FED don't need complicated system or precise alignment and polarisation, available for wide variety of dyes and has low photobleaching and phototoxicity for living cells. However, the distortion caused by negative value is one of the biggest obstacles to the further development of FED. In light of this, we propose a novel superresolution imaging method based on the FED method with parallel detection system, which is called image scanning difference microscopy (ISDM). Our method has achieved a significant breakthrough in FED, increasing the resolution further while reducing artefacts generated by negative values, which cannot be accomplished through combining other methods. In addition, ISDM does not require complex setup and optical alignment, long time imaging and imposing no constraint on dyes. Importantly, we realised a transverse resolution of ∼λ/6 (triple diffraction limit) with single wavelength, single incident path and low light intensity, which has never been achieved in any other far-field superresolution microscopy.

MARS (MS-Assisted Resolution of Signal) is a computational tool for feature extraction using multivariate curve resolution approaches. However, it is time-consuming for large-scale GC-MS datasets and sensitive to the component number estimation. Here we introduce MARS 2 with substantial improvements to overcome these limitations. Reverse matching (RM) and principal component optimization-iterative transformation target factor analysis (PCO-ITTFA) are developed to reduce the time for locating peak regions and determine the correct component number for resolving optimal features. The greatest strength of MARS 2 is “resolve once, extract anywhere”. It means that mass spectra of each component were needed to resolve from only one profile. Then, chromatographic features can be extracted automatically from hundreds of other profiles with the assistance from the resolved mass spectra. To evaluate its performance, plasma datasets and fatty acid standard mixtures were processed by MARS 2, AMDIS, ADAP-GC 3.0, eRah and MS-DIAL. Amino acid standard mixtures were analyzed by MARS 2 for calibration. Results show that MARS 2 can achieve better performance in both qualitative and quantitative analysis. It is implemented in Python programming language and open-sourced at https://github.com/mapancsu/MARS2 .

Depletion beams with long pulse durations ( ∼ 600 p s ) have recently been applied in stimulated emission depletion (STED) microscopy to reduce photobleaching and phototoxicity. Therefore, improving the resolution of pulse STED at lower depletion power for the super-resolution imaging of live biological specimens has attracted increasing interest. Herein, we present a simple method termed ratiometric photon reassignment based on the fluorescence lifetime, in which highly spatially resolved long-lifetime fluorophore components in the center are extracted, and short-lifetime fluorophore components in the periphery are discarded to improve resolution without increasing the depletion power. The experimental results demonstrate improved resolution and signal-to-noise ratio compared with traditional time-gated STED. Our proposed method requires lower budget and data processing because, in contrast to the separation of photons by lifetime tuning, lifetime measurements are not required.

The prevalence and large-scale uptake of the Internet of Things (IoT) have led to a growing trend of Identification Exploding, namely heterogeneous entities respectively identified to provide convenient intelligent services. To address challenges under Identification Exploding, unified modeling has become a promising approach to generalizing identification for heterogeneous entities in IoT. This paper surveys and discusses Identification Exploding's background to explore the possibility of unified modeling as one of the solutions. Meanwhile, challenges of realizing unified modeling are discussed. After that, comprehensive reviews on the latest modeling approaches and methods covering various IoT entities are carried out, including sensed entities and sensing devices. Special attention has been paid to modeling under resource-constrained IoT environments and relevant modeling-based industry solutions with critical analysis. It is proved that unified modeling is extremely significant under Identification Exploding Tendency in the IoT era.

Equivalence checking is a key component of the verification methodology for digital circuit designs. In this paper, we propose an equivalence checking framework for superconducting rapid single-flux-quantum (RSFQ) logic circuits which include acyclic circuits and bit-slice-based cyclic circuits. It consists of a structure checker and a logic checker. The structure checker is used to check whether the circuit meets the design rules of superconducting RSFQ logic circuits. The logic checker can be used to check whether two RSFQ gate-level circuits have the same logic function. For the logic checker, we propose a logic equivalence checking method based on logic cone partition. The circuit network is simplified layer by layer and iteratively partitioned into logic cones, each of which is verified by the SMT solver. The experimental results show the feasibility of our approach on superconducting RSFQ logic circuits.

The present study addresses the cardiac arrhythmia (CA) classification problem using the deep learning (DL)-based method for electrocardiography (ECG) data analysis. Recently, various DL techniques have been utilized to classify arrhythmias, with one typical approach to developing a one-dimensional (1D) convolutional neural network (CNN) model to handle the ECG signals in the time domain. Although the CA classification in the time domain is very prevalent, current methods’ performances are still not robust or satisfactory. This study aims to develop a solution for CA classification in two dimensions by introducing the recurrence plot (RP) combined with an Inception-ResNet-v2 network. The proposed method for nine types of CA classification was tested on the 1st China Physiological Signal Challenge 2018 dataset. During implementation, the optimal leads (lead II and lead aVR) were selected, and then 1D ECG segments were transformed into 2D texture images by the RP approach. These RP-based images as input signals were passed into the Inception-ResNet-v2 for CA classification. In the CPSC, Georgia, and the PTB_XL ECG databases of the PhysioNet/Computing in Cardiology Challenge 2020, the RP-based method achieved an average F1-score of 0.8521, 0.8529, and 0.8862, respectively. The results suggested the excellent generalization ability of the proposed method. To further assess the performance of the proposed method, we compared the 2D RP-image-based solution with the published 1D ECG-based works on the same dataset. Also, it was compared with two traditional ECG transform into 2D image methods, including the time waveform of the ECG recordings and time-frequency images based on continuous wavelet transform (CWT). The proposed method achieved the highest average F1-score of 0.844, with only two leads of the 12-lead ECG original data, which outperformed other works. Therefore, the promising results indicate that the 2D RP-based method has a high clinical potential for CA classification using fewer lead ECG signals.

Purpose This study aimed to develop and validate a prognostic model for metastasis-free survival (MFS) based on genes that may functionally interact with cytotoxic T lymphocytes (CTLs) and M2 macrophages in patients with triple-negative breast cancer (TNBC) who underwent adjuvant radiotherapy.Methods The transcriptional profiles and phenotypical files of TNBC and other subtypes of breast cancer were downloaded from the Gene Expression Omnibus (GEO). The abundance of infiltrated immune cells was evaluated through CIBERSORTx or MCP-counter. A weighted linear model, the score for MFS (SMFS), was developed by using least absolute shrinkage and selection operator (LASSO) in GSE58812 and validated in GSE2034 and GSE12276. The biological implication of SMFS was explored by evaluating its associations with TNBC molecular subtypes and other radiosensitivity- or immune-related signatures. Results A model consisting of the gene expression ratios of PCDH12/ELP3, PCDH12/MSRA and FAM160B2/MSRA with nonzero coefficients finally selected by LASSO was developed in GSE58812. In GSE2034 (treatment with adjuvant radiotherapy), SMFS was significantly associated with MFS in TNBC patients (HR=8.767, 95% CI: 1.856-41.408, P=0.006) and, to a lesser extent, in non-TNBC patients (HR=2.888, 95% CI: 1.076-7.750, P=0.035). However, the interaction of subtype (TNBC vs non-TNBC) and SMFS tended to be significant (Pinteraction=0.081). In contrast, SMFS was not significantly associated with MFS in either TNBC patients (P=0.499) or non-TNBC patients (P=0.536) in GSE12276 (treatment without radiotherapy). Among the four TNBC molecular subtypes, the c1 and c4 subtypes exhibited higher CTL infiltration and lower SMFS values than the c2 and c3 subtypes. In addition, SMFS was positively correlated with the abundance of endothelial cells (r=0.413, PConclusions The proposed model has the potential to predict MFS in TNBC patients after adjuvant radiotherapy. SMFS may represent a measurement of tumor immune suppression.

Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC–MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data analysis pipeline of untargeted metabolomics. In this study, pure ion chromatograms were extracted from a liquor dataset and left-sided colon cancer (LCC) dataset by K-means-clustering-based Pure Ion Chromatogram extraction method version 2.0 (KPIC2). Then, the nonlinear low-dimensional embedding by uniform manifold approximation and projection (UMAP) showed the separation of samples from different groups in reduced dimensions. The discriminant models were established by extreme gradient boosting (XGBoost) based on the features extracted by KPIC2. Results showed that features extracted by KPIC2 achieved 100% classification accuracy on the test sets of the liquor dataset and the LCC dataset, which demonstrated the rationality of the XGBoost model based on KPIC2 compared with the results of XCMS (92% and 96% for liquor and LCC datasets respectively). Finally, XGBoost can achieve better performance than the linear method and traditional nonlinear modeling methods on these datasets. UMAP and XGBoost are integrated into KPIC2 package to extend its performance in complex situations, which are not only able to effectively process nonlinear dataset but also can greatly improve the accuracy of data analysis in non-target metabolomics.

Combinations of steady-state visual evoked potential (SSVEP) and electromyography (EMG) are one of the most widely used hybrid brain-computer interfaces (BCI). For users who are suffering from severe motor impairments and could only control muscles above their necks, the EMG of jaw clench is commonly used together with SSVEP. Traditional asynchronous signal collecting and serial processing method is time-consuming. This study explored the simultaneous classification of SSVEP and jaw clench-related EMG from the same occipital electrodes. A convolutional neural network (CNN) model was adopted to classify the fusion signal after extracting different time and frequency domain features. Synchronous 12-class identification from 3 jaw clench patterns and 4 stimulate frequencies was tested on ten subjects. The 10-folder cross-validation results showed that using only 4 occipital electrodes, the CNN model with continuous wavelet transform feature achieved the overall best $89.3\pm4.0\%$ accuracy for sync 12-class. Besides, the parallel acquiring and analyzing method has the lowest consuming time. The proposed method could reduce the signal channels and shorten the collect-process time of the SSVEP-EMG hybrid BCI.

The combination of retention time (RT), accurate mass and tandem mass spectra can improve the structural annotation in untargeted metabolomics. However, the incorporation of RT for metabolite identification has received less attention because of the limitation of available RT data, especially for hydrophilic interaction liquid chromatography (HILIC). Here, the Graph Neural Network-based Transfer Learning (GNN-TL) is proposed to train a model for HILIC RTs prediction. The graph neural network was pre-trained using an in silico HILIC RT dataset (pseudo-labeling dataset) with ∼306 K molecules. Then, the weights of dense layers in the pre-trained GNN (pre-GNN) model were fine-tuned by transfer learning using a small number of experimental HILIC RTs from the target chromatographic system. The GNN-TL outperformed the methods in Retip, including the Random Forest (RF), Bayesian-regularized neural network (BRNN), XGBoost, light gradient-boosting machine (LightGBM), and Keras. It achieved the lowest mean absolute error (MAE) of 38.6 s on the test set and 33.4 s on an additional test set. It has the best ability to generalize with a small performance difference between training, test, and additional test sets. Furthermore, the predicted RTs can filter out nearly 60% false positive candidates on average, which is valuable for the identification of compounds complementary to mass spectrometry.

There is a high incidence of radiation enteritis (RE) after abdominal radiotherapy. The occurrence of RE seriously affects the treatment and quality of life of patients; however, its pathogenesis is complex and there are no effective drugs for its prevention or treatment. Intestinal ischemia plays an important role in the occurrence of enteritis. Previous studies have shown that targeting GTP-cyclohydrolase 1 (Gch1) to improve intestinal ischemia could be a new strategy to prevent and treat RE. A high content of the naturally occurring phthalide derivative ligustilide (LIG) has been found in the plant drug Rhizoma Ligustici Chuanxiong for the treatment of cardiovascular diseases. The purpose of this study was to evaluate the protective effects of LIG on RE. Ionizing radiation (IR) rat and endothelial cell models were used to observe and record rat body weights and stool morphologies, measure intestinal blood perfusion by laser Doppler blood flow imaging, determine the diastolic functions of mesenteric arteries, detect the levels of Gch1/BH4/eNOS pathway-related proteins and regulatory molecules in the mesenteric arteries and endothelial cells, and predict affinity by molecular docking technology. The results showed that LIG significantly improved the body weights, loose stools, intestinal villi lengths, intestinal perfusion and vasodilatory functions of IR rats. LIG also significantly improved Gch1 protein and BH4 levels in the mesenteric arteries and endothelial cells after IR, increased the NO content, reduced superoxide accumulation, and improved p-eNOS (Ser1177) levels in endothelial cells. LIG has good affinity for Gch1, which significantly improves its activity. These results indicate that LIG is the preferred compound for the prevention and treatment of RE by improving intestinal ischemia through the Gch1/BH4/eNOS pathway. This study provides a theoretical basis and new research ideas for the development of new drugs for RE.

This paper proposed a feature selection method combined with multi-time-scales analysis and heart rate variability (HRV) analysis for middle and early diagnosis of congestive heart failure (CHF). In previous studies regarding the diagnosis of CHF, researchers have tended to increase the variety of HRV features by searching for new ones or to use different machine learning algorithms to optimize the classification of CHF and normal sinus rhythms subject (NSR). In fact, the full utilization of traditional HRV features can also improve classification accuracy. The proposed method constructs a multi-time-scales feature matrix according to traditional HRV features that exhibit good stability in multiple time-scales and differences in different time-scales. The multi-scales features yield better performance than the traditional single-time-scales features when the features are fed into a support vector machine (SVM) classifier, and the results of the SVM classifier exhibit a sensitivity, a specificity, and an accuracy of 99.52%, 100.00%, and 99.83%, respectively. These results indicate that the proposed feature selection method can effectively reduce redundant features and computational load when used for automatic diagnosis of CHF.

Despite fungi playing an important role in nutrient decomposition in aquatic ecosystems and being considered as vital actors in the ecological processes, they received limited attention regarding the community in aquaculture pond sediments which are extremely important and typically disturbed habitats. Using an ITS1 region of fungal rDNA, this study aimed to investigate sediment fungal communities in fish, crab, and crayfish ponds for decades of farming practices at representative aquaculture regions in the middle Yangtze River basin, China. We then aimed to explore the community patterns associated with species-based farming practices in the ponds at 18 farms. The results showed that the pond sediments harbored more than 9,000 operational taxonomic units. The sediments had significantly higher alpha diversity in crab ponds compared to that in fish and crayfish ponds. The fungal phyla largely belonged to Ascomycota and Chytridiomycota, and the dominance of Rozellomycota over Basidiomycota and Aphelidiomycota was observed. The majority of sediment fungal members were ascribed to unclassified fungi, with higher proportions in fish ponds than crab and crayfish ponds. Further, the fungal communities were markedly distinct among the three types of ponds, suggesting divergent patterns of fungal community assemblages caused by farming practices in aquaculture ponds. The community diversity and structure were closely correlated to sediment properties, especially sediment carbon content and pH. Thus, the distribution and pattern of fungal communities in the sediments appear to primarily depend on species-based farming practices responsible for the resulting sediment carbon content and pH in aquaculture ponds. This study provides a detailed snapshot and extension of understanding fungal community structure and variability in pond ecosystems, highlighting the impacts of farming practices on the assembly and succession of sediment fungal communities in aquaculture ponds.

This paper reports a new candidate material λ-Ti3O5 for microwave absorption. λ-Ti3O5 has been proposed to be metastable and has emerged at room temperature only in the form of nanocrystals. To prepare high purity λ-Ti3O5, the formation energies of 27 different elements doped into λ-Ti3O5 were calculated by first-principles from the energy point of view. It was found that Li has the strongest stabilizing effect on λ-Ti3O5. The prepared λ-Ti3O5 and Li/λ-Ti3O5 show good microwave absorption properties: the efficient absorption bandwidth of λ-Ti3O5 can reach 7.9 GHz at 1.7 mm thickness, and the efficient absorption bandwidth of Li/λ-Ti3O5 can reach 7.4 GHz at 1.2 mm thickness. In addition, we propose a strategy of using the number of states of the micro-electric field to predict the microwave absorption properties of materials, which is helpful to the understanding of the origin and development of microwave absorption.

As a kind of extrusion process, the forward–backward–radial extrusion (FBRE) process can be used to produce complex-shaped parts with hoop protrusions or flanges. The control of metal flow is the key to affect forming, strain distribution, and forming load during the FBRE process. On the basis of summarizing the research of the FBRE process, this paper presents the numerical and experimental results of aluminum alloy hub production using this method. Material flow behavior and strain distribution within the final product were investigated by finite element methods. The parameters such as billet size, die corner radius, and friction conditions are optimized and determined to control the material flow in different sections. While the uniformity of extrusion deformation is greatly improved, the extrusion forming load is reduced, and the aluminum alloy hub was manufactured by the FBRE process. The comparison between the theoretical and the experimental results shows good agreement.

Purpose. This is a retrospective research comparing the clinical outcomes of single-hole versus multi-hole video-assisted thoracoscopic surgical (VATS) resection for solitary pulmonary nodules (SPN) and examining the factors influencing the diagnosis of benign and malignant pulmonary nodules. Method. We collected the clinical data, surgical status, outcomes, and corresponding imaging features of 317 patients with SPN who were surgically resected by VATS and diagnosed as benign or malignant by pathology in our hospital from January 2019 to December 2021. Result. Among the 317 patients, 124 (39.12%) underwent single-port VATS and 193 (60.88%) underwent multiple-hole VATS. All patients were grouped according to the different surgical methods, and their postoperative indicators were statistically analyzed. The results showed that neither the single-port VATS group nor the multi-port VATS group had any serious adverse events such as death during the perioperative period. The average operation time, intraoperative blood loss, drainage tube indwelling time, and postoperative hospital stay were significantly lower in the two groups. Statistics of postoperative pathological diagnosis showed that 98 cases (30.91%) of all nodules were benign nodules and 219 cases (69.09%) were malignant nodules, and a further single-multivariate analysis showed that age, nodule maximum diameter, lobular sign, burr sign, vascular cluster sign, and pleural depression sign were independent relevant factors for the diagnosis of benign and malignant nodules. Conclusion. VATS is less invasive and has fewer complications and is of great clinical value for both diagnosis and treatment of benign and malignant SPN. Age, maximum nodal diameter, lobar sign, burr sign, vascular set sign, and pleural depression sign were independent correlates affecting the diagnosis of benign and malignant SPN, which reminds that great attention should be paid to patients who are older and have risk factors on imaging, and early and timely active treatment or close follow-up should be carried out.

Space unmanned systems with modular and reconfigurable characteristics show a distributed spatial information processing scenario, which needs a new intelligent information processing architecture and task scheduling method, so as to make better use of on orbit resources, adapt to reconfigurable spatial computing and provide spatial reliability computing. we propose a dynamic computing resource and task allocation algorithm for modular reconfigurable space systems, by introducing Docker container technology to minimize completion time cost of a task for given resource constraints considering network traffic and variable workloads in a unified framework. The algorithm first answers the problem of how multiple modules perform cooperative and reliable computing in modular reconfigurable space systems, and gives a method to jointly optimize task allocating strategy and computing resource scheduling through online prediction and linear regression optimization in distributed environment. We design and implement the algorithm architecture on the Linux based platform, and perform series of tests on the simulated distributed network environment. The experiment results show that the algorithm has resource utilization and task response gain in various situations (different cluster node capabilities, different task response demands), obtain the cluster resource allocation balance, and minimize the total completion time of tasks.

Background The oncogenic role of the newly identified lncRNA LUADT1 has been revealed in lung adenocarcinoma. It was reported that LUADT1 plays a critical role in multiple human diseases. This study was carried out to investigate the role of LUADT1 in sepsis. Methods Sixty patients with sepsis and sixty healthy volunteers were recruited for this study. Plasma samples were collected from all participants. Human primary coronary artery endothelial cells were also used in this study. The expression of Pim-1, miR-195 and LUADT1 were detected by RT-qPCR. The interaction between miR-195 and LUADT1 was determined by overexpression experiments and luciferase activity assay. Cell apoptosis was detected by flow cytometry. The expression of apoptosis-related protein was detected by Western blotting. Results Bioinformatics analysis revealed the potential interaction between LUADT1 and miR-195, which was confirmed by dual luciferase reporter assay. LUADT1 was downregulated in patients with sepsis. Moreover, LPS treatment downregulated the expression of LUADT1 in primary cardiac endothelial cells. Overexpression of LUADT1 and miR-195 did not affect the expression of each other in primary cardiac endothelial cells. Interestingly, overexpression of LUADT1 was found to upregulate the expression of Pim-1, a target of miR-195. In addition, it was found that overexpression of LUADT1 and Pim-1 reduced the enhancement effects of miR-195 on LPS-induced cardiac endothelial cell apoptosis. Conclusion In summary, LUADT1 may protect cardiac endothelial cells against apoptosis in sepsis by regulating the miR-195/Pim-1 axis.

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has become a public health emergency of international concern. SARS-CoV-2 RNA detection is the diagnostic criterion for coronavirus disease 2019 (COVID-19). Nevertheless, RNA detection has many limitations, such as being time-consuming and cost-prohibitive, and it must be performed in specialized laboratories. Virus antibody detection is a routine method for screening for multiple viruses, but data about SARS-CoV-2 antibody detection are limited. Method Throat swabs and blood were collected from 67 suspected SARS-CoV-2 infection patients at the Affiliated Hospital of Zunyi Medical University and Zunyi Fourth People’s Hospital isolated observation departments. Throat swab samples were subjected to SARS-CoV-2 RNA detection by real-time PCR. Blood was used subjected to SARS-CoV-2 IgG/IgM detection by an enzyme-linked immunosorbent assay (ELISA) and gold immunochromatography assay (GICA). Blood underwent C-reactive protein detection by immunoturbidimetry, and white blood cells, neutrophil percentages and lymphocyte percentages were counted and calculated, respectively. Clinical symptoms, age and lifestyle habits (smoking and drinking) in all patients were recorded. Data were analysed using SPSS version 19. The results were confirmed by T and χ2 tests; correlations with detection results were analysed by kappa coefficients. Odds ratio (OR) and corrected OR values were analysed by logistic regression. P Results Of the 67 patients included in this study, 26 were SARS-CoV-2 RNA-positive. GICA IgM sensitivity was 50.9% (13/26), and specificity was 90.2% (37/41). ELISA IgM sensitivity was 76.9% (20/26), and specificity was 90.2% (37/41). ELISA IgG sensitivity was 76.9% (20/26), and specificity was 95.1% (39/41). The kappa coefficients between RNA detection and ELISA IgG, ELISA IgM, and GICA IgM results were 0.741 (P P < 0.01) and 0.430 (P < 0.01), respectively. Conclusion Among the candidate blood indicators, serum IgG and IgM detected by ELISA had the best consistency and validity when compared with standard RNA detection; these indicators can be used as potential preliminary screening tools to identify those who should undergo nucleic acid detection in laboratories without RNA detection abilities or as a supplement to RNA detection.

The superconducting rapid single flux quantum (RSFQ) logic circuit has the characteristics of high speed and low power consumption, making it an attractive candidate for future supercomputers. However, computer-aided design (CAD) tools for CMOS cannot be directly applied to RSFQ logic due to their distinct properties. For instance, the RSFQ logic gate can work properly when all its fan-ins have the same logic level. This paper presents the design flow from RTL to RSFQ logic netlist and schematic. First, we implement logic synthesis for RSFQ logic circuits. It achieves path balancing while minimizing the number of DFFs. In addition, we propose an automatic schematic generator for the RSFQ logic circuits. It converts the synthesized netlist into its equivalent schematic. A layer assignment algorithm is proposed, which makes all gates layered in the order of the clock arrival time. Experimental results with ISCAS85 and EPFL benchmarks along with some Kogge-Stone adders have shown a 29.2% reduction in the number of DFFs over the breadth-first first search; moreover, 59.57% and 5.3% decrease in the number of layers of the schematic and number of edge crossings over the ELK tool.

The rise of cloud computing and cloud storage brings huge benefits to the information industry, but at the same time, related applications also face greater information security challenges. Therefore, this paper proposes a risk assessment method of cloud system information security based on risk contagion, which mainly evaluates the risk value of the whole system caused by a module being attacked as a risk source. In this method, the calculation of asset value is improved, and the permissions relationship is introduced into the calculation of risk contagion coefficient.

Background Fibrosis is an integral component of the pathogenesis of acute lung injury and is associated with poor outcomes in patients with acute respiratory distress syndrome (ARDS). Fibrocytes are bone marrow-derived cells that traffic to injured tissues and contribute to fibrosis; hence their concentration in the peripheral blood has the potential to serve as a biomarker of lung fibrogenesis. We therefore sought to test the hypothesis that the concentration and phenotype of circulating fibrocytes in patients with ARDS predicts clinical outcomes. Methods For the animal studies, C57Bl/6 mice were infected with experimental Klebsiella pneumoniae in a model of acute lung injury; one-way ANOVA was used to compare multiple groups and two-way ANOVA was used to compare two groups over time. For the human study, 42 subjects with ARDS and 12 subjects with pneumonia (without ARDS) were compared to healthy controls. Chi-squared or Fisher’s exact test were used to compare binary outcomes. Survival data was expressed using a Kaplan-Meier curve and compared by log-rank test. Univariable and multivariable logistic regression were used to predict death. Results In mice with acute lung injury caused by Klebsiella pneumonia, there was a time-dependent increase in lung soluble collagen that correlated with sequential expansion of fibrocytes in the bone marrow, blood, and then lung compartments. Correspondingly, when compared via cross-sectional analysis, the initial concentration of blood fibrocytes was elevated in human subjects with ARDS or pneumonia as compared to healthy controls. In addition, fibrocytes from subjects with ARDS displayed an activated phenotype and on serial measurements, exhibited intermittent episodes of markedly elevated concentration over a median of 1 week. A peak concentration of circulating fibrocytes above a threshold of > 4.8 × 106 cells/mL cells correlated with mortality that was independent of age, ratio of arterial oxygen concentration to the fraction of inspired oxygen, and vasopressor requirement. Conclusions Circulating fibrocytes increase in a murine model of acute lung injury and elevation in the number of these cells above a certain threshold is correlated with mortality in human ARDS. Therefore, these cells may provide a useful and easily measured biomarker to predict outcomes in these patients.

Resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) has become the main clinical challenge of advanced lung cancer. This research aimed to explore the role of PARP1-mediated autophagy in the progression of TKI therapy. PARP1-mediated autophagy was evaluated in vitro by CCK-8 assay, clonogenic assay, immunofluorescence, and western blot in the HCC-827, H1975, and H1299 cells treated with icotinib (Ico), rapamycin, and AZD2281 (olaparib) alone or in combination. Our results and GEO dataset analysis confirmed that PARP1 is expressed at lower levels in TKI-sensitive cells than in TKI-resistant cells. Low PARP1 expression and high p62 expression were associated with good outcomes among patients with NSCLC after TKI therapy. AZD2281 and a lysosomal inhibitor reversed resistance to Ico by decreasing PARP1 and LC3 in cells, but an mTOR inhibitor did not decrease Ico resistance. The combination of AZD2281 and Ico exerted a markedly enhanced antitumor effect by reducing PARP1 expression and autophagy in vivo. Knockdown of PARP1 expression reversed the resistance to TKI by the mTOR/Akt/autophagy pathway in HCC-827IR, H1975, and H1299 cells. PARP1-mediated autophagy is a key pathway for TKI resistance in NSCLC cells that participates in the resistance to TKIs. Olaparib may serve as a novel method to overcome the resistance to TKIs.