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Non-small cell lung cancer (NSCLC) is the predominant form of lung cancer and is one of the most fatal cancers worldwide. Recently, the International Association for the Study of Lung Cancer (IASLC) proposed a novel grading system based on the predominant and high-grade histological patterns for invasive pulmonary adenocarcinoma (IPA). To improve outcomes for NSCLC patients, we combined serum metabolomics and fecal microbiology to screen biomarkers in patients with early-stage NSCLC and identified characteristic microbial profiles in patients with different grades of IPA. 26 genera and 123 metabolites were significantly altered in the early-stage NSCLC patients. Agathobacter, Blautia, Clostridium, and Muribaculacea were more abundant in the early-stage NSCLC patients compared with healthy controls. For the different grades of IPA, the characteristic microorganisms are as follows: Blautia and Marinobacter in IPA grade type 1; Dorea in IPA grade type 2; and Agathobacter in IPA grade type 3. In the metabolome results, the early-stage NSCLC group mainly included higher levels of sphingolipids (D-erythro-sphingosine 1-phosphate, palmitoyl sphingomyelin), fatty acyl (Avocadyne 1-acetate, 12(S)-HETE, 20-Carboxy-Leukotriene B4, Thromboxane B3, 6-Keto-prostaglandin f1alpha, Sebacic acid, Tetradecanedioic acid) and glycerophospholipids (LPC 20:2, LPC 18:0, LPC 18:4, LPE 20:2, LPC 20:1, LPC 16:1, LPC 20:0, LPA 18:2, LPC 17:1, LPC 17:2, LPC 19:0). Dysregulation of pathways, such as sphingolipid metabolism and sphingolipid signaling pathway may become an emerging therapeutic strategy for early-NSCLC. Correlation analysis showed that gut microbiota and serum metabolic profiles were closely related, while Muribaculacea and Clostridium were the core genera. These findings provide new biomarkers for the diagnosis of early-stage NSCLC and the precise grading assessment of prognostic-related IPAs, which are of clinical importance and warrant further investigation of the underlying molecular mechanisms.

As a kind of biomass resource, waste office paper can be used as a carbon precursor to prepare carbon materials. In this work, carbon microspheres with regular shape, uniform particle size and high carbon content were successfully prepared from waste office paper via a hydrothermal synthesis method with sulfuric acid as the catalyst. The effects of reaction temperature and sulfuric acid dosage on the morphology of the carbon microspheres were studied. The formation mechanism of the carbon microspheres was investigated by analyzing the structure and composition of the products. The results show that the hydrolysis of cellulose in waste paper under hydrothermal conditions was the key for the formation of carbon microspheres. The temperature of hydrothermal reaction and the use of sulfuric acid can affect the morphology of carbon microspheres. The carbon microspheres synthesized at 210 °C with 10 mL sulfuric acid have the best surface morphology, with uniform particle size and higher dispersion. Cyclic voltammetry and electrochemical impedance spectroscopy show that the carbon microspheres have good capacitance performance and can be used in capacitors. This study provides a low-cost precursor for carbon microspheres as well as a new method for the recycle of waste paper.

Human induced pluripotent stem cells (iPSC) resemble human embryonic stem cells with potential to differentiate into cells of all adult tissues. Nonetheless human iPSCs may have an epigenetic memory of their donor tissue origin, are easier to differentiate to those lineages, and their potential to other cell fates can be controlled. We generated a human iPSC line CIBi011-A from amniocytes of a healthy fetus. CIBi011-A serves as a useful source to investigate the epigenetic memory of iPSCs. As an iPSC line from a healthy donor, this line can also serve as a potential cell source from which to develop stem cell-based cell therapies.

Large-scale facial expression datasets are primarily composed of real-world facial expressions. Expression occlusion and large-angle faces are two important problems affecting the accuracy of expression recognition. Moreover, because facial expression data in natural scenes commonly follow a long-tailed distribution, trained models tend to recognize the majority classes while recognizing the minority classes with low accuracies. To improve the robustness and accuracy of expression recognition networks in an uncontrolled environment, this paper proposes an efficient network structure based on an attention mechanism that fuses global and local features (AM-FGL). We use a channel spatial model and local feature convolutional neural networks to perceive the global and local features of the human face, respectively. Because the distribution of real-world scene field expression datasets commonly follows a long-tail distribution, where neutral and happy expressions account for the tail expressions, a trained model exhibits low recognition accuracy for tail expressions such as fear and disgust. CutMix is a novel data enhancement method proposed in other fields; thus, based on the CutMix concept, a simple and effective data-balancing method is proposed (BC-EDB). The key idea is to paste key pixels (around eyes, mouths, and noses), which reduces the influence of overfitting. Our proposed method is more focused on the recognition of tail expression, occluded expression, and large-angle faces, and we achieved the most advanced results in occlusion-RAF-DB, 30∘ pose-RAF-DB, and 45∘ pose-RAF-DB with accuracies of 86.96%, 89.74%, and 88.53%.

Abstract Epigenome‐wide association study (EWAS) has been applied to analyze DNA methylation variation in complex diseases for a decade, and epigenome as a research target has gradually become a hot topic of current studies. The DNA methylation microarrays, next‐generation, and third‐generation sequencing technologies have prepared a high‐quality platform for EWAS. Here, the progress of EWAS research is reviewed, its contributions to clinical applications, and mainly describe the achievements of four typical diseases. Finally, the challenges encountered by EWAS and make bold predictions for its future development are presented.

Aqueous film-forming foam, used in firefighting, and biowastes, including biosolids, animal and poultry manures, and composts, provide a major source of poly- and perfluoroalkyl substances (PFAS) input to soil. Large amounts of biowastes are added to soil as a source of nutrients and carbon. They also are added as soil amendments to improve soil health and crop productivity. Plant uptake of PFAS through soil application of biowastes is a pathway for animal and human exposure to PFAS. The complexity of PFAS mixtures, and their chemical and thermal stability, make remediation of PFAS in both solid and aqueous matrices challenging. Remediation of PFAS in biowastes, as well as soils treated with these biowastes, can be achieved through preventing and decreasing the concentration of PFAS in biowaste sources (i.e., prevention through source control), mobilization of PFAS in contaminated soil and subsequent removal through leaching (i.e., soil washing) and plant uptake (i.e., phytoremediation), sorption of PFAS, thereby decreasing their mobility and bioavailability (i.e., immobilization), and complete removal through thermal and chemical oxidation (i.e., destruction). In this review, the distribution, bioavailability, and remediation of PFAS in soil receiving solid biowastes, which include biosolids, composts, and manure, are presented.

Abstract Recent researches show that cross technology interference management plays an important role in the emergence of innovative applications for nowadays wireless networks. Existing algorithms either suffer from the constraint on the need of clean preamble or require technological similarities among cross technology systems. In this paper, we ask the question: “whether the packet with interfered preamble can be decoded?" To answer this question, we investigate the property of preamble and propose Narthil, an innovative coexistence algorithm which based on imperfect interference management, handling packet detection, symbol synchronization, and channel estimation with interfered preambles. Narthil applies a simple Butterworth band-stop filter for imperfect interference filtering, and leverages the residual signals for packet detection and symbol synchronization, as well as channel estimation. The insight is the inherent properties of preamble such as periodicity and modulation scheme, and the frequency continuity in OFDM signal could be effectively leveraged for important processes such as packet detection, symbol synchronization, and channel estimation. These favorable properties could be used to further enhance the performance of other cross technology signal coexistence algorithms. We implement Narthil algorithm on our USRP/GNU Radio platform, and evaluate its performance by using 15 USRP-N210 devices through the real deployment experiments. The results demonstrate that Narthil can effectively detect the incoming packet and achieves a high accuracy symbol synchronization, as well as channel estimation of the interfered preamble, and also achieve a good performance on packet decoding.

Variations in PRKN or HTRA2 are associated with Parkinson’s disease. We generated a human induced pluripotent stem cell (iPSC) line CIBi007-A from a patient with young-onset Parkinson’s disease (YOPD) who carried variants in PRKN and HTRA2. The generated iPSCs resembled human embryonic stem cells, expressed pluripotency markers, exhibited a normal karyotype, and could be differentiated into three germ layers in vitro. This line will be valuable for investigating disease mechanisms of YOPD and screening candidate drugs.

The widely deployed WiFi APs and ZigBee devices make the coexistence for these two networks more pervasive than ever. Previous solutions focus on the contention resolution in time domain, but would lead to the underutilized frequency resources. However, for frequency domain schemes, to enable efficient coexistence for WiFi and ZigBee networks, the WiFi nodes or ZigBee nodes have to select the appropriate channel for transmission. Such scheme will need strict cooperations among these two independent systems, and could not work with the conventional legacy systems seamlessly. In this paper, we propose a frequency overlay approach named COFFEE (COexist wiFi For zigbEE networks), where subcarriers interfering with ZigBee transmissions are nullified by WiFi. According to our basic evaluations, due to the relatively robust feature in WiFi/OFDM design, nullifying small portion of subcarriers would affect the WiFi performance slightly, but could improve the capability for coexistence significantly. Thus by using COFFEE, WiFi and ZigBee nodes can transmit their packets concurrently without any coordination. Also, COFFEE and conventional legacy WiFi systems could work together seamlessly. Furthermore, we implement COFFEE with USRP software radio platform and evaluate the performance under real wireless network scenarios. The extensive evaluations show that compared to the coexistence schemes in time domain, COFFEE is able to increase the network throughput by more than 300%. Even when the ZigBee node never knows the transmission time of WiFi packet, it still can provide concurrent lossless transmission by using COFFEE with only 10% to 15% WiFi throughput reduction.

Parametric decay instability (PDI) generated in milliseconds is an important physical phenomenon in ionospheric heating. Usually, numerical simulations are used to study PDI mechanisms. They can intuitively investigate the generation and development process of PDI, which is necessary in experimental studies. When simulating the PDI phenomenon through the explicit finite-difference time-domain (FDTD), the spatial scale spans from kilometers to centimeters, and the time scale needs to meet the Courant–Friedrichs–Lewy condition. Simulating the PDI phenomenon is time-consuming and difficult due to the high spatial resolution and strict restriction on the discrete time step. Although a nested mesh technique can boost the computational efficiency, the application of a parallel strategy is imperative to further improve it. In this study, we present a hybrid Message Passing Interface (MPI)/OpenMP parallelization scheme to solve the above-mentioned problems. This scheme can achieve an adaptive calculation and automatic allocation of MPI tasks and OpenMP threads, proving its flexibility and portability. Under the EISCAT background parameters, the PDI phenomenon was simulated. The results of the wave mode conversion and intense localized turbulence were identical to those of the serial program. Furthermore, a new simulation example and the effect of the cavity depth on electrostatic waves and negative ion density cavity were investigated. By utilizing the proposed parallelization scheme, the simulation time can be reduced from 70 h for the serial program to 3.6 h.

Ground-based high-frequency modulated waves can periodically heat the ionosphere and create “virtual antennas”, which can radiate extremely low frequency (ELF, 0.3–3 kHz) or very low frequency (VLF, 3–30 kHz) waves for long-distance communication. Ionospheric X-mode and O-mode heating experiments using amplitude and beat-wave (BW) modulations were conducted on 21 November 2019. Experimental results were analyzed from multiple perspectives based on data from Dynasonde, a magnetometer, stimulated electromagnetic emissions, an ELF/VLF signal receiver, and ultra-high-frequency radar. The strongest excited ELF/VLF signals in previous BW modulation heating experiments were around 8–12 kHz; however, in this experiment, no signal excited in this frequency range was observed, and the signal with the highest signal/noise ratio was at the frequency of 3517 Hz, which will aid in understanding the best communication frequency under different ionospheric backgrounds. It is well-accepted that the electron temperature changes periodically with the modulation frequency. However, we noted that the electron temperature had insufficient cooling during the O-mode modulated heating process and then increased again, resulting in a continuous electron temperature increase. We found that this was related to the change in ion composition after analyzing ion-line spectra, which will be helpful in studying the effect of modulation heating on the ionosphere background.

In this paper, we develop a wireless data gathering model for a multi-drone system in traditional wireless sensor networks (WSNs), where each drone serves as a data collector in the extremely large and densely deployed area. Previous solutions usually consider how to schedule the traveling route among nodes, but fail to optimize the data transmission time. We focus on extending the data collection issue over Shannon model which involves the factors such as the transmission bandwidth and the SNR between a drone and a sensor node. A novel and typical system model is formulated and we investigate the corresponding Route Selection and Communication Association (RSCA) problem, that is, given a set of candidate flight routes and a fixed number of deployed nodes, we determine which routes should be selected for traveling and which nodes should be associated such that the overall energy consumption for data gathering could be minimized. We prove the RSCA problem is NP-hard by reduction from the Vertex Cover problem and then devise an efficient and accessible O(log log n) approximation algorithm within the time complexity bound by O(p2 log n log log n), where n is the number of sensor nodes and p is the number of routes in WSNs. Extensive simulations are carried out to investigate the performance of our designed algorithm by comparing with the brute-force and random methods. The proposed algorithm achieves 54% more energy consumption at most and 45% more on average comparing with the optimal solution. Furthermore, real-world trace-driven evaluations have been conducted to show that our obtained solution would hold 34% more at most and 25% more on average comparing with the optimal energy consumption and eventually validate our algorithm.

Wireless energy transfer technologies have played an important role in the development of Internet of Things. Most of the previous studies focus on scheduling mobile chargers efficiently for rechargeable sensor nodes. In this paper, we investigate the deployment problem for wireless charging stations (WCSs) in urban areas with respect to the users detouring cost when they move to the candidate WCSs. With pre-known user's trajectories and given number of WCSs, we deploy the WCSs to maximize the number of recharged users with guaranteed probability. We convert our deployment problem into an NP-hard weighted maximum coverage problem, and prove the objective function is a maximum submodular set function. To this end, a simple but efficient greedy algorithm with approximation factor of (1 - 1/ε) is proposed for the threshold detouring mode. In addition, an improved algorithm with an approximation factor of (1 - 1/√e) is presented for the linear/nonlinear detouring mode. Finally, we evaluate the performance of our algorithms by comparing them with two typical heuristic algorithms (flow-centric and random-based), and the impacts of different detouring thresholds on our algorithms by synthetic traces. Moreover, real trace-driven evaluations validate that our algorithm improves the coverage quality by 75% when compared to the two aforementioned algorithms.

Wireless energy transfer technologies have attracted increasing attention on empowering the wireless sensor nodes in recent years. In this paper, we consider a typical wireless rechargeable network, where a mobile charging vehicle is scheduled to charge a wireless sensor network with practical nodes' deployment restrictions that may result in low charging efficiency for sensor nodes by charging vehicle. In our model, we take the effects of charging distance and angle on charging efficiency into consideration. Intuitively, there is an inevitable tradeoff between the charging distance and the angle. First of all, the scheduling traveling path of charging vehicle in previous studies has been proved to be NP-hard. Even worse, the nonlinear property between the charging distance and angle makes the problem even harder. For these concerns, we aim at minimizing the recharging cycle time, which contains the traveling time and recharging time. To this end, we prove that the charging vehicle would travel along the shortest Hamiltonian cycle. And we show the optimal charging location for each wireless charging incident. Experimental results demonstrate that our proposed solution could improve the charging efficiency around two times compared with the baseline scheme without optimization for angle and distance.

Prussian blue and its analogues (PBA) based nanomaterials have been widely applied to removing pollutants in the recent years. However, easy aggregation and poor recycling largely limit their practical applications. In this work, spiderweb-like Fe-Co Prussian blue analogue/polyacrylonitrile (FCPBA/PAN) nanofibers were prepared by electrospinning and applied to degrading bisphenol-A (BPA) by activating peroxymonosulfate (PMS). Detailed characterization demonstrated that a high loading of FCPBA (86% of FCPBA in FCPBA/PAN) was successfully fixed on the PAN nanofibers. 67% of BPA was removed within 240 min when 500 mg·L−1 PMS and 233 mg·L−1 FCPBA/PAN were introduced in 20 mg·L−1 BPA solution at initial pH of 2.8. Electron paramagnetic resonance (EPR) and radical inhibition experiments were performed to identify the possible degradation mechanism. For comparison, a low loading of FCPBA nanofibers (0.6FCPBA/PAN nanofibers, 43% of FCPBA in FCPBA/PAN) were also prepared and tested the catalytic performance. The results showed that the activity of FCPBA/PAN was much higher than 0.6FCPBA/PAN. Furthermore, a FCPBA/PAN packed column was made as a reactor to demonstrate the reusability and stability of FCPBA/PAN nanofibers, which also exhibited the bright future for the industrial application. This work makes it possible to fabricate efficient PBA nanocatalysts with excellent recyclability and promotes the application of PBA in industrial areas.

Background and objective It has been proved that cyclooxygenase-2 (COX-2) is a key factor in lung cancer oncogenesis. COX-2 can be induced by a number of cytokines and growth factors and can be regulated by the JAK/STAT signaling pathway. Inhibiting the expression of COX-2 can prevent the development of lung cancer. The aim fo this study is to investigate whether the epidermal growth factor (EGF) can stimulate the signal transducers and activators of transcription 5 (STAT5) as well as to discover the effects of the STAT5 signaling pathway on the COX-2 in human lung adenocarcinoma A549 cells. Methods The phenomenon of STAT5 activation stimulated by the EGF was assayed through immunofluorescence and Western blot. The adenovirus containing the wild-type (WT)-STAT5 (AdWT-STAT5) plasmid, dominant-negative (DN)-STAT5 (Ad-CMV5Stat5aΔ740) plasmid, and STAT5 siRNA were transfected into A549 cells. The latter two groups were stimulated using EGF. Reverse transcriptase polymerase chain reaction was used to detect the mRNA expression of COX-2. Results STAT5 was not activated in A549 cells in vitro. EGF stimulation significantly increased the level of the p-STAT5 protein and induces the shuttling of p-STAT5 from the cytoplasm into the nucleus. STAT5 activation was crucial for the COX-2 expression induced by the EGF. STAT5 was required for COX-2 expression, but can mediated the effects of the COX-2 expression through pathways that were independent of transcriptional activation. Conclusion COX-2 expression is dependent on STAT5 phosphorylation. A second pathway does not require STAT5 phosphorylation.

Unfortunately, we had mixed up the institution and address of author “Qiongzheng Lin” with others. The correct institution and address of author “Qiongzheng Lin” are given here.