Update on the association of miR-149 rs2292832 C>T polymorphism with gastric cancer risk: A meta-analysis study of gastrointestinal cancers.

OBJECTIVE: Single nucleotide polymorphisms in microRNAs are believed to affect the occurrence and progression of cancer by altering the expression and biological functions of microRNAs. Several studies investigated the role of the miR-149 rs2292832 C>T polymorphism on the risk of gastric cancer (GC), but got conflicting results. METHODS: We performed a comprehensive and systematic search through the PubMed MEDLINE, Google Scholar, Science Direct, Scopus, CNKI, and Web of science, 8 studies were included in the meta-analysis to determine whether miR-149 rs2292832 C>T polymorphism contributed to the risk of GC. RESULTS: Pooled data indicated that miR-149 rs2292832 C>T polymorphism was not associated with GC risk. In the stratified analysis by ethnicity, miR-149 rs2292832 C>T polymorphism significantly increased GC risk under the allele comparison model (odds ratio [OR] = 1.27, 95% CI = 1.04-1.55, Pheterogeneity = 0.18, P = .02), recessive model (OR = 1.44, 95% CI = 1.04-2.01, Pheterogeneity = 0.19, P = .03) among Caucasians; but decreased GC risk under the allele comparison model (OR = 0.89, 95% CI = 0.81-0.98, Pheterogeneity = 0.22, P = .02) and dominant model (OR = 0.82, 95% CI = 0.72-0.93, Pheterogeneity = 0.15, P = .01) among Asian. CONCLUSION: Our meta-analysis suggests a positive correlation between miR-149 rs2292832 C>T polymorphism and GC development among Caucasians, but negative correlation among Asian population.

Anomaly traffic detection based on feature fluctuation for secure industrial internet of things.

The detection of anomaly traffic in internet of things (IoT) is mainly based on the original binary data at the traffic packet level and the structured data at the session flow level. This kind of dataset has a single feature extraction method and relies on prior manual knowledge. It is easy to lose critical information during data processing, which reduces the validity and robustness of the dataset. In this paper, we first construct a new anomaly traffic dataset based on the traffic packet and session flow data in the Iot-23 dataset. Second, we propose a feature extraction method based on feature fluctuation. Our proposed method can effectively solve the disadvantage that the data collected in different scenarios have different characteristics, which leads to the feature containing less information. Compared with the traditional anomaly traffic detection model, experiments show that our proposed method based on feature fluctuation has stronger robustness, can improve the accuracy of anomaly traffic detection and the generalization ability of the traditional model, and is more conducive to the detection of anomalous traffic in IoT.

Roles of Pyroptosis-Related Genes in the Diagnosis and Subtype Classification of Periodontitis.

Pyroptosis is widely involved in many diseases, including periodontitis. Nonetheless, the functions of pyroptosis-related genes (PRGs) in periodontitis are still not fully elucidated. Therefore, we aimed to investigate the role of PRGs in periodontitis. Three datasets (GSE10334, GSE16134, and GSE173078) from the Gene Expression Omnibus (GEO) were selected to analyze the differences in expression values of the PRGs between nonperiodontitis and periodontitis tissue samples using difference analysis. Following this, five hub PRGs (charged multivesicular body protein 2B, granzyme B, Z-DNA-binding protein 1, interleukin-1ß, and interferon regulatory factor 1) predicting periodontitis susceptibility were screened by establishing a random forest model, and a predictive nomogram model was constructed on the basis of these genes. Decision curve analysis suggested that the PRG-based predictive nomogram model could provide clinical benefits to patients. Three distinct PRG patterns (cluster A, cluster B, and cluster C) in the periodontitis samples were revealed according to the 48 significant PRGs, and the difference in the immune cell infiltration among the three patterns was explored. We observed that all infiltrating immune cells, except type 2 T helper cells, differ significantly among the three patterns. To quantify the PRG patterns, the PRG score was calculated by principal component analysis. According to the results, cluster B had the highest PRG score, followed by cluster A and cluster C. In conclusion, PRGs significantly contribute to the development of periodontitis. Our study of PRG patterns might open up a new avenue to guide individualized treatment plans for patients with periodontitis.

Temporal and spectral multiplexing for EUV multibeam ptychography with a high harmonic light source.

We demonstrate temporally multiplexed multibeam ptychography implemented for the first time in the EUV, by using a high harmonic based light source. This allows for simultaneous imaging of different sample areas, or of the same area at different times or incidence angles. Furthermore, we show that this technique is compatible with wavelength multiplexing for multibeam spectroscopic imaging, taking full advantage of the temporal and spectral characteristics of high harmonic light sources. This technique enables increased data throughput using a simple experimental implementation and with high photon efficiency.

Single-Frame Characterization of Ultrafast Pulses with Spatiotemporal Orbital Angular Momentum.

Light that carries spatiotemporal orbital angular momentum (ST-OAM) makes possible new types of optical vortices arising from transverse OAM. ST-OAM pulses exhibit novel properties during propagation, transmission, refraction, diffraction, and nonlinear conversion, attracting growing experimental and theoretical interest and studies. However, one major challenge is the lack of a simple and straightforward method for characterizing ultrafast ST-OAM pulses. Using spatially resolved spectral interferometry, we demonstrate a simple, stationary, single-frame method to quantitatively characterize ultrashort light pulses carrying ST-OAM. Using our method, the presence of an ST-OAM pulse, including its main characteristics such as topological charge numbers and OAM helicity, can be identified easily from the unique and unambiguous features directly seen on the raw data-without any need for a full analysis of the data. After processing and reconstructions, other exquisite features, including pulse dispersion and beam divergence, can also be fully characterized. Our fast characterization method allows high-throughput and quick feedback during the generation and optical alignment processes of ST-OAM pulses. It is straightforward to extend our method to single-shot measurement by using a high-speed camera that matches the pulse repetition rate. This new method can help advance the field of spatially and temporally structured light and its applications in advanced metrologies.

MTRR rs1532268 polymorphism and gastric cancer risk: evidence from a meta-analysis.

OBJECTIVE: The methionine synthase reductase (MTRR) gene encodes the MTRR enzyme involved in the metabolic pathway of homocysteine. Several studies investigated the effect of the MTRR rs1532268 gene polymorphism on the risk of gastric cancer (GC), but the results have been inconsistent. METHODS: We performed a comprehensive and systematic search of PubMed, Google Scholar, MEDLINE, Science Direct, Scopus, CNKI, and Web of Science. Five studies were included in this meta-analysis to determine whether MTRR rs1532268 polymorphism contributes to the risk of GC. RESULTS: Pooled data indicated that the MTRR rs1532268 polymorphism significantly increased GC risk under the allele comparison model (odds ratio [OR] = 1.14, 95% confidence interval [CI] = 1.01-1.29) and dominant model (OR = 1.14, 95% CI = 1.00-1.30). In the analysis stratified by ethnicity, no relationship was found in Whites or Asians. CONCLUSION: Our meta-analysis suggests a positive correlation between MTRR rs1532268 polymorphism and GC development.

Therapeutic Interventions of Gut-Brain Axis as Novel Strategies for Treatment of Alcohol Use Disorder Associated Cognitive and Mood Dysfunction.

Alcohol use disorders (AUD) is characterized by persistent or intermittent alcohol cravings and compulsive drinking. The functional changes in the central nervous system (CNS) after alcohol consumption are alcohol-associated cognitive impairment and mood disorders, which are major health issues reported in AUDs. Studies have shown that transferring the intestinal microbiota from AUDs patients to germ-free animals causes learning and memory dysfunction, depression and anxiety-like behavior, indicating the vital role of intestinal microbiota in development of neuropsychiatric disorders in AUD. Intestinal flora composition of AUD patients are significantly different from normal people, suggesting that intestinal flora imbalance orchestrate the development of neuropsychiatric disorders in AUD. Studies suggests that gut microbiome links bidirectional signaling network of the enteric nervous system (ENS) to central nervous system (CNS), forming gut-microbe-brain axis (brain-gut axis). In this review, we discussed pathogenesis and possible treatment of AUD-induced cognitive deficits, anxiety, and depression disorders. Further, we described the mechanism of intestinal flora imbalance and dysfunction of hippocampus-amygdala-frontal cortex (gut-limbic circuit system dysfunction). Therefore, we postulate therapeutic interventions of gut-brain axis as novel strategies for treatment of AUD-induced neuropsychiatric disorders.

A Survey of Blind Modulation Classification Techniques for OFDM Signals.

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed.

Recirculation of reject water in deep-dewatering process to influent of wastewater treatment plant and dewaterability of sludge conditioned with Fe2+/H2O2, Fe2+/Ca(ClO)2, and Fe2+/Na2S2O8: From bench to pilot-scale study.

Deep dewatering of sewage sludge pretreated with advanced oxidation processes (AOPs) is a strategy for efficient sludge reduction and subsequent disposal. The pretreatment and dewatering performance of sludge conditioned with three types of AOPs (Fe2+/H2O2, Fe2+/Ca(ClO)2, and Fe2+/Na2S2O8), compared with sludge conditioned with traditional conditioner (Fe3+/CaO), were investigated in both bench and pilot-scale tests. All of those conditioner systems could reduce the water content of dewatered sludge cake to below 60 wt% in bench-scale (about 16 kg raw sludge per round) and pilot-scale (approximate 800 kg raw sludge per round) diaphragm filter press dewatering. Compared with raw sludge, the deep-dewatering filtrate after different conditioning and dewatering processes had higher ammonia nitrogen (NH4+-N) and chemical oxygen demand (COD) contents due to the degradation of organic matter, and much lower total phosphorus (TP) content due to the formation of iron phosphate precipitate. A better biodegradability (i.e. higher BOD5/COD ratio) was found in the deep-dewatering filtrate of sludge conditioned with Fe2+/H2O2 (25.2 %) and Fe2+/Ca(ClO)2 (17.4 %). Most of the heavy metals (Cr, Cu, Ni, and Pb) (>79 wt%) have remained in the dewatered sludge cake, and most of the Cl element (>90 wt%) in the sludge pretreated by Fe2+/Ca(ClO)2 and Fe3+/CaO was kept in the filtrate, rather than the dewatered sludge cake. Based on the pilot-scale experimental results, if all the filtrate in the deep-dewatering process returned to the influent of WWTP, the loading ratios of TP, NH4+-N, COD in the four conditioner systems were less than 3 wt%. The above results proved that the AOPs conditioned sludge could achieve deep-dewatering in pilot-scale and the direct recirculation of deep-dewatering filtrate to the influent of wastewater treatment plant was feasible.

Deep learning based automatic diagnosis of first-episode psychosis, bipolar disorder and healthy controls.

Neuroimaging data driven machine learning based predictive modeling and pattern recognition has been attracted strongly attention in biomedical sciences. Machine learning based diagnosis techniques are widely applied in diagnosis of neurological diseases. However, machine learning techniques are difficult to effectively extract deep information in neuroimaging data, resulting in low classification accuracy of mental illnesses. To address this problem, we propose a deep learning based automatic diagnosis first-episode psychosis (FEP), bipolar disorder (BD) and healthy controls (HC) method. Specifically, we design a convolutional neural network (CNN) framework to automatically diagnosis based on structural magnetic functional imaging (sMRI). Our dataset consists of 89 FEP patients, 40 BD patients and 83 HC. A three-way classifier (FEP vs. BD vs. HC) and three binary classifiers (FEP vs. BD, FEP vs. HC, BD vs. HC) are trained based on their gray matter volume images. Experiment results show that the performance of CNN-based method outperforms the classic classifiers both in two and three categories classification task. Our research reveals that abnormal gray matter volume is one of the main characteristics for discriminating FEP, BD and HC.

Measurement and control of optical nonlinearities in dispersive dielectric multilayers.

Dispersive dielectric multilayer mirrors, high-dispersion chirped mirrors in particular, are widely used in modern ultrafast optics to manipulate spectral chirps of ultrashort laser pulses. Dispersive mirrors are routinely designed for dispersion compensation in ultrafast lasers and are assumed to be linear optical components. In this work, we report the experimental characterization of an unexpectedly strong nonlinear response in these chirped mirrors. At modest peak intensities <2 TW/cm2-well below the known laser-induced damage threshold of these dielectric structures-we observed a strong reflectivity decrease, local heating, transient spectral modifications, and time-dependent absorption of the incident pulse. Through computational analysis, we found that the incident laser field can be enhanced by an order of magnitude in the dielectric layers of the structure. The field enhancement leads to a wavelength-dependent nonlinear absorption, that shows no signs of cumulative damage before catastrophic failure. The nonlinear absorption is not a simply two-photon process but instead is likely mediated by defects that facilitate two-photon absorption. To mitigate this issue, we designed and fabricated a dispersive multilayer design that strategically suppresses the field enhancement in the high-index layers, shifting the high-field regions to the larger-bandgap, low-index layers. This strategy significantly increases the maximum peak intensity that the mirror can sustain. However, our finding of an onset of nonlinear absorption even at 'modest' fluence and peak intensity has significant implications for numerous past published experimental works employing dispersive mirrors. Additionally, our results will guide future ultrafast experimental work and ultrafast laser design.

Multimodal x-ray and electron microscopy of the Allende meteorite.

Multimodal microscopy that combines complementary nanoscale imaging techniques is critical for extracting comprehensive chemical, structural, and functional information, particularly for heterogeneous samples. X-ray microscopy can achieve high-resolution imaging of bulk materials with chemical, magnetic, electronic, and bond orientation contrast, while electron microscopy provides atomic-scale spatial resolution with quantitative elemental composition. Here, we combine x-ray ptychography and scanning transmission x-ray spectromicroscopy with three-dimensional energy-dispersive spectroscopy and electron tomography to perform structural and chemical mapping of an Allende meteorite particle with 15-nm spatial resolution. We use textural and quantitative elemental information to infer the mineral composition and discuss potential processes that occurred before or after accretion. We anticipate that correlative x-ray and electron microscopy overcome the limitations of individual imaging modalities and open up a route to future multiscale nondestructive microscopies of complex functional materials and biological systems.

A Convex Constraint Variational Method for Restoring Blurred Images in the Presence of Alpha-Stable Noises.

Blurred image restoration poses a great challenge under the non-Gaussian noise environments in various communication systems. In order to restore images from blur and alpha-stable noise while also preserving their edges, this paper proposes a variational method to restore the blurred images with alpha-stable noises based on the property of the meridian distribution and the total variation (TV). Since the variational model is non-convex, it cannot guarantee a global optimal solution. To overcome this drawback, we also incorporate an additional penalty term into the deblurring and denoising model and propose a strictly convex variational method. Due to the convexity of our model, the primal-dual algorithm is adopted to solve this convex variational problem. Our simulation results validate the proposed method.

Data-Prefetching Scheme Based on Playback Delay and Positioning Satisfaction in Peer-To-Peer Video-On-Demand System.

As one of the most important applications in peer-to-peer (P2P) networks, the video-on-demand (VoD) system freely supports video cassette recorder (VCR) operation for users. However, the users may experience significant playback delay after frequent VCR operations in the VoD system, which will affect the quality of experience (QoE) of the users. Hence, selecting an appropriate data-prefetching strategy to support better VCR operation is an important approach to improve the QoE. This paper proposes a data-prefetching strategy (DSA) to determine the most suitable anchor interval by considering the playback delay and positioning satisfaction. According to the DSA, we use the multiple-attribute decision-making (MADM) theory to model the selection of intervals of prefetching data blocks (i.e., anchor interval) and the technique for ordering preference by similarity to an ideal solution (TOPSIS) algorithm to solve the MADM. The simulation results show that the DSA strategy obtains higher positioning satisfaction than the existing schemes, which is approximately 60% higher than the anchor points, popular parts of video, and user interests (API)-based method. Moreover, with the increase in network bandwidth, the DSA strategy can minimize the playback delay after VCR operation using relative few extra bandwidths.

Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for Lp-Regularization Using the Multiple Sub-Dictionary Representation.

Both L1/2 and L2/3 are two typical non-convex regularizations of Lp (0

Optimal Time Allocation in Backscatter Assisted Wireless Powered Communication Networks.

This paper proposes a wireless powered communication network (WPCN) assisted by backscatter communication (BackCom). This model consists of a power station, an information receiver and multiple users that can work in either BackCom mode or harvest-then-transmit (HTT) mode. The time block is mainly divided into two parts corresponding to the data backscattering and transmission periods, respectively. The users first backscatter data to the information receiver in time division multiple access (TDMA) during the data backscattering period. When one user works in the BackCom mode, the other users harvest energy from the power station. During the data transmission period, two schemes, i.e., non-orthogonal multiple access (NOMA) and TDMA, are considered. To maximize the system throughput, the optimal time allocation policies are obtained. Simulation results demonstrate the superiority of the proposed model.

A Type-2 Block-Component-Decomposition Based 2D AOA Estimation Algorithm for an Electromagnetic Vector Sensor Array.

This paper investigates a two-dimensional angle of arrival (2D AOA) estimation algorithm for the electromagnetic vector sensor (EMVS) array based on Type-2 block component decomposition (BCD) tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of electromagnetic signals to accomplish blind estimation for array parameters with higher resolution. However, existing tensor decomposition methods encounter many restrictions in applications of the EMVS array, such as the strict requirement for uniqueness conditions of decomposition, the inability to handle partially-polarized signals, etc. To solve these problems, this paper investigates tensor modeling for partially-polarized signals of an L-shaped EMVS array. The 2D AOA estimation algorithm based on rank- ( L 1 , L 2 , · ) BCD is developed, and the uniqueness condition of decomposition is analyzed. By means of the estimated steering matrix, the proposed algorithm can automatically achieve angle pair-matching. Numerical experiments demonstrate that the present algorithm has the advantages of both accuracy and robustness of parameter estimation. Even under the conditions of lower SNR, small angular separation and limited snapshots, the proposed algorithm still possesses better performance than subspace methods and the canonical polyadic decomposition (CPD) method.

Optimal Resource Allocation Policies for Multi-User Backscatter Communication Systems.

This paper considers a backscatter communication (BackCom) system including a reader and N tags, where each tag receives excitation signals transmitted by the reader and concurrently backscatters information to the reader in time-division-multiple-access (TDMA) mode. In this system, we aim to maximize the total system goodput by jointly optimizing reader transmission power, time allocation, and reflection ratio for the cases of passive and semi-passive tags. For each case, an optimization problem is formulated, which is non-convex and can be solved by being decomposed into at most N feasible sub-problems based on the priority of allocated reader transmission power. First, for the passive tags case, by solving the convex sub-problems sequentially and comparing their maximum total goodput, we derive the optimal resource allocation policy. Then, for the semi-passive tags case, we find a close-to-optimal solution, since each sub-problem can be reformulated as a biconvex problem, which is solved by a proposed block coordinate descent (BCD)-based optimization algorithm. Finally, simulation results demonstrate the superiority of the proposed resource allocation policies.

RSS-Based Method for Sensor Localization with Unknown Transmit Power and Uncertainty in Path Loss Exponent.

The localization of a sensor in wireless sensor networks (WSNs) has now gained considerable attention. Since the transmit power and path loss exponent (PLE) are two critical parameters in the received signal strength (RSS) localization technique, many RSS-based location methods, considering the case that both the transmit power and PLE are unknown, have been proposed in the literature. However, these methods require a search process, and cannot give a closed-form solution to sensor localization. In this paper, a novel RSS localization method with a closed-form solution based on a two-step weighted least squares estimator is proposed for the case with the unknown transmit power and uncertainty in PLE. Furthermore, the complete performance analysis of the proposed method is given in the paper. Both the theoretical variance and Cramer-Rao lower bound (CRLB) are derived. The relationships between the deterministic CRLB and the proposed stochastic CRLB are presented. The paper also proves that the proposed method can reach the stochastic CRLB.

[Sodium nitrite enhanced the potentials of migration and invasion of human hepatocellular carcinoma SMMC-7721 cells through induction of mitophagy].

Nitrites play multiple characteristic functions in invasion and metastasis of hepatic cancer cells, but the exact mechanism is not yet known. Cancer cells can maintain the malignant characteristics via clearance of excess mitochondria by mitophagy. The purpose of this article was to determine the roles of nitrite, reactive oxygen species (ROS) and hypoxia inducing factor 1 alpha (HIF-1 α) in mitophagy of hepatic cancer cells. After exposure of human hepatocellular carcinoma SMMC-7721 cells to a serial concentrations of sodium nitrite for 24 h under normal oxygen, the maximal cell vitality was increased by 16 mg x (-1) sodium nitrite. In addition, the potentials of migration and invasion for SMMC-7721 cells were increased significantly at the same time. Furthermore, sodium nitrite exposure displayed an increase of stress fibers, lamellipodum and perinuclear mitochondrial distribution by cell staining with Actin-Tracker Green and Mito-Tracker Red, which was reversed by N-acetylcysteine (NAC, a reactive oxygen scavenger). DCFH-DA staining with fluorescent microscopy showed that the intracellular level of ROS concentration was increased by the sodium nitrite treatment. LC3 immunostaining and Western blot results showed that sodium nitrite enhanced cell autophagy flux. Under the transmission electron microscopy (TEM), more autolysosomes formed after sodium nitrite treatment and NAC could prevent autophagosome degradation. RT-PCR results indicated that the expression levels of COX I and COXIV mRNA were decreased significantly after sodium nitrite treatment. Meanwhile, laser scanning confocal microscopy showed that sodium nitrite significantly reduced mitochondrial mass detected by Mito-Tracker Green staining. The expression levels of HIF-1α, Beclin-1 and Bnip3 (mitophagy marker molecular) increased remarkably after sodium nitrite treatment, which were reversed by NAC. Our results demonstrated that sodium nitrite (16 mg x L(-1)) increased the potentials of invasion and migration of hepatic cancer SMMC-7721 cells through induction of ROS and HIF-1α mediated mitophagy.