Search

Synthetic aperture radar (SAR) images are often affected by a type of multiplicative noise known as “speckle” due to their active imaging characteristics. This property complicates the processing and interpretation of SAR images. While deep learning techniques have demonstrated success in despeckling, many models are tailored to specific noise levels. This specificity can limit a model's ability to generalize to real SAR images with varying noise levels, potentially leading to oversmoothing or overfocusing on specific details. To address these challenges, we present the Adaptive Noise Estimation and Despeckling Network (ANED-Net). This network consists of a noise-level estimation phase and a noise-level-guided nonblind denoising phase. During the nonblind denoising phase, we develop a noise-feature-guided denoising network. This network integrates a hierarchical encoder–decoder denoising module based on the Transformer block (T-unet) and a denoising enhancement control block. Together, they skillfully capture both local and global dependencies inherent in SAR images, facilitating effective noise removal. Furthermore, we also introduce a deep-attention mechanism to counteract the attentional collapse observed when the Transformer is extended in depth, enhancing the network's feature extraction capability and strengthening the model's denoising performance. Extensive tests on synthetic and real images show that ANED-Net is robust to different noise scenarios. It effectively mitigates speckle noise even at unspecified levels and outperforms many established methods.

Abstract The bus passenger detection algorithm is a key component of a public transportation bus management system. The detection techniques based on the convolutional neural network have been widely used in bus passenger detection. However, they require high memory and computational requirements, which hinder the deployment of bus passenger detectors in the bus system. In this paper, a lightweight bus passenger detection model based on YOLOv5 is introduced. To make the model more lightweight, the inner and outer cross‐stage bottleneck modules, called ICB and OCB, respectively, are proposed. The proposed module reduces the quantity of parameter and floating point operations and increases the detection speed. In addition, the neighbour feature attention pooling is adopted to improve detection accuracy. The performance of the lightweight model on the bus passenger dataset is empirically demonstrated. The experiment results demonstrate that the proposed model is lightweight and efficient. Compared lightweight YOLOv5n with the original algorithm, the model weight is reduced by 31% to 2.6M, and the detection speed is increased by 6% to 40FPS without an accuracy drop.

Real-world datasets often follow a long-tailed distribution, where a few majority (head) classes contain a large number of samples, while many minority (tail) classes contain significantly fewer samples. This imbalance creates an information disparity between head and tail classes, which can negatively impact the performance of deep networks. Some transfer knowledge techniques attempt to mitigate this gap by generating additional minority samples, either through oversampling the tail classes or transferring knowledge from the head classes to the tail classes. However, these methods often restrict the diversity of the generated minority samples, primarily focusing on transferring information only to the tail classes. This paper introduces a simple yet effective method for long-tailed classification, called mixed mutual transfer (MMT), which facilitates the mutual transfer of knowledge between head and tail classes by blending samples. The core idea of our method is to create new samples by blending head and tail samples. Head samples are selected using a uniform sampler that retains the long-tailed distribution, while tail samples are selected using a differential sampler that reverses the long-tailed distribution to alleviate imbalance. Our approach aims to diversify both tail and head classes. During the training phase, we utilize the generated samples to update the original dataset for training deep networks. Mixed mutual transfer simultaneously enhances the performance of both head and tail classes. Experimental results on various class-imbalanced datasets show that the proposed method significantly outperforms existing methods, demonstrating its effectiveness in improving the performance of long-tailed deep networks.

Abstract Background The association between body fat percentage (BFP) and kidney stone disease (KSD) among bus drivers has not been explored in the existing literature. Thus, this study was conducted to explore the influence of BFP on the risk of KSD as well as KSD development for bus drivers to fill the research gap. Methods A cross-sectional and longitudinal cohort study was designed. In total, 3433 bus drivers were included in the cross-sectional analyses, and 1864 bus drivers without KSD at baseline and with regular follow-up were included in the longitudinal cohort study. Results During a median follow-up of 2.9 years, KSD occurred in 15.0% of bus drivers. Multivariate logistic analysis found that each 5% higher BFP was not only significantly related with higher odds of KSD (odds ratio [OR] = 1.48), but also associated with higher odds of developing KSD (OR = 1.27). The risk of prevalent KSD in obesity group based on BFP was 2.47 times of the normal group; and the corresponding risk of developing KSD was 1.61 times. For obesity bus drives with age

Abstract The objective of this retrospective study was to predict short-term efficacy of anti-vascular endothelial growth factor (VEGF) treatment in diabetic macular edema (DME) using machine learning regression models. Real-world data from 279 DME patients who received anti-VEGF treatment at Ineye Hospital of Chengdu University of TCM between April 2017 and November 2022 were analyzed. Eight machine learning regression models were established to predict four clinical efficacy indicators. The accuracy of the models was evaluated using mean absolute error (MAE), mean square error (MSE) and coefficient of determination score (R 2). Multilayer perceptron had the highest R 2 and lowest MAE among all models. Regression tree and lasso regression had similar R 2, with lasso having lower MAE and MSE. Ridge regression, linear regression, support vector machines and polynomial regression had lower R 2 and higher MAE. Support vector machine had the lowest MSE, while polynomial regression had the highest MSE. Stochastic gradient descent had the lowest R 2 and high MAE and MSE. The results indicate that machine learning regression algorithms are valuable and effective in predicting short-term efficacy in DME patients through anti-VEGF treatment, and the lasso regression is the most effective ML algorithm for developing predictive regression models.

With advancements in computing technology and the rapid progress of data science, machine learning has been widely applied in various fields, showing great potential, especially in digital healthcare. In recent years, conversational diagnostic systems have been used to predict diseases through symptom checking. Early systems predicted the likelihood of a single disease by minimizing the number of questions asked. However, doctors typically perform differential diagnoses in real medical practice, considering multiple possible diseases to address diagnostic uncertainty. This requires systems to ask more critical questions to improve diagnostic accuracy. Nevertheless, such systems in acute medical situations need to process information quickly and accurately, but the complexity of differential diagnosis increases the system’s computational cost. To improve the efficiency and accuracy of telemedicine diagnostic systems, this study developed an optimized algorithm for the Top-K algorithm. This algorithm dynamically adjusts the number of the most likely diseases and symptoms by real-time monitoring of case progress, optimizing the diagnostic process, enhancing accuracy (99.81%), and increasing the exclusion rate of severe pathologies. Additionally, the Top-K algorithm optimizes the diagnostic model through a policy network loss function, effectively reducing the number of symptoms and diseases processed and improving the system’s response speed by 1.3–1.9 times compared to the state-of-the-art differential diagnosis systems.

Abstract Background Systematic description of library quality and sequencing performance of single-cell RNA sequencing (scRNA-seq) data is imperative for subsequent downstream modules, including re-pooling libraries. While several packages have been developed to visualise quality control (QC) metrics for scRNA-seq data, they do not include expression-based QC to discriminate between true variation and background noise. Results We present scQCEA (acronym of the single-cell RNA sequencing Quality Control and Enrichment Analysis), an R package to generate reports of process optimisation metrics for comparing sets of samples and visual evaluation of quality scores. scQCEA can import data from 10X or other single-cell platforms and includes functions for generating an interactive report of QC metrics for multi-omics data. In addition, scQCEA provides automated cell type annotation on scRNA-seq data using differential gene expression patterns for expression-based quality control. We provide a repository of reference gene sets, including 2348 marker genes, which are exclusively expressed in 95 human and mouse cell types. Using scRNA-seq data from 56 gene expressions and V(D)J T cell replicates, we show how scQCEA can be applied for the visual evaluation of quality scores for sets of samples. In addition, we use the summary of QC measures from 342 human and mouse shallow-sequenced gene expression profiles to specify optimal sequencing requirements to run a cell-type enrichment analysis function. Conclusions The open-source R tool will allow examining biases and outliers over biological and technical measures, and objective selection of optimal cluster numbers before downstream analysis. scQCEA is available at https://isarnassiri.github.io/scQCEA/ as an R package. Full documentation, including an example, is provided on the package website.

With the rapid development of smart grid (SG) technologies, massive system data has been generated for SG for grid operation status monitoring and fault warning, this massive integrated data has brought great challenges for data transmission and storage. Thus, the breakthrough of data compression technology has become more and more important for data processing, this proposal, a lossless data compression method is proposed. The proposed method improves the Lempel-Ziv-Welch (LZW) algorithm. Specifically, the proposed method employs a parallel search approach to search the index of the dictionary globally by dividing the dictionary into several small dictionaries of different sizes and bit widths. In addition, the dynamic variable-length coding method is used as the output code which can offer flexible bit widths instead of fixed values, and the optimal dictionary partition combination and size parameters are selected to construct dictionaries. At last, the improved algorithm is cascaded with Huffman algorithm to form the proposed data compression algorithm. The data compression efficiency has been successfully verified by comparing with conventional Huffman and LZW data compression algorithms through data simulation, and it is observed to offer a better compression ratio than those methods with only LZW algorithm or Huffman algorithm, and save storage space effectively. More than this, the proposed method has realized lossless compression for power system data and guaranteed the integrity of the data, which will have better applicability for dealing with any similar information data.

Polygenic Risk Scores (PRS) show promise for facilitating cancer risk stratification. Here, the authors performed a large genome-wide association study and develop a PRS for nasopharyngeal carcinoma, which could improve risk stratification and personalized screening.

ObjectiveTo evaluate the diagnostic accuracy of deep learning algorithms to identify age-related macular degeneration and to explore factors impacting the results for future model training.MethodsDiagnostic accuracy studies published in PubMed, EMBASE, the Cochrane Library, and ClinicalTrails.gov before 11 August 2022 which employed deep learning for age-related macular degeneration detection were identified and extracted by two independent researchers. Sensitivity analysis, subgroup, and meta-regression were performed by Review Manager 5.4.1, Meta-disc 1.4, and Stata 16.0. The risk of bias was assessed using QUADAS-2. The review was registered (PROSPERO CRD42022352753).ResultsThe pooled sensitivity and specificity in this meta-analysis were 94% (P = 0, 95% CI 0.94-0.94, I2 = 99.7%) and 97% (P = 0, 95% CI 0.97-0.97, I2 = 99.6%), respectively. The pooled positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the area under the curve value were 21.77(95% CI 15.49-30.59), 0.06 (95% CI 0.04-0.09), 342.41 (95% CI 210.31-557.49), and 0.9925, respectively. Meta-regression indicated that types of AMD (P = 0.1882, RDOR = 36.03) and layers of the network (P = 0.4878, RDOR = 0.74) contributed to the heterogeneity.ConclusionsConvolutional neural networks are mostly adopted deep learning algorithms in age-related macular degeneration detection. Convolutional neural networks, especially ResNets, are effective in detecting age-related macular degeneration with high diagnostic accuracy. Types of age-related macular degeneration and layers of the network are the two essential factors that impact the model training process. Proper layers of the network will make the model more reliable. More datasets established by new diagnostic methods will be used to train deep learning models in the future, which will benefit for fundus application screening, long-range medical treatment, and reducing the workload of physicians.

This paper is concerned with the existence of a compact global attractor for a class of competition model in n−dimensional (n ≥ 1) domains. Using mathematical induction and more detailed interpolation estimates, especially Gagliardo-Nirenberg inequality, we obtain the existence of a compact global attractor, which implies the uniform boundedness of the global solutions. In particular, we get that the Shigesada-Kawasaki-Teramoto competition model has a compact global attractor for n < 10. The result of the S-K-T model extends the existence results of compact global attractor in [21] from n < 8 to n < 10, and extends the uniform boundedness results of the global solutions in [17] to the non-convex domain.

Abstract Background Protein biomarkers play important roles in cancer diagnosis. Many efforts have been made on measuring abnormal expression intensity in biological samples to identity cancer types and stages. However, the change of subcellular location of proteins, which is also critical for understanding and detecting diseases, has been rarely studied. Results In this work, we developed a machine learning model to classify protein subcellular locations based on immunohistochemistry images of human colon tissues, and validated the ability of the model to detect subcellular location changes of biomarker proteins related to colon cancer. The model uses representative image patches as inputs, and integrates feature engineering and deep learning methods. It achieves 92.69% accuracy in classification of new proteins. Two validation datasets of colon cancer biomarkers derived from published literatures and the human protein atlas database respectively are employed. It turns out that 81.82 and 65.66% of the biomarker proteins can be identified to change locations. Conclusions Our results demonstrate that using image patches and combining predefined and deep features can improve the performance of protein subcellular localization, and our model can effectively detect biomarkers based on protein subcellular translocations. This study is anticipated to be useful in annotating unknown subcellular localization for proteins and discovering new potential location biomarkers.

This paper introduces the basic working principle of SWY-II digital water level instrument, analyzes and summarizes the operation failure types and corresponding solutions of several kinds of underground fluid observation instruments for earthquake precursors based on years of working experience. The types of operation faults can be roughly divided into: network communication fault, power supply system fault, instrument software and hardware fault, etc. the corresponding solutions are: restart the equipment, replace the power supply and communication cable, replace the spare parts of the standby machine, maintain the circuit board components of the instrument, reset and upgrade the software program, etc. The specific solutions for the fault of the observation instrument can provide technical support for the continuous and reliable observation data of the underground fluid, and provide certain reference for the maintenance personnel of the earthquake precursor instrument in the maintenance of the instrument.

A new neural network prediction model is proposed for predicting ship motion attitude with high accuracy. This prediction model is based on an adaptive dynamic particle swarm optimization algorithm (ADPSO) and bidirectional long short-term memory (BiLSTM) neural network, which is to optimize the hyperparameters of BiLSTM neural network by the proposed ADPSO algorithm. The ADPSO algorithm introduces dynamic search space strategy into the classical particle swarm optimization algorithm and adjusts the learning factor adaptively to balance the global and local search ability, so as to improve the optimization performance and improve its optimization effect in BiLSTM parameter optimization process. The results show that the model can obtain higher prediction accuracy and faster convergence speed, and has better prediction performance in the prediction of ship motion attitude.

Objective To estimate the safety and feasibility of laparoendoscopic single-site surgery (LESS) in pregnant patients with acute abdomen. Methods Baseline characteristics, surgical results, and obstetric and neonatal outcomes were retrospectively compared between single and multiport procedures in patients who underwent laparoscopic surgery during pregnancy between 2017 and 2021. Results Fifty-four pregnant patients were included: 26 who underwent LESS (salpingectomy, 11 cases/cystectomy, 15 cases) and 28 who underwent conventional laparoscopic surgeries (salpingectomy, 12 cases/cystectomy, 16 cases) during pregnancy. One patient in the single-port group required additional ports. No patients converted to laparotomy. In patients undergoing salpingectomy, the single-port group showed lower 8- and 24-h postoperative pain scores, shorter hospital stays, and lower Self-rating Anxiety Scale scores prior to discharge versus conventional laparoscopy. One patient experienced postoperative vaginal bleeding and a missed abortion during follow-up. In patients receiving cystectomy, 8- and 24-h pain scores, postoperative hospital stay, and anxiety scores were lower in the single-port versus multiport group. Other outcomes were comparable between the groups. Conclusion The feasibility and efficacy of laparoscopic surgery during pregnancy is similar between single- or multiport routes, however, the single-port route may be associated with less postoperative pain, shorter hospital stay, and lower anxiety.

The fierce competition in tourism has brought a string of problems, such as the homogeneous development of regional tourism resources and the escalation of regional competition. One key issue in urban tourism today is how to identify competitors, recognize own advantageous resources, and clarify the direction of development. This paper evaluates the competitiveness of regional tourism in three steps: setting up an evaluation system, calculating niche and niche overlap, and conducting synthesis and cluster analysis. Firstly, a comprehensive evaluation system of urban tourism niche was established, which includes such four dimensions as tourism resources, tourism market, social economy, and eco-environment, and the weight of each index was calculated by entropy method. Next, regional niche and niche overlap were computed by niche theory. Taking the mean of niche overlap and that of composite niche as the references, the authors drew an overall distribution map of regional tourism niche. After that, a cluster analysis was carried out with the four-dimensional niche and composite niche of regional tourism. Finally, an empirical analysis was performed on the tourism competitiveness in ten prefectures of Shaanxi Province, using the data in 2010–2018. The results show that the prefectures differed greatly in tourism development and competed fiercely against each other.

Abstract Background Riemerella anatipestifer (RA), the causative agent of duck infectious serositis, leads to high mortality in duck flocks and great economic losses in duck industry. Previous studies on RA are largely focused on its detection, virulence factors, serology, epidemiology as well as antibiotic resistance. Neither drug tolerant persisters nor the persister level under the treatment of antibiotics has been revealed. The persisters are non-growing or dormant cells within an isogenic bacterial population; they play important roles in recurrent infection and formation of drug resistant mutants. The aim of this study is to detect the drug tolerant persisters from the exponentially grown population of RA reference strain (RA 11845) or RA clinical isolate (RA TQ3), and address whether a single antibiotic or a combination of two or three antimicrobials can eradicate the persisters at respective maximum serum/plasma concentration (Cmax). Result With the concentration of a test antibiotic increased, a small fraction of cells in the exponentially grown culture of RA reference strain (RA 11845) or RA clinical isolate (RA TQ3) always survived, irrespective of treatment time, indicating the presence of drug tolerant presisters. A single antibiotic cannot eradicate the persisters of both RA strains at respective Cmax, except that the Cmax of ceftiofur wiped out the population of the reference strain (RA 11845). Besides, the clinical isolate RA TQ3 presented a higher tolerance to ceftiofur in comparison to that of the reference strain (RA 11845). Combination of any two or three antimicrobials eliminated the drug tolerant persisters of RA TQ3 completely at respective Cmax. Conclusion A sub-community of drug tolerant persisters was present in RA population. Persisters of RA TQ3 are single drug tolerant and not multidrug tolerant persisters.

Abstract A series of N-doped graphene (NG) and TiO2 supported MnOx–CeO2 catalysts were prepared by a hydrothermal method. The catalysts with different molar ratios of Mn/Ce (6: 1, 10: 1, 15: 1) were investigated for the low-temperature selective catalytic reduction (SCR) of NO x with NH3. The synthesized catalysts were characterized by HRTEM, SEM, XRD, BET, XPS, and NH3-TPD technologies. The characterization results indicated that manganese and cerium oxide particles dispersed on the surface of the TiO2–NG support uniformly, and that manganese and cerium oxides existed in different valences on the surface of the TiO2–NG support. At Mn element loading of 8 wt%, MnO x –CeO2(10: 1)/TiO2–1%NG displayed superior activity and improved SO2 resistance. On the basis of the catalyst characterization, excellent catalytic performance and SO2 tolerance at low temperature were attributed to the high content of manganese with high oxidation valence, extensive oxidation of NO into NO2 by CeO2 and strong NO adsorption capacity, and electron transfer of N-doped graphene.

In order to obtain accurate underwater target tracking results in forward-looking sonar image sequences, an adaptive particle swarm optimization (APSO) algorithm is proposed to track the underwater target. Specifically, an adaptive inertia weight is first applied to solve the problem of diversity loss and premature convergence in the PSO algorithm. Then, the individual optimal fitness value of a random particle selected from particle swarm is compared with that of the current particle, and the larger one is used to update the velocity of the particle to further prevent particles from falling into the local optimum. On this basis, when the underwater target is occluded, a new update strategy based on an adaptive discrete swarm optimization algorithm is used to update the particle's position according to the level of occlusion so as to achieve a better tracking result. Finally, the experimental results show that the proposed APSO can accurately complete underwater target tracking. Compared with other algorithms, the proposed APSO has higher tracking accuracy and faster tracking speed, and it has a certain effectiveness and adaptability.

As one of the main sources of remote sensing big data, synthetic aperture radar (SAR) can provide all-day and all-weather Earth image acquisition. However, speckle noise in SAR images brings a notable limitation for its big data applications, including image analysis and interpretation. Deep learning has been demonstrated as an advanced method and technology for SAR image despeckling. Most existing deep-learning-based methods adopt supervised learning and use synthetic speckled images to train the despeckling networks. This is because they need clean images as the references, and it is hard to obtain purely clean SAR images in real-world conditions. However, significant differences between synthetic speckled and real SAR images cause the domain gap problem. In other words, they cannot show superior performance for despeckling real SAR images as they do for synthetic speckled images. Inspired by recent studies on self-supervised denoising, we propose an advanced SAR image despeckling method by virtue of Bernoulli-sampling-based self-supervised deep learning, called SSD-SAR-BS. By only using real speckled SAR images, Bernoulli-sampled speckled image pairs (input–target) were obtained as the training data. Then, a multiscale despeckling network was trained on these image pairs. In addition, a dropout-based ensemble was introduced to boost the network performance. Extensive experimental results demonstrated that our proposed method outperforms the state-of-the-art for speckle noise suppression on both synthetic speckled and real SAR datasets (i.e., Sentinel-1 and TerraSAR-X).

In recent years, with the development of computer science, deep learning is held as competent enough to solve the problem of inference and learning in high dimensional space. Therefore, it has received unprecedented attention from both the academia and the business community. Compared with CPU/GPU, FPGA has attracted much attention for its high-energy efficiency, short development cycle and reconfigurability in the aspect of deep learning algorithm. However, because of the limited research on OpenCL optimization on FPGA of deep learning algorithms, OpenCL tools and models applied to CPU/GPU cannot be directly used on FPGA. This makes it difficult for software programmers to use FPGA when implementing deep learning algorithms for a rewarding performance. To solve this problem, this paper proposed an OpenCL computational model based on FPGA template architecture to optimize the time-consuming convolution layer in deep learning. The comparison between the program applying the computational model and the corresponding optimization program provided by Xilinx indicates that the former is 8-40 times higher than the latter in terms of performance.

Specific primers synthesized according to conservative regions of polygalacturonase inhibiting protein (PGIP) gene were used to amplify Prunus Dulcis genomic DNA by polymerase-chain reaction (PCR). Six bands (pgip1, pgip2, pgip3, pgip4, pgip5 and pgip6) of genes were obtained and cloned into PBS-T vector. According to the length of bands, 717bp, 864bp, 796bp were A1 (pgip1, pgip2, pgip3), A2 (pgip4), A4 (pgip5, pgip6), respectively. DNA sequences showed that the fragments taken together were the gene encoding PGIP. A2 and A3 contained two exons interrupted by one intron, which has GT-AG sequence. Its DNA and amino acid sequences were highly homologies to those from Prunus Persica; Prunus Salicina; Prunus Americana; Prunus Mume, respectively. A conserved lencinerial fragment exists in the derived protein sequence.

Explicit structural inference is one key point to improve the accuracy of scene parsing. Meanwhile, adversarial training method is able to reinforce spatial contiguity in output segmentations. To take both advantages of the structural learning and adversarial training simultaneously, we propose a novel deep learning network architecture called Structural Inference Embedded Adversarial Networks (SIEANs) for pixel-wise scene labeling. The generator of our SIEANs, a novel designed scene parsing network, makes full use of convolutional neural networks and long short-term memory networks to learn the global contextual information of objects in four different directions from RGB-(D) images, which is able to describe the (three-dimensional) spatial distributions of objects in a more comprehensive and accurate way. To further improve the performance, we explore the adversarial training method to optimize the generator along with a discriminator, which can not only detect and correct higher-order inconsistencies between the predicted segmentations and corresponding ground truths, but also exploit full advantages of the generator by fine-tuning its parameters so as to obtain higher consistencies. The experimental results demonstrate that our proposed SIEANs is able to achieve a better performance on PASCAL VOC 2012, SIFT FLOW, PASCAL Person-Part, Cityscapes, Stanford Background, NYUDv2, and SUN-RGBD datasets compared to the most of state-of-the-art methods.

Vertical pendulum (VP) tiltmeter is a kind of earthquake precursor observation equipment, which is used to record the interaction force associated with astronomical tidal tilts caused. Currently, VP broadband tiltmeter and vertical sensor (VS) vertical pendulum tiltmeter are primarily used. In this paper, we compare the two different instruments by using four aspects—mechanical structure, circuitry, zeroing, and bandwidth—based on their working principles and applications. We conclude that VP broadband tiltmeter is more superior compared with VS vertical pendulum tiltmeter because of its higher bandwidth and degree of automation.

Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery.

Atrial fibrillation (AF) is the most common cardiac arrhythmia at the clinic. Recent GWAS identified several variants associated with AF, but they account for

Heart failure affects 1-2% of the adult population worldwide and coronary artery disease (CAD) is the underlying etiology of heart failure in 70% of the patients. The pathway of apelin and its apelin receptor (APJ) was implicated in the pathogenesis of heart failure in animal models, but a similar role in humans is unknown. We studied a functional variant, rs9943582 (-154G/A), at the 5'-untranslated region, that was associated with decreased expression of the APJ receptor gene (APLNR) in a population consisting of 1,751 CAD cases and 1,022 controls. Variant rs9943582 was not associated with CAD, but among CAD patients, it showed significant association with left ventricular systolic dysfunction (431 CAD patients with left ventricular systolic dysfunction (LV ejection fraction or LVEF< 40%) versus 1,046 CAD patients without LV systolic dysfunction (LVEF>50%) (P-adj = 6.71 × 10(-5), OR = 1.43, 95% CI, 1.20-1.70). Moreover, rs9943582 also showed significant association with quantitative echocardiographic parameters, including left ventricular end-diastolic diameter (effect size: increased 1.67 ± 0.43 mm per risk allele A, P = 1.15 × 10(-4)), left atrial size (effect size: increased 2.12 ± 0.61 mm per risk allele A, P = 9.56 × 10(-4)) and LVEF (effect size: decreased 2.59 ± 0.32 percent per risk allele A, P = 7.50 × 10(-15)). Our findings demonstrate that allele A of rs9943582 was significantly associated with left ventricular systolic dysfunction, left ventricular end-diastolic diameter, the left atrial diameter and LVEF in the CAD population, which suggests an important role of the apelin/APJ system in the pathology of heart failure associated with ischemic heart disease.

Abstract The c-Myc and human telomerase reverse transcriptase gene (hTERT) gene are frequently deregulated and overexpressed in malignancy. hTERT activity is induced by c-Myc and strategies designed to inhibit c-Myc expression in cancer cells may have considerable therapeutic value. We designed and used a short hairpin RNA to inhibit c-Myc expression in Colo 320 cells and validated its effect on cell proliferation. In this study, four c-Myc-shRNA expression vectors were constructed and introduced into Colo 320 cells. The effects of c-Myc silencing on tumor cell growth was assessed by soft agar assay and DNA synthesis experiments. The expressions of c-Myc and hTERT were also assessed by real-time reverse transcription-polymerase chain reaction and Western blot analysis. Upon transient transfection with plasmid encoding shRNA, it was found that expression of c-Myc and hTERT decreased in shRNA-transfected cells. The downregulation of c-Myc and hTERT inhibited cell growth, shortened telomere lengths, and suppressed telomerase activity. In conclusion, our findings demonstrate that shRNA of c-Myc can inhibit the DNA replication in Colo 320 cells effectively and reduce telomere length and telomerase activity, therefore, it could be used as a new potential anticancer tool for therapy of human colon cancer.