Network Analysis-Based Approach for Exploring the Potential Diagnostic Biomarkers of Acute Myocardial Infarction

Acute myocardial infarction (AMI) is a severe cardiovascular disease that is a serious threat to human life. However, the specific diagnostic biomarkers have not been fully clarified and candidate regulatory targets for AMI have not been identified. In order to explore the potential diagnostic biomarkers and possible regulatory targets of AMI, we used a network analysis-based approach to analyze microarray expression profiling of peripheral blood in patients with AMI. The significant differentially-expressed genes (DEGs) were screened by Limma and constructed a gene function regulatory network (GO-Tree) to obtain the inherent affiliation of significant function terms. The pathway action network was constructed, and the signal transfer relationship between pathway terms was mined in order to investigate the impact of core pathway terms in AMI. Subsequently, constructed the transcription regulatory network of DEGs. Weighted gene co-expression network analysis (WGCNA) was employed to identify significantly altered gene modules and hub genes in two groups. Subsequently, the transcription regulation network of DEGs was constructed. We found that specific gene modules may provide a better insight into the potential diagnostic biomarkers of AMI. Our findings revealed and verified that NCF4, AQP9, NFIL3, DYSF, GZMA, TBX21, PRF1 and PTGDR genes by RT-qPCR. TBX21 and PRF1 may be potential candidates for diagnostic biomarker and possible regulatory targets in AMI

Tumor elimination by clustered microRNAs miR-306 and miR-79 via noncanonical activation of JNK signaling

JNK signaling plays a critical role in both tumor promotion and tumor suppression. Here, we identified clustered microRNAs (miRNAs) miR-306 and miR-79 as novel tumor-suppressor miRNAs that specifically eliminate JNK-activated tumors in Drosophila. While showing only a slight effect on normal tissue growth, miR-306 and miR-79 strongly suppressed growth of multiple tumor models, including malignant tumors caused by Ras activation and cell polarity defects. Mechanistically, these miRNAs commonly target the mRNA of an E3 ubiquitin ligase ring finger protein 146 (RNF146). We found that RNF146 promotes degradation of tankyrase (Tnks), an ADP-ribose polymerase that promotes JNK activation in a noncanonical manner. Thus, downregulation of RNF146 by miR-306 and miR-79 leads to hyper-enhancement of JNK activation. Our data show that, while JNK activity is essential for tumor growth, elevation of miR-306 or miR-79 overactivate JNK signaling to the lethal level via noncanonical JNK pathway and thus eliminate tumors, providing a new miRNA-based strategy against cancer

Early embryonic development of green crucian carp <em>Carassius auratus</em> indigentiaus subsp. nov.

The early embryonic development of the green crucian carp (Carassius auratus indigentiaus subsp. nov.) was observed to study its timing and characteristics. The fertilized eggs are round, slightly yellow, and viscous demersal. The egg diameter after water swelling was 1.47 ± 0.04 mm. Embryonic development can be divided into eight stages according to its major characteristics: blastoderm formation, mitotic, blastula, gastrula, neurula, blastopore closure, organogenetic and hatching stages. Under a water temperature of 24 ± 1 °C, salinity of 35 ± 1, and pH of 7.4 ± 0.5, the blastoderm began to form 35 min after fertilization. It entered the mitotic stage at 55 min, blastula stage at 220 min, gastrula stage at 460 min, neurula stage at 675 min, blastopore closure stage at 700 min, organogenetic stage at 900 min and hatching stage at 3390 min. The total length of newly hatched larvae was 4.07 ± 0.35 mm. Regression models of growth characteristics were obtained. The full-length growth rate was fastest from 15 to 26 days, with an average of 0.396 mm/day. Compared with other cyprinid fishes, green crucian carp exhibited some distinct characteristics in certain stages of embryonic development. The eye primordium developed before the sarcomere, and the heart rate was relatively high before the member stage. Yolk fluctuation was observed during the multi-cell phase of embryonic development. The sarcomere formed after the eye primordium. The heart rate in the hatching phase was 136 beats/min. This study provides a reference for embryonic development in green crucian carp, which will assist its large-scale cultivation

V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception

Recent advancements in Vehicle-to-Everything communication technology have enabled autonomous vehicles to share sensory information to obtain better perception performance. With the rapid growth of autonomous vehicles and intelligent infrastructure, the V2X perception systems will soon be deployed at scale, which raises a safety-critical question: \textit{how can we evaluate and improve its performance under challenging traffic scenarios before the real-world deployment?} Collecting diverse large-scale real-world test scenes seems to be the most straightforward solution, but it is expensive and time-consuming, and the collections can only cover limited scenarios. To this end, we propose the first open adversarial scene generator V2XP-ASG that can produce realistic, challenging scenes for modern LiDAR-based multi-agent perception systems. V2XP-ASG learns to construct an adversarial collaboration graph and simultaneously perturb multiple agents' poses in an adversarial and plausible manner. The experiments demonstrate that V2XP-ASG can effectively identify challenging scenes for a large range of V2X perception systems. Meanwhile, by training on the limited number of generated challenging scenes, the accuracy of V2X perception systems can be further improved by 12.3\% on challenging and 4\% on normal scenes. Our code will be released at https://github.com/XHwind/V2XP-ASG.Comment: ICRA 2023, see https://github.com/XHwind/V2XP-AS

Exploring Impaired SERCA Pump-Caused Alternation Occurrence in Ischemia

Impaired sarcoplasmic reticulum (SR) calcium transport ATPase (SERCA) gives rise to Ca(2+) alternans and changes of the Ca2+release amount. These changes in Ca(2+) release amount can reveal the mechanism underlying how the interaction between Ca(2+) release and Ca(2+) uptake induces Ca(2+) alternans. This study of alternans by calculating the values of Ca(2+) release properties with impaired SERCA has not been explored before. Here, we induced Ca(2+) alternans by using an impaired SERCA pump under ischemic conditions. The results showed that the recruitment and refractoriness of the Ca(2+) release increased as Ca(2+) alternans occurred. This indicates triggering Ca waves. As the propagation of Ca waves is linked to the occurrence of Ca(2+) alternans, the "threshold" for Ca waves reflects the key factor in Ca(2+) alternans development, and it is still controversial nowadays. We proposed the ratio between the diastolic network SR (NSR) Ca content (Cansr) and the cytoplasmic Ca content (Ca i ) (Cansr/Ca i ) as the "threshold" of Ca waves and Ca(2+) alternans. Diastolic Cansr, Ca i , and their ratio were recorded at the onset of Ca(2+) alternans. Compared with certain Cansr and Ca i , the "threshold" of the ratio can better explain the comprehensive effects of the Ca(2+) release and the Ca(2+) uptake on Ca(2+) alternans onset. In addition, these ratios are related with the function of SERCA pumps, which vary with different ischemic conditions. Thus, values of these ratios could be used to differentiate Ca(2+) alternans from different ischemic cases. This agrees with some experimental results. Therefore, the certain value of diastolic Cansr/Ca i can be the better "threshold" for Ca waves and Ca(2+) alternans

The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research

Advances in Single-vehicle intelligence of automated driving have encountered significant challenges because of limited capabilities in perception and interaction with complex traffic environments. Cooperative Driving Automation~(CDA) has been considered a pivotal solution to next-generation automated driving and intelligent transportation. Though CDA has attracted much attention from both academia and industry, exploration of its potential is still in its infancy. In industry, companies tend to build their in-house data collection pipeline and research tools to tailor their needs and protect intellectual properties. Reinventing the wheels, however, wastes resources and limits the generalizability of the developed approaches since no standardized benchmarks exist. On the other hand, in academia, due to the absence of real-world traffic data and computation resources, researchers often investigate CDA topics in simplified and mostly simulated environments, restricting the possibility of scaling the research outputs to real-world scenarios. Therefore, there is an urgent need to establish an open-source ecosystem~(OSE) to address the demands of different communities for CDA research, particularly in the early exploratory research stages, and provide the bridge to ensure an integrated development and testing pipeline that diverse communities can share. In this paper, we introduce the OpenCDA research ecosystem, a unified OSE integrated with a model zoo, a suite of driving simulators at various resolutions, large-scale real-world and simulated datasets, complete development toolkits for benchmark training/testing, and a scenario database/generator. We also demonstrate the effectiveness of OpenCDA OSE through example use cases, including cooperative 3D LiDAR detection, cooperative merge, cooperative camera-based map prediction, and adversarial scenario generation

In Silico Investigation into Cellular Mechanisms of Cardiac Alternans in Myocardial Ischemia

Myocardial ischemia is associated with pathophysiological conditions such as hyperkalemia, acidosis, and hypoxia. These physiological disorders may lead to changes on the functions of ionic channels, which in turn form the basis for cardiac alternans. In this paper, we investigated the roles of hyperkalemia and calcium handling components played in the genesis of alternans in ischemia at the cellular level by using computational simulations. The results show that hyperkalemic reduced cell excitability and delayed recovery from inactivation of depolarization currents. The inactivation time constant τf of L-type calcium current (ICaL) increased obviously in hyperkalemia. One cycle length was not enough for ICaL to recover completely. Alternans developed as a result of ICaL responding to stimulation every other beat. Sarcoplasmic reticulum calcium-ATPase (SERCA2a) function decreased in ischemia. This change resulted in intracellular Ca (Cai) alternans of small magnitude. A strong Na+-Ca2+ exchange current (INCX) increased the magnitude of Cai alternans, leading to APD alternans through excitation-contraction coupling. Some alternated repolarization currents contributed to this repolarization alternans

Circular RNAs as a potential source of neoepitopes in cancer

Neoepitopes have attracted much attention as targets for immunotherapy against cancer. Therefore, efficient neoepitope screening technology is an essential step in the development of personalized vaccines. Circular RNAs (circRNAs) are generated by back-splicing and have a single-stranded continuous circular structure. So far, various circRNAs have been poorly characterized, though new evidence suggests that a few translated circRNAs may play a role in cancer. In the present study, circRNA was used as a source of neoepitope, a novel strategy as circRNA-derived neoepitopes have never been previously explored. The present study reports CIRC_neo (circRNA-derived neoepitope prediction pipeline), which is a comprehensive and automated bioinformatic pipeline for the prediction of circRNA-derived neoepitopes from RNA sequencing data. The computational prediction from sequencing data requires complex computational workflows to identify circRNAs, derive the resulting peptides, infer the types of human leukocyte antigens (HLA I and HLA II) in patients, and predict the neoepitopes binding to these antigens. The present study proposes a novel source of neoepitopes. The study focused on cancer-specific circRNAs, which have greatly expanded the source pool for neoepitope discovery. The statistical analysis of different features of circRNA-derived neoepitopes revealed that circRNAs could produce long proteins or truncated proteins. Because the peptides were completely foreign to the human body, they could be highly immunogenic. Importantly, circRNA-derived neoepitopes capable of binding to HLA were discovered. In the current study, circRNAs were systematically analyzed, revealing potential targets and novel research clues for cancer diagnosis, treatment, and prospective personalized vaccine research

Automated Driving Systems Data Acquisition and Processing Platform

This paper presents an automated driving system (ADS) data acquisition and processing platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected automated vehicle (CAV) cooperative perception. This platform presents a holistic pipeline from the raw advanced sensory data collection to data processing, which can process the sensor data from multiple CAVs and extract the objects' Identity (ID) number, position, speed, and orientation information in the map and Frenet coordinates. First, the ADS data acquisition and analytics platform are presented. Specifically, the experimental CAVs platform and sensor configuration are shown, and the processing software, including a deep-learning-based object detection algorithm using LiDAR information, a late fusion scheme to leverage cooperative perception to fuse the detected objects from multiple CAVs, and a multi-object tracking method is introduced. To further enhance the object detection and tracking results, high definition maps consisting of point cloud and vector maps are generated and forwarded to a world model to filter out the objects off the road and extract the objects' coordinates in Frenet coordinates and the lane information. In addition, a post-processing method is proposed to refine trajectories from the object tracking algorithms. Aiming to tackle the ID switch issue of the object tracking algorithm, a fuzzy-logic-based approach is proposed to detect the discontinuous trajectories of the same object. Finally, results, including object detection and tracking and a late fusion scheme, are presented, and the post-processing algorithm's improvements in noise level and outlier removal are discussed, confirming the functionality and effectiveness of the proposed holistic data collection and processing platform

Multiobjective Sparse Ensemble Learning by Means of Evolutionary Algorithms

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Ensemble learning can improve the performance of individual classifiers by combining their decisions. The sparseness of ensemble learning has attracted much attention in recent years. In this paper, a novel multiobjective sparse ensemble learning (MOSEL) model is proposed. Firstly, to describe the ensemble classifiers more precisely the detection error trade-off (DET) curve is taken into consideration. The sparsity ratio (sr) is treated as the third objective to be minimized, in addition to false positive rate (fpr) and false negative rate (fnr) minimization. The MOSEL turns out to be augmented DET (ADET) convex hull maximization problem. Secondly, several evolutionary multiobjective algorithms are exploited to find sparse ensemble classifiers with strong performance. The relationship between the sparsity and the performance of ensemble classifiers on the ADET space is explained. Thirdly, an adaptive MOSEL classifiers selection method is designed to select the most suitable ensemble classifiers for a given dataset. The proposed MOSEL method is applied to well-known MNIST datasets and a real-world remote sensing image change detection problem, and several datasets are used to test the performance of the method on this problem. Experimental results based on both MNIST datasets and remote sensing image change detection show that MOSEL performs significantly better than conventional ensemble learning methods