Mckeewalsh6169

Anomaly detection systems can accurately identify malicious network traffic, providing network security. With the development of internet technology, network attacks are becoming more and more sourced and complicated, making it difficult for traditional anomaly detection systems to effectively analyze and identify abnormal traffic. At present, deep neural network (DNN) technology achieved great results in terms of anomaly detection, and it can achieve automatic detection. However, there still exists misclassified traffic in the prediction results of deep neural networks, resulting in redundant alarm information. This paper designs a two-level anomaly detection system based on deep neural network and association analysis. We made a comprehensive evaluation of experiments using DNNs and other neural networks based on publicly available datasets. Through the experiments, we chose DNN-4 as an important part of our system, which has high precision and accuracy in identifying malicious traffic. The Apriori algorithm can mine rules between various discretized features and normal labels, which can be used to filter the classified traffic and reduce the false positive rate. JZL184 Finally, we designed an intrusion detection system based on DNN-4 and association rules. We conducted experiments on the public training set NSL-KDD, which is considered as a modified dataset for the KDDCup 1999. The results show that our detection system has great precision in malicious traffic detection, and it achieves the effect of reducing the number of false alarms.Modified gibberellin (GA) signaling leads to semi-dwarfism with low nitrogen (N) use efficiency (NUE) in crops. An understanding of GA-mediated N uptake is essential for the development of crops with improved NUE. The function of GA in modulating N uptake capacity and nitrate (NO3-) transporters (NRTs) was analyzed in the GA synthesis-deficient mutant zmga3ox grown under low (LN) and sufficient (SN) N conditions. LN significantly suppressed the production of GA1, GA3, and GA4, and the zmga3ox plants showed more sensitivity in shoots as well as LN stress. Moreover, the higher anthocyanin accumulation and the decrease of chlorophyll content were also recorded. The net NO3- fluxes and 15N content were decreased in zmga3ox plants under both LN and SN conditions. Exogenous GA3 could restore the NO3- uptake in zmga3ox plants, but uniconazole repressed NO3- uptake. Moreover, the transcript levels of ZmNRT2.1/2.2 were downregulated in zmga3ox plants, while the GA3 application enhanced the expression level. Furthermore, the RNA-seq analyses identified several transcription factors that are involved in the GA-mediated transcriptional operation of NRTs related genes. These findings revealed that GAs influenced N uptake involved in the transcriptional regulation of NRTs and physiological responses in maize responding to nitrogen supply.Mangrove sediments represent unique microbial ecosystems that act as a buffer zone, biogeochemically recycling marine waste into nutrient-rich depositions for marine and terrestrial species. Marine unicellular protists, thraustochytrids, colonizing mangrove sediments have received attention due to their ability to produce large amounts of long-chain ω3-polyunsaturated fatty acids. This paper represents a comprehensive study of two new thraustochytrids for their production of valuable biomolecules in biomass, de-oiled cakes, supernatants, extracellular polysaccharide matrixes, and recovered oil bodies. Extracted lipids (up to 40% of DW) rich in polyunsaturated fatty acids (up to 80% of total fatty acids) were mainly represented by docosahexaenoic acid (75% of polyunsaturated fatty acids). Cells also showed accumulation of squalene (up to 13 mg/g DW) and carotenoids (up to 72 µg/g DW represented by astaxanthin, canthaxanthin, echinenone, and β-carotene). Both strains showed a high concentration of protein in biomass (29% DW) and supernatants (2.7 g/L) as part of extracellular polysaccharide matrixes. Alkalinization of collected biomass represents a new and easy way to recover lipid-rich oil bodies in the form of an aqueous emulsion. The ability to produce added-value molecules makes thraustochytrids an important alternative to microalgae and plants dominating in the food, pharmacological, nutraceutical, and cosmetics industries.Respiratory syncytial virus (RSV) is one of the major causes of acute lower respiratory tract infection worldwide. The absence of a commercial vaccine and the limited success of current therapeutic strategies against RSV make further research necessary. We used a multi-cohort analysis approach to investigate host transcriptomic biomarkers and shed further light on the molecular mechanism underlying RSV-host interactions. We meta-analyzed seven transcriptome microarray studies from the public Gene Expression Omnibus (GEO) repository containing a total of 922 samples, including RSV, healthy controls, coronaviruses, enteroviruses, influenzas, rhinoviruses, and coinfections, from both adult and pediatric patients. We identified > 1500 genes differentially expressed when comparing the transcriptomes of RSV-infected patients against healthy controls. Functional enrichment analysis showed several pathways significantly altered, including immunologic response mediated by RSV infection, pattern recognition receptors, cell cycle, and olfactory signaling. In addition, we identified a minimal 17-transcript host signature specific for RSV infection by comparing transcriptomic profiles against other respiratory viruses. These multi-genic signatures might help to investigate future drug targets against RSV infection.The timing and duration of flowering are key agronomic traits that are often associated with the ability of a variety to escape abiotic stress such as heat and drought. Flowering information is valuable in both plant breeding and agricultural production management. Visual assessment, the standard protocol used for phenotyping flowering, is a low-throughput and subjective method. In this study, we evaluated multiple imaging sensors (RGB and multiple multispectral cameras), image resolution (proximal/remote sensing at 1.6 to 30 m above ground level/AGL), and image processing (standard and unsupervised learning) techniques in monitoring flowering intensity of four cool-season crops (canola, camelina, chickpea, and pea) to enhance the accuracy and efficiency in quantifying flowering traits. The features (flower area, percentage of flower area with respect to canopy area) extracted from proximal (1.6-2.2 m AGL) RGB and multispectral (with near infrared, green and blue band) image data were strongly correlated (r up to 0.