62 research outputs found

    Identification of long non-protein coding RNAs in chicken skeletal muscle using next generation sequencing

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    AbstractVertebrate genomes encode thousands of non-coding RNAs including short non-coding RNAs (such as microRNAs) and long non-coding RNAs (lncRNAs). Chicken (Gallus gallus) is an important model organism for developmental biology, and the recently assembled genome sequences for chicken will facilitate the understanding of the functional roles of non-coding RNA genes during development. The present study concerns the first systematic identification of lncRNAs using RNA-Seq to sample the transcriptome during chicken muscle development. A computational approach was used to identify 281 new intergenic lncRNAs in the chicken genome. Novel lncRNAs in general are less conserved than protein-coding genes and slightly more conserved than random non-coding sequences. The present study has provided an initial chicken lncRNA catalog and greatly increased the number of chicken ncRNAs in the non-protein coding RNA database. Furthermore, the computational pipeline presented in the current work will be useful for characterizing lncRNAs obtained from deep sequencing data

    Tracking Cyber Adversaries with Adaptive Indicators of Compromise

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    A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US

    A Riemann solver at a junction compatible with a homogenization limit

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    We consider a junction regulated by a traffic lights, with n incoming roads and only one outgoing road. On each road the Phase Transition traffic model, proposed in [6], describes the evolution of car traffic. Such model is an extension of the classic Lighthill-Whitham-Richards one, obtained by assuming that different drivers may have different maximal speed. By sending to infinity the number of cycles of the traffic lights, we obtain a justification of the Riemann solver introduced in [9] and in particular of the rule for determining the maximal speed in the outgoing road.Comment: 19 page

    YOLO-SCL: a lightweight detection model for citrus psyllid based on spatial channel interaction

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    Efficient and accurate detection and providing early warning for citrus psyllids is crucial as they are the primary vector of citrus huanglongbing. In this study, we created a dataset comprising images of citrus psyllids in natural environments and proposed a lightweight detection model based on the spatial channel interaction. First, the YOLO-SCL model was based on the YOLOv5s architecture, which uses an efficient channel attention module to perform local channel attention on the inputs in the recursive gated convolutional modules to achieve a combination of global spatial and local channel interactions, improving the model’s ability to express the features of the critical regions of small targets. Second, the lightweight design of the 21st layer C3 module in the neck network of the YOLO-SCL model and the small target feature information were retained to the maximum extent by deleting the two convolutional layers, whereas the number of parameters was reduced to improve the detection accuracy of the model. Third, with the detection accuracy of the YOLO-SCL model as the objective function, the black widow optimization algorithm was used to optimize the hyperparameters of the YOLO-SCL model, and the iterative mechanism of swarm intelligence was used to further improve the model performance. The experimental results showed that the YOLO-SCL model achieved a [email protected] of 97.07% for citrus psyllids, which was 1.18% higher than that achieved using conventional YOLOv5s model. Meanwhile, the number of parameters and computation amount of the YOLO-SCL model are 6.92 M and 15.5 GFlops, respectively, which are 14.25% and 2.52% lower than those of the conventional YOLOv5s model. In addition, after using the black widow optimization algorithm to optimize the hyperparameters, the [email protected] of the YOLO-SCL model for citrus psyllid improved to 97.18%, making it more suitable for the natural environments in which citrus psyllids are to be detected. The experimental results showed that the YOLO-SCL model has good detection accuracy for citrus psyllids, and the model was ported to the Jetson AGX Xavier edge computing platform, with an average processing time of 38.8 ms for a single-frame image and a power consumption of 16.85 W. This study provides a new technological solution for the safety of citrus production

    Identification of floR Variants Associated With a Novel Tn4371-Like Integrative and Conjugative Element in Clinical Pseudomonas aeruginosa Isolates

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    Florfenicol is widely used to control respiratory diseases and intestinal infections in food animals. However, there are increasing reports about florfenicol resistance of various clinical pathogens. floR is a key resistance gene that mediates resistance to florfenicol and could spread among different bacteria. Here, we investigated the prevalence of floR in 430 Pseudomonas aeruginosa isolates from human clinical samples and identified three types of floR genes (designated floR, floR-T1 and floR-T2) in these isolates, with floR-T1 the most prevalent (5.3%, 23/430). FloR-T2 was a novel floR variant identified in this study, and exhibited less identity with other FloR proteins than FloRv. Moreover, floR-T1 and floR-T2 identified in P. aeruginosa strain TL1285 were functionally active and located on multi-drug resistance region of a novel incomplete Tn4371-like integrative and conjugative elements (ICE) in the chromosome. The expression of the two floR variants could be induced by florfenicol or chloramphenicol. These results indicated that the two floR variants played an essential role in the host’s resistance to amphenicol and the spreading of these floR variants might be related with the Tn4371 family ICE
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