Search

Adversarial attacks have received much attention as communication network applications rise in popularity. Connected and Automated Vehicles (CAVs) must be protected against adversarial attacks to ensure passenger and vehicle safety on the road. Nevertheless, CAVs are susceptible to several types of attacks, such as those that target intra- and inter-vehicle networks. These harmful attacks not only cause user privacy and confidentiality to be lost, but they also have more grave repercussions, such as physical harm and death. It is critical to precisely and quickly identify adversarial attacks to protect CAVs. This research proposes (1) a new dataset comprising three adversarial attacks in the CAV network traffic and normal traffic, (2) a two-phased adversarial attack detection technique named TAAD-CAV, where in the first phase, an ensemble voting classifier having three machine learning classifiers and one separate deep learning classifier is trained, and the output is used in the next phase. In the second phase, a meta classifier (i.e., Decision Tree is used as a meta classifier) is trained on the combined predictions from the previous phase to detect adversarial attacks. We preprocess the dataset by cleaning data, removing missing values, and adjusting the Z-score normalization. Evaluation metrics such as accuracy, recall, precision, F1-score, and confusion matrix are employed to evaluate and compare the performance of the proposed model. Results reveal that TAAD-CAV achieves the highest accuracy with a value of 70% compared with individual ML and DL classifiers. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is an infectious disease caused by Mycobacterium. It primarily impacts the lungs but can also endanger other organs, such as the renal system, spine, and brain. When an infected individual sneezes, coughs, or speaks, the virus can spread through the air, which contributes to its high contagiousness. The goal is to enhance detection recognition with an X-ray image dataset. This paper proposed a novel approach, named the Tuberculosis Segmentation-Guided Diagnosis Model (TSSG-CNN) for Detecting Tuberculosis, using a combined semantic segmentation and adaptive convolutional neural network (CNN) architecture. The proposed approach is distinguished from most of the previously proposed approaches in that it uses the combination of a deep learning segmentation model with a follow-up classification model based on CNN layers to segment chest X-ray images more precisely as well as to improve the diagnosis of TB. It contrasts with other approaches like ILCM, which is optimized for sequential learning, and explainable AI approaches, which focus on explanations. Moreover, our model is beneficial for the simplified procedure of feature optimization from the perspectives of approach using the Mayfly Algorithm (MA). Other models, including simple CNN, Batch Normalized CNN (BN-CNN), and Dense CNN (DCNN), are also evaluated on this dataset to evaluate the effectiveness of the proposed approach. The performance of the TSSG-CNN model outperformed all the models with an impressive accuracy of 98.75% and an F1 score of 98.70%. The evaluation findings demonstrate how well the deep learning segmentation model works and the potential for further research. The results suggest that this is the most accurate strategy and highlight the potential of the TSSG-CNN Model as a useful technique for precise and early diagnosis of TB. [ABSTRACT FROM AUTHOR]

The hydrographic variability in the fjords of Svalbard significantly influences water mass properties, causing distinct patterns of microbial diversity and community composition between surface and subsurface layers. However, surveys on the phytoplankton-associated bacterial communities, pivotal to ecosystem functioning in Arctic fjords, are limited. This study investigated the interactions between phytoplankton and heterotrophic bacterial communities in Svalbard fjord waters through comprehensive eDNA metabarcoding with 16S and 18S rRNA genes. The 16S rRNA sequencing results revealed a homogenous community composition including a few dominant heterotrophic bacteria across fjord waters, whereas 18S rRNA results suggested a spatially diverse eukaryotic plankton distribution. The relative abundances of heterotrophic bacteria showed a depth-wise distribution. By contrast, the dominant phytoplankton populations exhibited variable distributions in surface waters. In the network model, the linkage of phytoplankton (Prasinophytae and Dinophyceae) to heterotrophic bacteria, particularly Actinobacteria, suggested the direct or indirect influence of bacterial contributions on the fate of phytoplankton-derived organic matter. Our prediction of the metabolic pathways for bacterial activity related to phytoplankton-derived organic matter suggested competitive advantages and symbiotic relationships between phytoplankton and heterotrophic bacteria. Our findings provide valuable insights into the response of phytoplankton-bacterial interactions to environmental changes in Arctic fjords. [ABSTRACT FROM AUTHOR]

Iris bungei (Iridaceae) collected in Mongolia was subjected to bioassay-directed isolation using a tyrosinase inhibition assay. A dimeric stilbene (1), two phenylpropanoid glycosides (2 , 3), and a flavone glycoside (5), along with fifteen known secondary metabolites (4 , 6 19) were isolated from the methanolic extract of the whole plant. The structures of the new metabolites were elucidated by 1D and 2D NMR spectroscopic and chemical methods. Among the isolated compounds, the new triferuloyl glycosides (2 and 3) exhibited significant tyrosinase inhibitory effects with IC 50 values of 16.4 ± 0.5 and 18.3 ± 0.7 μM, respectively. [Display omitted] • A dimeric stilbene, two phenylpropanoid glycosides, and a flavone glycoside isolated from Iris bungei. • New structures were elucidated by analysis of spectroscopic and chemical methods. • Two new triferuloyl glycosides exhibited significant tyrosinase inhibitory effects. [ABSTRACT FROM AUTHOR]

Studying cancer metabolism gives insight into tumorigenic survival mechanisms and susceptibilities. In melanoma, we identify HEXIM1, a transcription elongation regulator, as a melanoma tumor suppressor that responds to nucleotide stress. HEXIM1 expression is low in melanoma. Its overexpression in a zebrafish melanoma model suppresses cancer formation, while its inactivation accelerates tumor onset in vivo. Knockdown of HEXIM1 rescues zebrafish neural crest defects and human melanoma proliferation defects that arise from nucleotide depletion. Under nucleotide stress, HEXIM1 is induced to form an inhibitory complex with P-TEFb, the kinase that initiates transcription elongation, to inhibit elongation at tumorigenic genes. The resulting alteration in gene expression also causes anti-tumorigenic RNAs to bind to and be stabilized by HEXIM1. HEXIM1 plays an important role in inhibiting cancer cell-specific gene transcription while also facilitating anti-cancer gene expression. Our study reveals an important role for HEXIM1 in coupling nucleotide metabolism with transcriptional regulation in melanoma.