Hepatic cancer stem cells (HCSCs) are considered as main players for the hepatocellular carcinoma (HCC) initiation, metastasis, drug resistance and recurrence. There is a growing evidence supporting the down-regulated miRNAs in HCSCs as key suppressors for the stemness traits, but still more details are vague about how these miRNAs modulate the HCC development. To uncover some of these miRNA regulatory aspects in HCSC, we compiled 15 down-regulated miRNA and their validated and predicted up-regulated targets in HCSC. The targets were enriched for several cancer cell stemness hallmarks and CSC pre-metastatic niche, which support these miRNAs role in suppression of HCSCs neoplastic transformation. Further, we constructed miRNA-Transcription factor (TF) regulatory networks, which provided new insights on the role of the proposed miRNA-TF co-regulation in the cancer stemness axis and its cross talk with the surrounding microenvironment. Our analysis revealed HCSC important hubs as candidate regulators for targeting hepatic cancer stemness such as, miR-148a, miR-214, E2F family, MYC and SLC7A5. Finally, we proposed a possible model for miRNA and TF co-regulation of HCSC signaling pathways. Our study identified an HCSC signature and set bridges between the reported results to give guide for future validation of HCC therapeutic strategies avoiding drug resistance.

Satellite telemetry data plays an ever-important role in both the safety and the reliability of a satellite. These two factors are extremely significant in the field of space systems and space missions. Since it is challenging to repair space systems in orbit, health monitoring and early anomaly detection approaches are crucial for the success of space missions. A large number of efficient and accurate methods for health monitoring and anomaly detection have been proposed in aerospace systems but without showing enough concern for the patterns that can be mined from normal operational telemetry data. Concerning this, the present paper proposes DCLOP, an intelligent Deep Clustering-based Local Outlier Probabilities approach that aims at detecting anomalies alongside extracting realistic and reasonable patterns from the normal operational telemetry data. The proposed approach combines (i) a new deep clustering method that uses a dynamically weighted loss function with (ii) the adapted version of Local Outlier Probabilities based on the results of deep clustering. The DCLOP approach effectively monitors the health status of a spacecraft and detects the early warnings of its on-orbit failures. Therefore, this approach enhances the validity and accuracy of anomaly detection systems. The performance of the suggested approach is assessed using actual cube satellite telemetry data. The experimental findings prove that the suggested approach is competitive to the currently used techniques in terms of effectiveness, viability, and validity.

Terrorist bombing-induced casualties are not only related to immediate fatalities but also to structural deterioration, damage, or even collapse that might occur and may lead to tremendous loss of life. Efficient assessment of blast-induced structural damage following explosion events is becoming a growing problem in modern societies. An attempt based on machine learning is made in this study to anticipate structures’ responses and the associated structural damage to reinforced concrete (RC) buildings exposed to extremely short-duration explosive loads. A program is developed to generate a set of analytically derived data for nonlinear building models subjected to explosive loads. Common machine learning models and Python libraries were utilized during the development of our program implementation to learn from a dataset. The latter has different features or input parameters, such as the amount of explosive charge, the distance from the building, fundamental period, and the building’s mass and rigidity, as well as the soil type. Our database is thus used, along with our regression-and-classification based implementations, to generate an output index that estimates and categorizes the state of damage based on the several most-important parameters of the explosion exposure. In the input database, the state of damage, based on the values of captured damage indices, is classified into one of four cases. Our code efficiently predicts those cases using a model that learns from the database. The prediction rates of the presented model reach an overall high accuracy. Therefore, the proposed model provides an accurate prediction of the level of structural damage by using the computed damage indices.