Successful cyber-attacks are caused by the exploitation of some vulnerabilities in the software and/or hardware that exist in systems deployed in premises or the cloud. Although hundreds of vulnerabilities are discovered every year, only a small fraction of them actually become exploited, thereby there exists a severe class imbalance between the number of exploited and non-exploited vulnerabilities. The open source national vulnerability database, the largest repository to index and maintain all known vulnerabilities, assigns a unique identifier to each vulnerability. Each registered vulnerability also gets a severity score based on the impact it might inflict upon if compromised. Recent research works showed that the cvss score is not the only factor to select a vulnerability for exploitation, and other attributes in the national vulnerability database can be effectively utilized as predictive feature to predict the most exploitable vulnerabilities. Since cybersecurity management is highly resource savvy, organizations such as cloud systems will benefit when the most likely exploitable vulnerabilities that exist in their system software or hardware can be predicted with as much accuracy and reliability as possible, to best utilize the available resources to fix those first. Various existing research works have developed vulnerability exploitation prediction models by addressing the existing class imbalance based on algorithmic and artificial data resampling techniques but still suffer greatly from the overfitting problem to the major class rendering them practically unreliable. In this research, we have designed a novel cost function feature to address the existing class imbalance. We also have utilized the available large text corpus in the extracted dataset to develop a custom-trained word vector that can better capture the context of the local text data for utilization as an embedded layer in neural networks. Our developed vulnerability exploitation prediction models powered by a novel cost function and custom-trained word vector have achieved very high overall performance metrics for accuracy, precision, recall, F1-Score and AUC score with values of 0.92, 0.89, 0.98, 0.94 and 0.97, respectively, thereby outperforming any existing models while successfully overcoming the existing overfitting problem for class imbalance.
Medicinal plants continue to play an important role in modern medications and healthcare as consumers generally believe that most of them cause fewer or milder adverse effects than the conventional modern medicines. In order to use the plants as a source of medicinal agents, the bioactive compounds are usually extracted from plants. Therefore, the extraction of bioactive compounds from medicinal plants is a crucial step in producing plant-derived drugs. One of the bioactive compounds isolable from medicinal plants, orientin, is often used in various bioactivity studies due to its extensive beneficial properties. The extraction of orientin in different medicinal plants and its medicinal properties, which include antioxidant, antiaging, antiviral, antibacterial, anti-inflammation, vasodilatation and cardioprotective, radiation protective, neuroprotective, antidepressant-like, antiadipogenesis, and antinociceptive effects, are discussed in detail in this review.
Multiple myeloma is an incurable disease. Little is known about the genetic and molecular mechanisms governing the pathogenesis of multiple myeloma. The risk of multiple myeloma predispositions varies among different ethnicities. More than 50% of myeloma cases showed normal karyotypes with conventional cytogenetic analysis due to the low mitotic activity and content of plasma cells in the bone marrow. In the present study, high resolution array comparative genomic hybridization technique was used to identify copy number aberrations in 63 multiple myeloma patients of Malaysia.
Stimuli responsiveness has been an attractive feature of smart material design, wherein the chemical and physical properties of the material can be varied in response to small environmental change. Polyurethane (PU), a widely used synthetic polymer can be upgraded into a light-responsive smart polymer by introducing a light-sensitive moiety into the polymer matrix. For instance, azobenzene, spiropyran, and coumarin result in reversible light-induced reactions, while o-nitrobenzyl can result in irreversible light-induced reactions. These variations of light-stimulus properties endow PU with wide ranges of physical, mechanical, and chemical changes upon exposure to different wavelengths of light. PU responsiveness has rarely been reviewed even though it is known to be one of the most versatile polymers with diverse ranges of applications in household, automotive, electronic, construction, medical, and biomedical industries. This review focuses on the classes of light-responsive moieties used in PU systems, their synthesis, and the response mechanism of light-responsive PU-based materials, which also include dual- or multi-responsive light-responsive PU systems. The advantages and limitations of light-responsive PU are reviewed and challenges in the development of light-responsive PU are discussed.