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Cube Satellites, aka CubeSats, are a class of nano satellites that have gained popularity recently, especially for those that consider CubeSats as an emerging alternative to conventional satellites for space programs. This is because they are cost-effective, and they can be built using commercial off-the-shelf components. Moreover, CubeSats can communicate with each other in space and ground stations to carry out many functions such as remote sensing (e.g., land imaging, education), space research, wide area measurements and deep space communications. Consequently, communications between CubeSats and ground stations is critical. Any antenna design for a CubeSat needs to meet size and weight restrictions while yielding good antenna radiation performance. To date, a limited number of works have surveyed, compared and categorised the proposed antenna designs for CubeSats based on their operating frequency bands. To this end, this paper contributes to the literature by focusing on different antenna types with different operating frequency bands that are proposed for CubeSat applications. This paper reviews 48 antenna designs, which include 18 patch antennas, 5 slot antennas, 4 dipole and monopole antennas, 3 reflector antennas, 3 reflectarray antennas, 5 helical antennas, 2 metasurface antennas and 3 millimeter and sub-millimeter wave antennas. The current CubeSat antenna design challenges and design techniques to address these challenges are discussed. In addition, we classify these antennas according to their operating frequency bands, e.g., VHF, UHF, L, S, C, X, Ku, K/Ka, W and mm/sub-mm wave bands and provide an extensive qualitative comparison in terms of their size, −10 dB bandwidths, gains, reflection coefficients, and deployability. The suitability of different antenna types for different applications as well as the future trends for CubeSat antennas are also presented.

Traumatic brain injury (TBI) induces a series of epigenetic changes in brain tissue, among which histone modifications are associated with the deterioration of TBI. In this study, we explored the role of histone H3 modifications in a weight-drop model of TBI in rats. Screening for various histone modifications, immunoblot analyses revealed that the phosphorylation of histone H3 serine 10 (p-H3S10) was significantly upregulated after TBI in the brain tissue surrounding the injury site. A similar posttraumatic regulation was observed for phosphorylated extracellular signal-regulated kinase (p-ERK), which is known to phosphorylate H3S10. In support of the hypothesis that ERK-mediated phosphorylation of H3S10 contributes to TBI pathogenesis, double immunofluorescence staining of brain sections showed high levels and colocalization of p-H3S10 and p-ERK predominantly in neurons surrounding the injury site. To test the hypothesis that inhibition of ERK-H3S10 signaling ameliorates TBI pathogenesis, the mitogen-activated protein kinase–extracellular signal-regulated kinase kinase (MEK) 1/2 inhibitor U0126, which inhibits ERK phosphorylation, was administered into the right lateral ventricle of TBI male and female rats via intracerebroventricular cannulation for 7 days post trauma. U0126 administration indeed prevented H3S10 phosphorylation and improved motor function recovery and cognitive function compared to vehicle treatment. In agreement with our findings in the rat model of TBI, immunoblot and double immunofluorescence analyses of brain tissue specimens from patients with TBI demonstrated high levels and colocalization of p-H3S10 and p-ERK as compared to control specimens from non-injured individuals. In conclusion, our findings indicate that phosphorylation-dependent activation of ERK-H3S10 signaling participates in the pathogenesis of TBI and can be targeted by pharmacological approaches.

COVID-19 disease has been taking away millions of lives from the earth since 2020. Thousands of people have lost their lives because of the shortage of limited medical resources. To tackle this urgent issue, this paper proposed a CNN classification model based on application to help a patient determine if COVID-19 infects lung CT scan of the patient. The application contains a user interface based on the Flask framework, combined with a trained CNN model, which will proceed with the CT scan uploaded by the patient and inform the patient of the result. Additionally, the application simulates a personal virtual assistant, which can be utilized to answer patients' queries. The personal virtual assistant, also known as a chatbot, is made with Natural Language Processing (NLP), which will transfer the patient's input into tokens and return the most likely response to the patient. Unfortunately, after constructing the CNN model, the prediction was not highly accurate. The experimental result demonstrated that the loss value is generally decreasing throughout the training process, and the accuracy score is generally increasing. However, the overall performance was not smooth, which means the model was unstable. The reason that causes this problem is the shortage of the training dataset.