An Efficient and Accurate Super-Resolution Approach to Low-Field MRI via U-Net Architecture With Logarithmic Loss and L2 Regularization
Low-field (LF) MRI scanners have the power to revolutionize medical imaging by provid- 27 ing a portable and cheaper alternative to high-field MRI scanners. However, such scanners are usu- 28 ally significantly noisier and lower quality than their high-field counterparts. This prevents them 29 from appealing to global markets. The aim of this paper is to improve the SNR and overall image quality of low-field MRI scans (called super-resolution) to improve diagnostic capability and, as a result, make it more accessible. To address this issue, we propose a Nested U-Net neural network architecture super-resolution algorithm that outperforms previously suggested super-resolution deep learning methods with an average PSNR of 78.83 ± 0.01 and SSIM of 0.9551 ± 0.01. Our ANOVA paired t-test and Post-Hoc Tukey test demonstrate significance with a p-value < 0.0001 and no other network demonstrating significance higher than 0.1. We tested our network on artificial noisy downsampled synthetic data from 1500 T1 weighted MRI images through the dataset called the T1- mix. Four board-certified radiologists scored 25 images (100 image ratings total) on the Likert scale (1-5) assessing overall image quality, anatomical structure, and diagnostic confidence across our architecture and other published works (SR DenseNet, Generator Block, SRCNN, etc.). Our algo- rithm outperformed all other works with the highest MOS, 4.4 ± 0.3. We also introduce a new type of loss function called natural log mean squared error (NLMSE), outperforming MSE, MAE, and MSLE on this specific SR task. Additionally, we ran inference on actual Hyperfine scan images with successful qualitative results using a Generator RRDB block. In conclusion, we present a more ac- curate deep learning method for single image super-resolution applied to low-field MRI via a 45 Nested U-Net architecture.
Analysis on a New Electric Field Measurement Method Using Ionic Propulsion Propeller
Given the high sensitivity of electronic instruments, electromagnetic field intensity measuring is now becoming an essential part of the industry. Current electric field intensity meters are unfit for individual use and focus mainly on electromagnetic radiation rather than the field itself. In ionic propulsion, the propulsion force is proportional to electric field intensity but the use of this property on measurement remains largely unexplored. Here, our team investigates ionic propulsion in electric fields generated by electro-static methods and then systematically varies the point of measurement inside the field, thereby altering the intensity of the field without focusing on electromagnetic radiation. By combining the Van de graaff generator with an adjustable ionic thrust propeller, we find that the propeller speed which is proportional to the electric field is directly determined by the electric field intensity. Furthermore, we applied stroboscopy to the system to measure RPM, and have achieved the direct interaction between field intensity and RPM, which could be a new meter for field intensity measurement.
Study of regenerative and ontogenetic processes under the influence of EHF EMR.
The increased sensitivity of aquatic organisms to the effects of EMF has been proven by numerous experimental studies. It has been repeatedly noted that exposure to EMF of certain frequencies and intensities leads to disruption of physiological functions, orientation in time and space, changes in the behavior of organisms, suppression of motor activity. Other ranges of electromagnetic radiation, on the contrary, can cause the effects of increased regeneration, growth rate and survival. In connection with these trends, the purpose of our research is to analyze the effects of the influence of electromagnetic radiation of extremely high frequency on the development of the Xenopus laevis and the regeneration of newts and planarians
In silico identification and physicochemical analysis of potential novel antimicrobial peptides from Momordica charantia L.
The emergence of antibacterial resistance has necessitated the development of alternative treatments, such as antimicrobial peptides (AMPs). AMPs are part of the innate immune systems of various organisms such as Momordica charantia L., a known medicinal plant in Southeast Asia. In this study, potential novel AMPs from M. charantia were derived in silico to provide prospective antibiotic alternatives using promising plant-based peptides. M. charantia protein sequences that were 500 amino acids long were digested using proteolytic enzymes, resulting in 3,621 peptides. Each resulting sequence was characterized as either AMP or Non-AMP using four statistical analysis tools, and those identified as AMPs were analyzed. This led to 102 AMPs, 53 of which were unregistered on the Data Repository for Antimicrobial Peptides, indicating that they have yet to be derived from other species. Six of the eight studied physicochemical properties show strong correlations with each other, suggesting that subsequent AMP design studies may focus on these six properties. As such, M. charantia may be a rich source of potential AMPs and, thereby, alternative antibiotics. The in vitro examination of these novel AMPs is also recommended to further understand their potential as alternative antibiotics sourced from locally available plants.
Conscious Brain Mind-Controlled Cybonthitic Cyborg Bionic-Leg - V2
Lower limb amputations affect about 28.9 million people worldwide, influencing normal human functions, we are developing a conscious brain mind-controlled Cybonthitic cyborg bionic-leg to provide a professional solution for this problem, which is classified as restricted knee movement, short-term solution, limited pressure bearing, unspecific analog reading of EMG; Because the output voltage measured in nano-volts, resulting in unspecific knee movement. The functionality of these modern gadgets is still limited due to a lack of neuromuscular control (i.e. For movement creation, control relies on human efferent neural signals to peripheral muscles). Electromyographic (EMG) or myoelectric signals are neuromuscular control signals that can be recorded from muscles for our engineering goals. We worked on a sophisticated prosthetic knee design with a 100-degree angle of motion. We also used a specific type of coiled spring to absorb abrupt or unexpected motion force. In addition, we amplified the EMG output from (Nano-Voltage) to (Milli-Voltage) using customized instrumentation amplifiers (operational amplifiers). We used a full-wave rectifier to convert AC to DC, as a consequence of these procedures, sine-wave output voltage measures in millivolts, and the spring constant indicates the most force for every 1cm. Von mises Stress analysis shows bearing as 3000N is the maximum load for the design. Detecting the edge of a stairwell using the first derivative. The benefit of a system that controls the prosthetic limb is activated by the patient’s own EMG impulses, rather than sensors linked to the body.