Understanding the molecular mechanisms of seed germination and seedling growth is vital for mining functional genes for the improvement of plant drought in a desert. Tamarix hispida is extremely resistant to drought and soil salinity perennial shrubs or trees. This study was the first to investigate the protein abundance profile of the transition process during the processes of T. hispida seed germination and seedling growth using label-free proteomics approaches. Our data suggested that asynchronous regulation of transcriptomics and proteomics occurs upon short-term seed germination and seedling growth of T. hispida. Enrichment analysis revealed that the main differentially abundant proteins had significant enrichment in stimulus response, biosynthesis, and metabolism. Two delta-1-pyrroline-5-carboxylate synthetases (P5CS), one Ycf3-interacting protein (Y3IP), one low-temperature-induced 65 kDa protein-like molecule, and four peroxidases (PRX) were involved in both water deprivation and hyperosmotic salinity responses. Through a comparative analysis of transcriptomics and proteomics, we found that proteomics may be better at studying short-term developmental processes. Our results support the existence of several mechanisms that enhance tolerance to salinity and drought stress during seedling growth in T. hispida.
has been marked as a pandemic worldwide caused by the SARS-CoV-2 virus. Different studies are being conducted with a view to preventing and lessening the infections caused by covid-19. In future, many other wind-borne diseases may also appear and even emerge as "pandemic". To prevent this, various measures should be an integral part of our daily life such as wearing face masks. It is tough to manually ensure individuals safety. The goal of this paper is to automate the process of contactless surveillance so that substantial prevention can be ensured against all kinds of wind-borne diseases. For automating the process, real time analysis and object detection is a must for which deep learning is the most efficient approach. In this paper, a deep learning model is used to check if a person takes any preventive measures. In our experimental analysis, we considered real time face mask detection as a preventive measure. We proposed a new face mask detection dataset. The accuracy of detecting a face mask along with the identity of a person achieved accuracy of 99.5%. The proposed model decreases time consumption as no human intervention is needed to check an individual person. This model helps to decrease infection risk by using a contactless automation system.
The Kansas State University (KSU) Engineering Student Council (ESC) and the College of Engineering Dean's office established DEN 015: New Student Orientation Seminar Series (NSOS) in 1994. (The course identifier DEN is assigned to all Dean of Engineering courses). Emphasis is on new student (freshman and transfer) transition to college life. Students obtain administrative, academic, and professional development information, and receive guidance on how to become a successful student. New students discuss this information in small groups with mentors (older engineering students are trained and assigned to lead discussion). Homework assignments are reviewed and discussed. Students are introduced to their assigned departments, department assemblies are explained, and advising information is provided in an effort to connect new students to their college and departments.
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