University of Missouri

About: University of Missouri is a education organization based out in Columbia, Missouri, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 41427 authors who have published 83598 publications receiving 2911437 citations. The organization is also known as: Mizzou & Missouri-Columbia.

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Abstract: In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

Amount and distribution of 5-methylcytosine in human DNA from different types of tissues or cells

Abstract: Analysis of the total base composition of DNA from seven different normal human tissues and eight different types of homogeneous human cell populations revealed considerable tissue-specific and cell-specific differences in the extent of methylation of cytosine residues. The two most highly methylated DNAs were from thymus and brain with 1.00 and 0.98 mole percent 5-methylcytosine (m5C), respectively. The two least methylated DNAs from in vivo sources were placental DNA and sperm DNA, which had 0.76 and 0.84 mole percent m5C, respectively. The differences between these two groups of samples were significant with p less than 0.01. The m5C content of DNA from six human cell lines or strains ranged from 0.57 to 0.85 mole percent. The major and minor base composition of DNA fractionated by reassociation kinetics was also determined. The distribution of m5C among these fractions showed little or no variation with tissue or cell type with the possible exception of sperm DNA. In each case, nonrepetitive DNA sequences were hypomethylated compared to unfractionated DNA.

The Experiences in Close Relationship Scale (ECR)-Short Form: Reliability, Validity, and Factor Structure

Abstract: We developed a 12-item, short form of the Experiences in Close Relationship Scale (ECR; Brennan, Clark, & Shaver, 1998) across 6 studies. In Study 1, we examined the reliability and factor structure of the measure. In Studies 2 and 3, we cross-validated the reliability, factor structure, and validity of the short form measure; whereas in Study 4, we examined test-retest reliability over a 1-month period. In Studies 5 and 6, we further assessed the reliability, factor structure, and validity of the short version of the ECR when administered as a stand-alone instrument. Confirmatory factor analyses indicated that 2 factors, labeled Anxiety and Avoidance, provided a good fit to the data after removing the influence of response sets. We found validity to be equivalent for the short and the original versions of the ECR across studies. Finally, the results were comparable when we embedded the short form within the original version of the ECR and when we administered it as a stand-alone measure.