University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Estimating the number of potential organ donors in the United States.

Abstract: background As the need for transplantable organs increases, waiting lists of patients become longer. We studied the size and composition of the national pool of brain-dead organ donors during a three-year period and, on the basis of these data, considered ways to increase the rate of donation. methods We reviewed hospital medical records of deaths occurring in the intensive care unit from 1997 through 1999 in the service areas of 36 organ-procurement organizations to identify brain-dead potential organ donors. We examined data on characteristics of the potential donors, the processes of referral to organ-procurement organizations and of requesting donations, and the hospitals. results We identified a total of 18,524 brain-dead potential organ donors during the study period. The predicted annual number of brain-dead potential organ donors is between 10,500 and 13,800. The overall consent rate (the number of families agreeing to donate divided by the number of families asked to donate) for 1997 through 1999 was 54 percent, and the overall conversion rate (the number of actual donors divided by the number of potential donors) was 42 percent. Hospitals with 150 or more beds were more likely than smaller hospitals to have potential donors and actual donors (P<0.001); 19 percent of hospitals accounted for 80 percent of all potential donors. Calculations of the number of donors per million persons in the general population did not correlate well with the performance of organ-procurement organizations as measured by the conversion rate. conclusions Lack of consent to a request for donation was the primary cause of the gap between the number of potential donors and the number of actual donors. Since potential and actual donors are highly concentrated in larger hospitals, resources invested to improve the process of obtaining consent in larger hospitals should maximize the rate of organ recovery. The performance of organ-procurement organizations can be assessed objectively through the comparison of the number of actual donors with the number of potential donors in the given service area.

Promiscuous DNA: horizontal transfer of transposable elements and why it matters for eukaryotic evolution.

Abstract: Horizontal transfer is the passage of genetic material between genomes by means other than parent-to-offspring inheritance. Although the transfer of genes is thought to be crucial in prokaryotic evolution, few instances of horizontal gene transfer have been reported in multicellular eukaryotes; instead, most cases involve transposable elements. With over 200 cases now documented, it is possible to assess the importance of horizontal transfer for the evolution of transposable elements and their host genomes. We review criteria for detecting horizontal transfers and examine recent examples of the phenomenon, shedding light on its mechanistic underpinnings, including the role of host-parasite interactions. We argue that the introduction of transposable elements by horizontal transfer in eukaryotic genomes has been a major force propelling genomic variation and biological innovation.

A Survey on Contrastive Self-supervised Learning

Abstract: Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudo labels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning methods for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we have a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make substantial progress.