Keys to academic success for under-represented minority young investigators: recommendations from the Research in Academic Pediatrics Initiative on Diversity (RAPID) National Advisory Committee.

BackgroundAlthough Latinos, African-Americans, and American Indians/Alaska Natives comprise 34% of Americans, these under-represented minorities (URMs) account for only 7% of US medical-school faculty. Even when URMs become faculty, they face many substantial challenges to success. Little has been published, however, on keys to academic success for URM young faculty investigators.MethodsThe Research in Academic Pediatrics Initiative on Diversity (RAPID) goal is to enhance the professional advancement of URM junior faculty pursuing research careers in general academic pediatrics. One important RAPID component is the annual mentoring/career-development conference, which targets URM residents, fellows, and junior faculty, and has included 62 URM participants since its 2013 inception. A conference highlight is the panel discussion on keys to academic success for URM young investigators, conducted by the RAPID National Advisory Committee, a diverse group of leading senior researchers. The article aim was to provide a guide to academic success for URM young investigators using the 2018 RAPID Conference panel discussion. A modified Delphi technique was used to provide a systematic approach to obtaining answers to six key questions using an expert panel: the single most important key to success for URM young investigators; ensuring optimal mentorship; how to respond when patients/families say, "I don't want you to see my child because you are ____"; best strategies for maximizing funding success; how to balance serving on time-consuming committees with enough time to advance research/career objectives; and the single thing you wish someone had told you which would have substantially enhanced your success early on.Results/conclusionsThis is the first published practical guide on keys to academic success for URM young investigators. Identified keys to success included having multiple mentors, writing prolifically, being tenaciously persistent, having mentors who are invested in you, dealing with families who do not want you to care for their child because of your race/ethnicity by seeking to understand the reasons and debriefing with colleagues, seeking non-traditional funding streams, balancing committee work with having enough time to advance one's research and career by using these opportunities to generate scholarly products, and asking for all needed resources when negotiating for new jobs

Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine

High-throughput DNA sequencing is revolutionizing the study of cancer and enabling the measurement of the somatic mutations that drive cancer development. However, the resulting sequencing datasets are large and complex, obscuring the clinically important mutations in a background of errors, noise, and random mutations. Here, we review computational approaches to identify somatic mutations in cancer genome sequences and to distinguish the driver mutations that are responsible for cancer from random, passenger mutations. First, we describe approaches to detect somatic mutations from high-throughput DNA sequencing data, particularly for tumor samples that comprise heterogeneous populations of cells. Next, we review computational approaches that aim to predict driver mutations according to their frequency of occurrence in a cohort of samples, or according to their predicted functional impact on protein sequence or structure. Finally, we review techniques to identify recurrent combinations of somatic mutations, including approaches that examine mutations in known pathways or protein-interaction networks, as well as de novo approaches that identify combinations of mutations according to statistical patterns of mutual exclusivity. These techniques, coupled with advances in high-throughput DNA sequencing, are enabling precision medicine approaches to the diagnosis and treatment of cancer

Prevention of mammary carcinogenesis by short-term estrogen and progestin treatments

INTRODUCTION: Women who have undergone a full-term pregnancy before the age of 20 have one-half the risk of developing breast cancer compared with women who have never gone through a full-term pregnancy. This protective effect is observed universally among women of all ethnic groups. Parity in rats and mice also protects them against chemically induced mammary carcinogenesis. METHODS: Seven-week-old virgin Lewis rats were given N-methyl-N-nitrosourea. Two weeks later the rats were treated with natural or synthetic estrogens and progestins for 7–21 days by subcutaneous implantation of silastic capsules. RESULTS: In our current experiment, we demonstrate that short-term sustained exposure to natural or synthetic estrogens along with progestins is effective in preventing mammary carcinogenesis in rats. Treatment with 30 mg estriol plus 30 mg progesterone for 3 weeks significantly reduced the incidence of mammary cancer. Short-term exposure to ethynyl estradiol plus megesterol acetate or norethindrone was effective in decreasing the incidence of mammary cancers. Tamoxifen plus progesterone treatment for 3 weeks was able to confer only a transient protection from mammary carcinogenesis, while 2-methoxy estradiol plus progesterone was effective in conferring protection against mammary cancers. CONCLUSIONS: The data obtained in the present study demonstrate that, in nulliparous rats, long-term protection against mammary carcinogenesis can be achieved by short-term treatments with natural or synthetic estrogen and progesterone combinations

To Drive or to Be Driven? The Impact of Autopilot, Navigation System, and Printed Maps on Driver’s Cognitive Workload and Spatial Knowledge

The technical advances in navigation systems should enhance the driving experience, supporting drivers’ spatial decision making and learning in less familiar or unfamiliar environments. Furthermore, autonomous driving systems are expected to take over navigation and driving in the near future. Yet, previous studies pointed at a still unresolved gap between environmental exploration using topographical maps and technical navigation means. Less is known about the impact of the autonomous system on the driver’s spatial learning. The present study investigates the development of spatial knowledge and cognitive workload by comparing printed maps, navigation systems, and autopilot in an unfamiliar virtual environment. Learning of a new route with printed maps was associated with a higher cognitive demand compared to the navigation system and autopilot. In contrast, driving a route by memory resulted in an increased level of cognitive workload if the route had been previously learned with the navigation system or autopilot. Way-finding performance was found to be less prone to errors when learning a route from a printed map. The exploration of the environment with the autopilot was not found to provide any compelling advantages for landmark knowledge. Our findings suggest long-term disadvantages of self-driving vehicles for spatial memory representations

Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic Inference Using a Delta Posterior

Although neural machine translation models reached high translation quality, the autoregressive nature makes inference difficult to parallelize and leads to high translation latency. Inspired by recent refinement-based approaches, we propose LaNMT, a latent-variable non-autoregressive model with continuous latent variables and deterministic inference procedure. In contrast to existing approaches, we use a deterministic inference algorithm to find the target sequence that maximizes the lowerbound to the log-probability. During inference, the length of translation automatically adapts itself. Our experiments show that the lowerbound can be greatly increased by running the inference algorithm, resulting in significantly improved translation quality. Our proposed model closes the performance gap between non-autoregressive and autoregressive approaches on ASPEC Ja-En dataset with 8.6x faster decoding. On WMT'14 En-De dataset, our model narrows the gap with autoregressive baseline to 2.0 BLEU points with 12.5x speedup. By decoding multiple initial latent variables in parallel and rescore using a teacher model, the proposed model further brings the gap down to 1.0 BLEU point on WMT'14 En-De task with 6.8x speedup.Comment: This paper was accepted to AAAI 2020, the copyright is transferred to AAA

Gospel Choir Spring Concert

Kennesaw State University School of Music presents Gospel Choir Spring Concert.https://digitalcommons.kennesaw.edu/musicprograms/1391/thumbnail.jp