State University of New York System

About: State University of New York System is a education organization based out in Albany, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 54077 authors who have published 78070 publications receiving 2985160 citations.

Eukaryotic ribosomes require initiation factors 1 and 1A to locate initiation codons

Abstract: The scanning model of translation initiation is a coherent description of how eukaryotic ribosomes reach the initiation codon after being recruited to the capped 5' end of messenger RNA. Five eukaryotic initiation factors (eIF 2, 3, 4A, 4B and 4F) with established functions have been assumed to be sufficient to mediate this process. Here we report that eIF1 and eIF1A are also both essential for translation initiation. In their absence, 43S ribosomal preinitiation complexes incubated with ATP, eIF4A, eIF4B and eIF4F bind exclusively to the cap-proximal region but are unable to reach the initiation codon. Individually, eIF1A enhances formation of this cap-proximal complex, and eIF1 weakly promotes formation of a 48S ribosomal complex at the initiation codon. These proteins act synergistically to mediate assembly of ribosomal initiation complexes at the initiation codon and dissociate aberrant complexes from the mRNA.

Patients' Understanding of Their Treatment Plans and Diagnosis at Discharge

Abstract: Objective To ascertain whether patients at discharge from a municipal teaching hospital knew their discharge diagnoses, treatment plan (names and purpose of their medications), and common side effects of prescribed medications. Patients and methods From July to October 1999, we surveyed 47 consecutive patients at discharge from the medical service of a municipal teaching hospital in New York City (Brooklyn, NY). Patients were asked to state either the trade or the generic name(s) of their medication(s), their purpose, and the major side effect(s), as well as their discharge diagnoses. Patients were excluded if they were not oriented to person, place, and time, were unaware of the circumstances surrounding their admission to the hospital, and/or did not speak or understand English. Results Of the 47 patients surveyed, 4 were excluded. Of the remaining 43 patients, 12 (27.9%) were able to list all their medications, 16 (37.2%) were able to recount the purpose of all their medications, 6 (14.0%) were able to state the common side effect(s) of all their medications, and 18 (41.9%) were able to state their diagnosis or diagnoses. The mean number of medications prescribed at discharge was 3.89. Conclusions Less than half of our study patients were able to list their diagnoses, the name(s) of their medication(s), their purpose, or the major side effect(s). Lacking awareness of these factors affects a patient's ability to comply fully with discharge treatment plans. Whether lack of communication between physician and patient is actually the cause of patient unawareness of discharge Instructions or if this even affects patient outcome requires further study.

Connected sensor cover: self-organization of sensor networks for efficient query execution

Abstract: Spatial query execution is an essential functionality of a sensor network, where a query gathers sensor data within a specific geographic region. Redundancy within a sensor network can be exploited to reduce the communication cost incurred in execution of such queries. Any reduction in communication cost would result in an efficient use of the battery energy, which is very limited in sensors. One approach to reduce the communication cost of a query is to self-organize the network, in response to a query, into a topology that involves only a small subset of the sensors sufficient to process the query. The query is then executed using only the sensors in the constructed topology. The self-organization technique is beneficial for queries that run sufficiently long to amortize the communication cost incurred in self-organization. In this paper, we design and analyze algorithms for suchself-organization of a sensor network to reduce energy consumption. In particular, we develop the notion of a connected sensor cover and design a centralized approximation algorithm that constructs a topology involving a near-optimal connected sensor cover. We prove that the size of the constructed topology is within an O(logn) factor of the optimal size, where n is the network size. We develop a distributed self-organization version of the approximation algorithm, and propose several optimizations to reduce the communication overhead of the algorithm. We also design another distributed algorithm based on node priorities that has a further lower communication overhead, but does not provide any guarantee on the size of the connected sensor cover constructed. Finally, we evaluate the distributed algorithms using simulations and show that our approaches results in significant communication cost reductions.