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Institution

University of California, Davis

EducationDavis, California, United States
About: University of California, Davis is a education organization based out in Davis, California, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 78770 authors who have published 180033 publications receiving 8064158 citations. The organization is also known as: UC Davis & UCD.
Topics: Population, Poison control, Gene, Galaxy, Genome


Papers
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Journal ArticleDOI
TL;DR: It is shown that an important asymmetry between altruistic cooperation and altruistic punishment allows altruistic punished to evolve in populations engaged in one-time, anonymous interactions, and this process allows both altruism punishment and altruism cooperation to be maintained even when groups are large.
Abstract: Both laboratory and field data suggest that people punish noncooperators even in one-shot interactions. Although such “altruistic punishment” may explain the high levels of cooperation in human societies, it creates an evolutionary puzzle: existing models suggest that altruistic cooperation among nonrelatives is evolutionarily stable only in small groups. Thus, applying such models to the evolution of altruistic punishment leads to the prediction that people will not incur costs to punish others to provide benefits to large groups of nonrelatives. However, here we show that an important asymmetry between altruistic cooperation and altruistic punishment allows altruistic punishment to evolve in populations engaged in one-time, anonymous interactions. This process allows both altruistic punishment and altruistic cooperation to be maintained even when groups are large and other parameter values approximate conditions that characterize cultural evolution in the small-scale societies in which humans lived for most of our prehistory.

1,514 citations

Journal ArticleDOI
TL;DR: In this article, an approach to smoothing and forecasting for time series with missing observations is proposed, where the EM algorithm is used in conjunction with the conventional Kalman smoothed estimators to derive a simple recursive procedure for estimating the parameters.
Abstract: . An approach to smoothing and forecasting for time series with missing observations is proposed. For an underlying state-space model, the EM algorithm is used in conjunction with the conventional Kalman smoothed estimators to derive a simple recursive procedure for estimating the parameters by maximum likelihood. An example is given which involves smoothing and forecasting an economic series using the maximum likelihood estimators for the parameters.

1,513 citations

Journal ArticleDOI
TL;DR: A randomized, double-blind trial in patients with sepsis and a presumed diagnosis of gram-negative infection was conducted in this article, where the patients received either a single 100mg intravenous dose of HA-1A or placebo.
Abstract: Background HA-1A is a human monoclonal IgM antibody that binds specifically to the lipid A domain of endotoxin and prevents death in laboratory animals with gram-negative bacteremia and endotoxemia Methods To evaluate the efficacy and safety of HA-1A, we conducted a randomized, double-blind trial in patients with sepsis and a presumed diagnosis of gram-negative infection The patients received either a single 100-mg intravenous dose of HA-1A(in 35 g of albumin) or placebo (35 g of albumin) Other interventions, including the administration of antibiotics and fluids, were not affected by the study protocol Results Of 543 patients with sepsis who were treated, 200 (37 percent) had gram-negative bacteremia as proved by blood culture For the patients with gram-negative bacteremia followed to death or day 28, there were 45 deaths among the 92 recipients of placebo (49 percent) and 32 deaths among the 105 recipients of HA-1A (30 percent; P = 0014) For the patients with gram-negative bacteremia and sho

1,512 citations

Journal ArticleDOI
TL;DR: The results demonstrate that MAKER provides a simple and effective means to convert a genome sequence into a community-accessible genome database, and should prove especially useful for emerging model organism genome projects for which extensive bioinformatics resources may not be readily available.
Abstract: We have developed a portable and easily configurable genome annotation pipeline called MAKER. Its purpose is to allow investigators to independently annotate eukaryotic genomes and create genome databases. MAKER identifies repeats, aligns ESTs and proteins to a genome, produces ab initio gene predictions, and automatically synthesizes these data into gene annotations having evidence-based quality indices. MAKER is also easily trainable: Outputs of preliminary runs are used to automatically retrain its gene-prediction algorithm, producing higher-quality gene-models on subsequent runs. MAKER’s inputs are minimal, and its outputs can be used to create a GMOD database. Its outputs can also be viewed in the Apollo Genome browser; this feature of MAKER provides an easy means to annotate, view, and edit individual contigs and BACs without the overhead of a database. As proof of principle, we have used MAKER to annotate the genome of the planarian Schmidtea mediterranea and to create a new genome database, SmedGD. We have also compared MAKER’s performance to other published annotation pipelines. Our results demonstrate that MAKER provides a simple and effective means to convert a genome sequence into a community-accessible genome database. MAKER should prove especially useful for emerging model organism genome projects for which extensive bioinformatics resources may not be readily available.

1,503 citations

Journal ArticleDOI
TL;DR: The observed diversity of these NBS-LRR proteins indicates the variety of recognition molecules available in an individual genotype to detect diverse biotic challenges.
Abstract: The Arabidopsis genome contains ∼200 genes that encode proteins with similarity to the nucleotide binding site and other domains characteristic of plant resistance proteins. Through a reiterative process of sequence analysis and reannotation, we identified 149 NBS-LRR–encoding genes in the Arabidopsis (ecotype Columbia) genomic sequence. Fifty-six of these genes were corrected from earlier annotations. At least 12 are predicted to be pseudogenes. As described previously, two distinct groups of sequences were identified: those that encoded an N-terminal domain with Toll/Interleukin-1 Receptor homology (TIR-NBS-LRR, or TNL), and those that encoded an N-terminal coiled-coil motif (CC-NBS-LRR, or CNL). The encoded proteins are distinct from the 58 predicted adapter proteins in the previously described TIR-X, TIR-NBS, and CC-NBS groups. Classification based on protein domains, intron positions, sequence conservation, and genome distribution defined four subgroups of CNL proteins, eight subgroups of TNL proteins, and a pair of divergent NL proteins that lack a defined N-terminal motif. CNL proteins generally were encoded in single exons, although two subclasses were identified that contained introns in unique positions. TNL proteins were encoded in modular exons, with conserved intron positions separating distinct protein domains. Conserved motifs were identified in the LRRs of both CNL and TNL proteins. In contrast to CNL proteins, TNL proteins contained large and variable C-terminal domains. The extant distribution and diversity of the NBS-LRR sequences has been generated by extensive duplication and ectopic rearrangements that involved segmental duplications as well as microscale events. The observed diversity of these NBS-LRR proteins indicates the variety of recognition molecules available in an individual genotype to detect diverse biotic challenges.

1,503 citations


Authors

Showing all 79538 results

NameH-indexPapersCitations
Eric S. Lander301826525976
Ronald C. Kessler2741332328983
George M. Whitesides2401739269833
Ronald M. Evans199708166722
Virginia M.-Y. Lee194993148820
Scott M. Grundy187841231821
Julie E. Buring186950132967
Patrick O. Brown183755200985
Anil K. Jain1831016192151
John C. Morris1831441168413
Douglas R. Green182661145944
John R. Yates1771036129029
Barry Halliwell173662159518
Roderick T. Bronson169679107702
Hongfang Liu1662356156290
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023262
20221,122
20218,398
20208,661
20198,165
20187,556