University of California, Irvine

About: University of California, Irvine is a education organization based out in Irvine, California, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 47031 authors who have published 113602 publications receiving 5521832 citations. The organization is also known as: UC Irvine & UCI.

Functional- and abundance-based mechanisms explain diversity loss due to N fertilization.

Abstract: Human activities have increased N availability dramatically in terrestrial and aquatic ecosystems. Extensive research demonstrates that local plant species diversity generally declines in response to nutrient enrichment, yet the mechanisms for this decline remain unclear. Based on an analysis of >900 species responses from 34 N-fertilization experiments across nine terrestrial ecosystems in North America, we show that both trait-neutral and trait-based mechanisms operate simultaneously to influence diversity loss as production increases. Rare species were often lost because of soil fertilization, randomly with respect to traits. The risk of species loss due to fertilization ranged from >60% for the rarest species to 10% for the most abundant species. Perennials, species with N-fixing symbionts, and those of native origin also experienced increased risk of local extinction after fertilization, regardless of their initial abundance. Whereas abundance was consistently important across all systems, functional mechanisms were often system-dependent. As N availability continues to increase globally, management that focuses on locally susceptible functional groups and generally susceptible rare species will be essential to maintain biodiversity.

Harnessing neuroplasticity for clinical applications

Abstract: Neuroplasticity can be defined as the ability of the nervous system to respond to intrinsic or extrinsic stimuli by reorganizing its structure, function and connections. Major advances in the understanding of neuroplasticity have to date yielded few established interventions. To advance the translation of neuroplasticity research towards clinical applications, the National Institutes of Health Blueprint for Neuroscience Research sponsored a workshop in 2009. Basic and clinical researchers in disciplines from central nervous system injury/stroke, mental/addictive disorders, paediatric/developmental disorders and neurodegeneration/ageing identified cardinal examples of neuroplasticity, underlying mechanisms, therapeutic implications and common denominators. Promising therapies that may enhance training-induced cognitive and motor learning, such as brain stimulation and neuropharmacological interventions, were identified, along with questions of how best to use this body of information to reduce human disability. Improved understanding of adaptive mechanisms at every level, from molecules to synapses, to networks, to behaviour, can be gained from iterative collaborations between basic and clinical researchers. Lessons can be gleaned from studying fields related to plasticity, such as development, critical periods, learning and response to disease. Improved means of assessing neuroplasticity in humans, including biomarkers for predicting and monitoring treatment response, are needed. Neuroplasticity occurs with many variations, in many forms, and in many contexts. However, common themes in plasticity that emerge across diverse central nervous system conditions include experience dependence, time sensitivity and the importance of motivation and attention. Integration of information across disciplines should enhance opportunities for the translation of neuroplasticity and circuit retraining research into effective clinical therapies.

PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies

Abstract: A new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides daily and 0.25° rainfall estimates for the latitude band 60°S–60°N for the period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data. It is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5° monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset ...