14-Year Outcome of Angle-Closure Prevention with Laser Iridotomy in the Zhongshan Angle Closure Prevention Study: Extended Follow-Up of a Randomized Controlled Trial

Purpose: This study aimed to evaluate the efficacy of laser peripheral iridotomy (LPI) prophylaxis for primary angle closure suspects (PACS) after 14 years and to identify risk factors for the conversion from PACS to primary angle closure (PAC)./ Design: An extended follow-up of Zhongshan Angle Closure Prevention (ZAP) study./ Participants: A total of 889 Chinese patients aged 50 to 70 years with bilateral PACS./ Methods: Each patient received LPI in one randomly selected eye, with the fellow untreated eye serving as a control. Since the risk of glaucoma was low and acute angle closure (AAC) only occurred in rare cases, the follow-up was extended to 14 years despite substantial benefits of LPI reported after the 6-year visit./ Main Outcome Measures: The primary outcome was incidence of PAC, a composite endpoint including peripheral anterior synechiae (PAS), intraocular pressure (IOP) > 24 mmHg, or AAC. Results During the 14 years, 390 LPI-treated eyes and 388 control eyes were lost to the follow-up. A total of 33 LPI-treated eyes and 105 control eyes reached primary endpoints (P <0.01). Within them, twelve eyes developed AAC or primary angle closure glaucoma (AAC: five control eyes and one LPI-treated eye; PACG: four control eyes and two LPI-treated eyes). The hazard ratio for progression to PAC was 0.31 (95% confidence interval, 0.21–0.46) in LPI-treated eyes compared with control eyes. At the 14-year visit, LPI-treated eyes had severer nuclear cataract, higher IOP, larger angle width and limbal anterior chamber depth (LACD) than control eyes. Higher IOP, shallower LACD, and central anterior chamber depth (CACD) were associated with an increased risk of developing endpoints in control eyes. In the treated group, eyes with higher IOP, shallower LACD, or less IOP elevation after dark room–prone provocative tests (DRPPT) were more likely to develop PAC after LPI./ Conclusions: Despite a two-third decrease in PAC incidence after LPI, the cumulative risk of PAC was relatively low in the community-based PACS population over 14 years. Apart from IOP, IOP elevation after DRPPT, CACD, and LACD, more risk factors are needed to achieve precise prediction of PAC occurrence and guide clinical practice

Tissue Expression Difference between mRNAs and lncRNAs

Messenger RNA (mRNA) and long noncoding RNA (lncRNA) are two main subgroups of RNAs participating in transcription regulation. With the development of next generation sequencing, increasing lncRNAs are identified. Many hidden functions of lncRNAs are also revealed. However, the differences in lncRNAs and mRNAs are still unclear. For example, we need to determine whether lncRNAs have stronger tissue specificity than mRNAs and which tissues have more lncRNAs expressed. To investigate such tissue expression difference between mRNAs and lncRNAs, we encoded 9339 lncRNAs and 14,294 mRNAs with 71 expression features, including 69 maximum expression features for 69 types of cells, one feature for the maximum expression in all cells, and one expression specificity feature that was measured as Chao-Shen-corrected Shannon's entropy. With advanced feature selection methods, such as maximum relevance minimum redundancy, incremental feature selection methods, and random forest algorithm, 13 features presented the dissimilarity of lncRNAs and mRNAs. The 11 cell subtype features indicated which cell types of the lncRNAs and mRNAs had the largest expression difference. Such cell subtypes may be the potential cell models for lncRNA identification and function investigation. The expression specificity feature suggested that the cell types to express mRNAs and lncRNAs were different. The maximum expression feature suggested that the maximum expression levels of mRNAs and lncRNAs were different. In addition, the rule learning algorithm, repeated incremental pruning to produce error reduction algorithm, was also employed to produce effective classification rules for classifying lncRNAs and mRNAs, which gave competitive results compared with random forest and could give a clearer picture of different expression patterns between lncRNAs and mRNAs. Results not only revealed the heterogeneous expression pattern of lncRNA and mRNA, but also gave rise to the development of a new tool to identify the potential biological functions of such RNA subgroups

Environmental heterogeneity modulates the effect of plant diversity on the spatial variability of grassland biomass

Plant productivity varies due to environmental heterogeneity, and theory suggests that plant diversity can reduce this variation. While there is strong evidence of diversity effects on temporal variability of productivity, whether this mechanism extends to variability across space remains elusive. Here we determine the relationship between plant diversity and spatial variability of productivity in 83 grasslands, and quantify the effect of experimentally increased spatial heterogeneity in environmental conditions on this relationship. We found that communities with higher plant species richness (alpha and gamma diversity) have lower spatial variability of productivity as reduced abundance of some species can be compensated for by increased abundance of other species. In contrast, high species dissimilarity among local communities (beta diversity) is positively associated with spatial variability of productivity, suggesting that changes in species composition can scale up to affect productivity. Experimentally increased spatial environmental heterogeneity weakens the effect of plant alpha and gamma diversity, and reveals that beta diversity can simultaneously decrease and increase spatial variability of productivity. Our findings unveil the generality of the diversity-stability theory across space, and suggest that reduced local diversity and biotic homogenization can affect the spatial reliability of key ecosystem functions.Fil: Daleo, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Alberti, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Chaneton, Enrique Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Iribarne, Oscar Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Tognetti, Pedro Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Bakker, Jonathan. University of Washington; Estados UnidosFil: Borer, Elizabeth. University of Minnesota; Estados UnidosFil: Bruschetti, Carlos Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: MacDougall, Andrew S.. University Of Guelph. Department Of Integrative Biology.; CanadáFil: Pascual, Jesus Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Sankaran, Mahesh. University of Leeds; Reino Unido. Tata Institute of Fundamental Research; IndiaFil: Seabloom, Eric. University of Minnesota; Estados UnidosFil: Wang, Shaopeng. Peking University; ChinaFil: Bagchi, Sumanta. Indian Institute of Science; IndiaFil: Brudvig, Lars A.. Michigan State University; Estados UnidosFil: Catford, Jane A.. University of Melbourne; Australia. Kings College London (kcl);Fil: Dickman, Chris R.. The University Of Sydney; AustraliaFil: Dickson, Tymothy L.. University of Nebraska; Estados UnidosFil: Donohue, Ian. Trinity College Dublin; Reino UnidoFil: Eisenhauer, Nico. Universitat Leipzig; Alemania. German Centre for Integrative Biodiversity Research; AlemaniaFil: Gruner, Daniel S.. University of Maryland; Estados UnidosFil: Haider, Sylvia. German Centre for Integrative Biodiversity Research; Alemania. Martin Luther University Halle-Wittenberg; Alemania. Leuphana University of Lüneburg; AlemaniaFil: Jentsch, Anke. University of Bayreuth; AlemaniaFil: Knops, Johannes M. H.. Xi’an Jiaotong-Liverpool University; ChinaFil: Lekberg, Ylva. University of Montana; Estados UnidosFil: McCulley, Rebecca L.. University of Kentucky; Estados UnidosFil: Moore, Joslin L.. University of Melbourne; Australia. Monash University; Australia. Arthur Rylah Institute for Environmental Research; AustraliaFil: Mortensen, Brent. Benedictine College; Estados UnidosFil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria; Argentina. Universidad Nacional de la Patagonia Austral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rocca, Camila. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentin

Multidimensional responses of grassland stability to eutrophication

Eutrophication usually impacts grassland biodiversity, community composition, and biomass production, but its impact on the stability of these community aspects is unclear. One challenge is that stability has many facets that can be tightly correlated (low dimensionality) or highly disparate (high dimensionality). Using standardized experiments in 55 grassland sites from a globally distributed experiment (NutNet), we quantify the effects of nutrient addition on five facets of stability (temporal invariability, resistance during dry and wet growing seasons, recovery after dry and wet growing seasons), measured on three community aspects (aboveground biomass, community composition, and species richness). Nutrient addition reduces the temporal invariability and resistance of species richness and community composition during dry and wet growing seasons, but does not affect those of biomass. Different stability measures are largely uncorrelated under both ambient and eutrophic conditions, indicating consistently high dimensionality. Harnessing the dimensionality of ecological stability provides insights for predicting grassland responses to global environmental change

Soil Respiration in Tibetan Alpine Grasslands: Belowground Biomass and Soil Moisture, but Not Soil Temperature, Best Explain the Large-Scale Patterns

The Tibetan Plateau is an essential area to study the potential feedback effects of soils to climate change due to the rapid rise in its air temperature in the past several decades and the large amounts of soil organic carbon (SOC) stocks, particularly in the permafrost. Yet it is one of the most under-investigated regions in soil respiration (Rs) studies. Here, Rs rates were measured at 42 sites in alpine grasslands (including alpine steppes and meadows) along a transect across the Tibetan Plateau during the peak growing season of 2006 and 2007 in order to test whether: (1) belowground biomass (BGB) is most closely related to spatial variation in Rs due to high root biomass density, and (2) soil temperature significantly influences spatial pattern of Rs owing to metabolic limitation from the low temperature in cold, high-altitude ecosystems. The average daily mean Rs of the alpine grasslands at peak growing season was 3.92 µmol CO2 m−2 s−1, ranging from 0.39 to 12.88 µmol CO2 m−2 s−1, with average daily mean Rs of 2.01 and 5.49 µmol CO2 m−2 s−1 for steppes and meadows, respectively. By regression tree analysis, BGB, aboveground biomass (AGB), SOC, soil moisture (SM), and vegetation type were selected out of 15 variables examined, as the factors influencing large-scale variation in Rs. With a structural equation modelling approach, we found only BGB and SM had direct effects on Rs, while other factors indirectly affecting Rs through BGB or SM. Most (80%) of the variation in Rs could be attributed to the difference in BGB among sites. BGB and SM together accounted for the majority (82%) of spatial patterns of Rs. Our results only support the first hypothesis, suggesting that models incorporating BGB and SM can improve Rs estimation at regional scale

Inference of ground surface temperature history from subsurface temperature data: Interpreting ensembles of borehole logs

Ground surface temperature histories (GSTHs) inferred from borehole temperaturedepth ( T-z ) data are often degraded, to a various extent, by random or systematic noise in the T-z data and in the measurements of thermophysical properties of the earth. To minimize the effects of noise, and hence improve the fidelity of the inferred GSTH, a plausible approach is to perform a simultaneous inversion, of the T-z logs in a region, or alternatively, to invert the individual T-z logs and then average the resulting GSTHs. Averaging and simultaneous inversion are conceptually different: whereas an averaging can always be peformed, a simultaneous inversion is predicated on the assumption of a common transient component of the GSTH in all the T-z logs. In this work we examine and compare the two approaches, using a time domain inverse formulation based on the method of least squares. We consider a set of scenarios: (a) multiple T-z logs from a single borehole, (b) multiple boreholes from a single site, (c) multiple boreholes in similar climatological settings, and (d) multiple boreholes in different climatological settings. We show that for (a), (b) and (c), averaging and simultaneous inversion yield nearly identical results. For boreholes in different settings, the assumption of a common transient GSTH may be invalid and averaging and simultaneous inversion give divergent results.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43230/1/24_2004_Article_BF00878843.pd