Stem radial growth responds to environmental conditions, and has been widely used as a proxy to study long-term patterns of tree growth and to assess the impact of environmental changes on growth patterns. In this study, we use a tree ring dataset from the Catalan Ecological and Forest Inventory to study the temporal variability of Scots pine (Pinus sylvestris L.) stem growth during the 20th century across a relatively large region (Catalonia, NE Spain) close to the southern limit of the distribution of the species. Basal area increment (BAI) was modelled as a function of tree size and environmental variables by means of mixed effects models. Our results showed an overall increase of 84% in Scots pine BAI during the 20th century, consistent with most previous studies for temperate forests. This trend was associated with increased atmospheric CO 2 concentrations and, possibly, with a general increase in nutrient availability, and we interpreted it as a fertilization effect. Over the same time period, there was also a marked increase in temperature across the study region (0.19 1C per decade on average). This warming had a negative impact on radial growth, particularly at the drier sites, but its magnitude was not enough to counteract the fertilization effect. In fact, the substantial warming observed during the 20th century in the study area did not result in a clear pattern of increased summer drought stress because of the large variability in precipitation, which did not show any clear time trend. But the situation may change in the future if temperatures continue to rise and/or precipitation becomes scarcer. Such a change could potentially reverse the temporal trend in growth, particularly at the driest sites, and is suggested in our data by the relative constancy of radial growth after ca. 1975, coinciding with the warmer period. If this situation is representative of other relatively dry, temperate forests, the implications for the regional carbon balance would be substantial.

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A high prevalence of PCV2 infection was found in pigs from farms with and without PMWS. Besides the presence of PCV2, unknown additional factors may be necessary to induce the full expression of PMWS.

We investigated the effectiveness and tolerability of azacitidine in patients with World Health Organization-defined myelodysplastic syndromes, or acute myeloid leukemia with 20-30% bone marrow blasts. Patients were treated with azacitidine, with one of three dosage regimens: for 5 days (AZA 5); 7 days including a 2-day break (AZA 5-2-2); or 7 days (AZA 7); all 28-day cycles. Overall response rates were 39.4%, 67.9%, and 51.3%, respectively, and median overall survival (OS) durations were 13.2, 19.1, and 14.9 months. Neutropenia was the most common grade 3-4 adverse event. These results suggest better effectiveness-tolerability profiles for 7-day schedules.

PurposeTo conduct a clinical validation of a virtual reality-based experimental system that is able to assess the spherical subjective refraction simplifying the methodology of ocular refraction.MethodsFor the agreement assessment, spherical refraction measurements were obtained from 104 eyes of 52 subjects using three different methods: subjectively with the experimental prototype (Subj.E) and the classical subjective refraction (Subj.C); and objectively with the WAM-5500 autorefractor (WAM). To evaluate precision (intra- and inter-observer variability) of each refractive tool independently, 26 eyes were measured in four occasions.ResultsWith regard to agreement, the mean difference (±SD) for the spherical equivalent (M) between the new experimental subjective method (Subj.E) and the classical subjective refraction (Subj.C) was −0.034 D (±0.454 D). The corresponding 95% Limits of Agreement (LoA) were (−0.856 D, 0.924 D). In relation to precision, intra-observer mean difference for the M component was 0.034 ± 0.195 D for the Subj.C, 0.015 ± 0.177 D for the WAM and 0.072 ± 0.197 D for the Subj.E. Inter-observer variability showed worse precision values, although still clinically valid (below 0.25 D) in all instruments.ConclusionsThe spherical equivalent obtained with the new experimental system was precise and in good agreement with the classical subjective routine. The algorithm implemented in this new system and its optical configuration has been shown to be a first valid step for spherical error correction in a semiautomated way.

Abstract. The ubiquity of missing data in plant trait databases may hinder trait-based analyses of ecological patterns and processes. Spatially explicit datasets with information on intraspecific trait variability are rare but offer great promise in improving our understanding of functional biogeography. At the same time, they offer specific challenges in terms of data imputation. Here we compare statistical imputation approaches, using varying levels of environmental information, for five plant traits (leaf biomass to sapwood area ratio, leaf nitrogen content, maximum tree height, leaf mass per area and wood density) in a spatially explicit plant trait dataset of temperate and Mediterranean tree species (Ecological and Forest Inventory of Catalonia, IEFC, dataset for Catalonia, north-east Iberian Peninsula, 31 900 km 2 ). We simulated gaps at different missingness levels (10-80 %) in a complete trait matrix, and we used overall trait means, species means, k nearest neighbours (kNN), ordinary and regression kriging, and multivariate imputation using chained equations (MICE) to impute missing trait values. We assessed these methods in terms of their accuracy and of their ability to preserve trait distributions, multi-trait correlation structure and bivariate trait relationships. The relatively good performance of mean and species mean imputations in terms of accuracy masked a poor representation of trait distributions and multivariate trait structure. Species identity improved MICE imputations for all traits, whereas forest structure and topography improved imputations for some traits.No method performed best consistently for the five studied traits, but, considering all traits and performance metrics, MICE informed by relevant ecological variables gave the best results. However, at higher missingness (> 30 %), species mean imputations and regression kriging tended to outperform MICE for some traits. MICE informed by relevant ecological variables allowed us to fill the gaps in the IEFC incomplete dataset (5495 plots) and quantify imputation uncertainty. Resulting spatial patterns of the studied traits in Catalan forests were broadly similar when using species means, regression kriging or the best-performing MICE application, but some important discrepancies were observed at the local level. Our results highlight the need to assess imputation quality beyond just imputation accuracy and show that including environmental information in statistical imputation approaches yields more plausible imputations in spatially explicit plant trait datasets.

 Minimum water potential ( min ) is a key variable to characterize dehydration tolerance and hydraulic safety margins (HSM) in plants.  min is usually estimated as the absolute minimum tissue  experienced by a species, but this is problematic because sample extremes are affected by sample size and by the underlying probability distribution. We compare alternative approaches to estimate  min and assess the corresponding uncertainties and biases; propose statistically robust estimation methods based on extreme value theory (EVT); and assess the implications of our results for the characterization of hydraulic risk. Our results show that current estimates of  min and HSM are biased, as they are strongly affected by sample size. Because sampling effort is generally higher for species living in dry environments, the differences in current  min estimates between these species and those living under milder Accepted ArticleThis article is protected by copyright. All rights reserved conditions are partly artifactual. When this bias is corrected using EVT methods, resulting HSM tend to increase substantially with resistance to embolism across species. Although data availability and representativeness remain the main challenges for proper determination of  min , a closer look at  distributions and the use of statistically robust methods to estimate  min opens new ground for characterizing plant hydraulic risks.

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