Species abundance distributions: moving beyond single prediction theories to integration within an ecological framework

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75247/1/j.1461-0248.2007.01094.x.pd

Test beam performance measurements for the Phase I upgrade of the CMS pixel detector

A new pixel detector for the CMS experiment was built in order to cope with the instantaneous luminosities anticipated for the Phase I Upgrade of the LHC. The new CMS pixel detector provides four-hit tracking with a reduced material budget as well as new cooling and powering schemes. A new front-end readout chip mitigates buffering and bandwidth limitations, and allows operation at low comparator thresholds. In this paper, comprehensive test beam studies are presented, which have been conducted to verify the design and to quantify the performance of the new detector assemblies in terms of tracking efficiency and spatial resolution. Under optimal conditions, the tracking efficiency is (99.95 ± 0.05) %, while the intrinsic spatial resolutions are (4.80 ± 0.25) μm and (7.99 ± 0.21) μm along the 100 μm and 150 μm pixel pitch, respectively. The findings are compared to a detailed Monte Carlo simulation of the pixel detector and good agreement is found.Peer reviewe

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Risk scores for predicting outcomes in patients with type 2 diabetes and nephropathy: The RENAAL study

Diabetic nephropathy is the most important cause of ESRD. The aim of this study was to develop a risk score from risk predictors for ESRD, with and without death, in the Reduction of Endpoints in NIDDM with the Angiotensin II Antagonist Losartan (RENAAL) study and to compare ability of the ESRD risk score and its components to predict ESRD. The risk score was developed from coefficients of independent risk factors from multivariate analysis of baseline variables and equals (1.96 X log [urinary albumin:creatinine ratio]) - (0.78 serum albumin [g/dl]) + (1.28 X serum creatinine [mg/dl] - (0.11 X hemoglobin [g/dl]). It was robust with respect to severity of nephropathy, gender, race, and treatment group. The risk score for ESRD or death was comparable. The four risk predictors for progression of kidney disease were independent of therapy. For combined treatment groups, the hazard ratio between the fourth and first quartiles of the ESRD risk score was 49.0, as compared with the corresponding hazard ratios for each component: 14.7 for urinary albumin:creatinine ratio, 9.2 for serum creatinine, 5.5 for hemoglobin, and 10.2 for serum albumin. The RENAAL risk scores for ESRD with or without death emphasize the importance of identification of level of albuminuria, serum albumin, serum creatinine, and hemoglobin to predict development of ESRD in patients with type 2 diabetes and nephropathy. Although albuminuria is a strong risk factor for ESRD, the contribution of serum albumin, serum creatinine, and hemoglobin level further enhances prediction of ESRD. Future trials with a similar patient population and outcomes measures should consider adjusting analyses for baseline risk factor