Investigating periphyton biofilm response to changing phosphorus concentrations in UK rivers using within-river flumes

The excessive growth of benthic algal biofilms in UK rivers is a widespread problem, resulting in loss of plant communities and wider ecological damage. Elevated nutrient concentrations (particularly phosphorus) are often implicated, as P is usually considered the limiting nutrient in most rivers. Phosphorus loadings to rivers in the UK have rapidly decreased in the last decade,due to improvements in sewage treatment and changes to agricultural practises. However, in many cases, these improvements in water quality have not resulted in a reduction in nuisance algal growth. It is therefore vital that catchment managers know what phosphorus concentrations need to be achieved, in order to meet the UK’s obligations to attain good ecological status, under the EU’s Water Framework Directive. This study has developed a novel methodology, using within river mesocosms, which allows P concentrations of river water to be either increased or decreased, and the effect on biofilm accrual rate is quantified. These experiments identify the phosphorus concentrations at which algae becomes P-limited, which can be used to determine knowledge-based P targets for rivers. The ability to reduce P concentrations in river water enables algae–nutrient limitation to be studied in nutrient-enriched rivers for the first time

Loading fluorescent Ca²⁺ indicators into living cells

Small-molecule fluorescent Ca2+ reporters are the most widely used tools in the field of Ca2+ signaling. The excellent spatial and temporal resolution afforded by fluorescent reporters has driven the understanding of Ca2+ as a messenger in many different cell types. In many situations, the cellular loading and monitoring of fluorescent Ca2+ indicators is quite trivial. However, there are numerous pitfalls that require consideration to ensure that optimal data are recorded. Fluorescent Ca2+ indicators have carboxylic acid groups for binding of Ca2+. Because these “free-acid” forms of the indicators are hydrophilic they cannot readily cross cell membranes and need to be introduced into cells using techniques such as microinjection, pinocytosis, or diffusion from a patch pipette. However, the most convenient and widely used method for loading indicators into cells is as hydrophobic compounds in which the carboxylic acid groups are esterified (commonly as acetoxymethyl [AM] or acetate esters). The ester versions of the indicators permeate the plasma membrane. The Ca2+-sensitive, free-acid form of the indicator is liberated following hydrolysis of the ester groups by intracellular esterases

Plasma N-Terminal Pro-Brain Natriuretic Peptide as Prognostic Marker in Fatal Cardial Decompensation with Sunitinib Malate Therapy

A 74-year-old man with metastatic renal cell carcinoma and a history of cardiac failure was treated with sunitinib malate. MUGA echocardiography could not detect a relevant change in the ejection fraction although the clinical situation of the patient worsened dramatically. The only parameter to hint at the deteriorated cardiac function was plasma N-terminal pro-brain natriuretic peptide (BNP). Finally, the patient died after only one cycle of sunitinib treatment. We propose to prospectively include BNP for the early detection of cardiovascular decompensation in high-risk patients. Future studies concerning the relevance of BNP in drug-related cardiotoxicity are urgently needed. Copyright (C) 2010 S. Karger AG, Base

Missing wages: How to test for biased estimates in wage functions?

This paper investigates how to test for nonresponse selection bias in wage functions induced by missing income information. We suggest an “easy-to-implement” approach which requires information on interviewer IDs and the interview date rather than hard-to-get interviewer characteristics

Learning-by-Doing in the Renewable Energy Equipment Industry or in Renewable Electricity Production: Why Does It Matter to Differentiate? A Case Study of Germany

In economic models of energy and climate policy, endogenous technological change is generally introduced as the result of either investment in research-and-development or of learningby-doing. In this paper, we analyze alternative ways of modeling learning-by-doing in the renewable energy sector in a top-down CGE model. Conventionally, learning-by-doing effects in the renewable energy sector are allocated to the production of renewable based electricity. We build on the observation that learning-by-doing also takes place in sectors that deliver capital goods to the renewable electricity sector, in particular in the production of machinery and equipment for renewable energy technologies. We therefore implement learning-by-doing alternatively in the renewable energy equipment industry and in renewable electricity production and show why it matters to differentiate between these two approaches. The main differences originate from effects on international trade, since the output of the machinery and equipment sector is intensively traded on international markets unlike renewable electricity

Mixed Pricing in Online Marketplaces

A rich theory literature predicts mixed strategies in posted prices due to standard price discrimination, search frictions, and various other rationales. While typically interpreted as implying occasional sales or price dispersion, online marketplaces enable a firm to truly use randomization as a tool in pricing, and so such behavior should be expected to arise in online settings. We investigate a case of mixed pricing across a large subset of products on a major e-commerce website. We first test for randomizing behavior, and then construct a model of price discrimination that would generate randomization as optimal behavior. We estimate the model and use it to assess pricing effects of a proposed merger in the industry