Toxic effects of Pb2+ on growth of cowpea (Vigna unguiculata)

A concentration as low as 1 mu M lead (Pb) is highly toxic to plants, but previous studies have typically related plant growth to the total amount of Pb added to a solution. In the present experiment, the relative fresh mass of cowpea (Vigna unguiculata) was reduced by 10% at a Pb2+ activity of 0.2 mu M for the shoots and at a Pb2+ activity of 0.06 mu M for the roots. The primary site of Pb2+ toxicity was the root, causing severe reductions in root growth, loss of apical dominance (shown by an increase in branching per unit root length), the formation of localized swellings behind the root tips (due to the initiation of lateral roots), and the bending of some root tips. In the root, Pb was found to accumulate primarily within the cell walls and intercellular spaces. (C) 2007 Elsevier Ltd. All rights reserved

Rhizotoxicity of aluminate and polycationic aluminium at high pH

Although monomeric Al species are often toxic in acidic soils, the effects of the aluminate ion (Al(OH)4-) on roots grown in alkaline media are still unclear. Dilute, alkaline (pH 9.5) nutrient solutions were used to investigate the effects of Al(OH)4- on root growth of mungbean (Vigna radiata L.). Root growth was reduced by 13 % after 3 d growth in solutions with an Al(OH)4- activity of 16 μM and no detectable polycationic Al (Al13). This decrease in root growth was associated with the formation of lesions on the root tips (due to the rupturing of the epidermal and outer cortical cells) and a slight limitation to root hair growth (particularly on the lateral roots). When roots displaying these symptoms were transferred to fresh Al(OH)4- solutions for a further 12 h, no root tip lesions were observed and root hair growth on the lateral roots improved. The symptoms were similar to those induced by Al13 at concentrations as low as 0.50 μM Al which are below the detection limit of the ferron method. Thus, Al(OH)4- is considered to be non-toxic, with the observed reduction in root growth in solutions containing Al(OH)4- due to the gradual formation of toxic Al13 in the bulk nutrient solution resulting from the acidification of the alkaline nutrient solution by the plant roots

Toxic effects of Pb2+ on the growth and mineral nutrition of signal grass (Brachiaria decumbens) and Rhodes grass (Chloris gayana)

Although grasses are commonly used to revegetate sites contaminated with lead (Pb), little is known regarding the Pb-tolerance of many of these species. Using dilute solution culture to mimic the soil solution, the growth of signal grass (Brachiaria decumbens Stapf cv. Basilisk) and Rhodes grass (Chloris gayana Kunth cv. Pioneer) was related to the mean activity of Pb2+ {Pb2+} in solution. There was a 50% reduction in fresh mass of signal grass shoots at 5 mu M {Pb2+} and at 3 mu M {Pb2+} for the roots. Rhodes grass was considerably more sensitive to Pb in solution, with shoot and root fresh mass being reduced by 50% at 0.5 mu M {Pb2+}. The higher tolerance of signal grass to Pb appeared to result from the internal detoxification of Pb, rather than from the exclusion of Pb from the root. At toxic {Pb2+}, an interveinal chlorosis developed in the shoots of signal grass (possibly a Pb-induced Mn deficiency), whilst in Rhodes grass, Pb2+ caused a bending of the root tips and the formation of a swelling immediately behind some of the root apices. Root hair growth did not appear to be reduced by Pb2+ in solution, being prolific at all {Pb2+} in both species

A many-analysts approach to the relation between religiosity and well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

A Many-analysts Approach to the Relation Between Religiosity and Well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N = 10, 535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β = 0.120). For the second research question, this was the case for 65% of the teams (median reported β = 0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

Preoperative medical treatment in Cushing's syndrome : frequency of use and its impact on postoperative assessment : data from ERCUSYN

Background: Surgery is the definitive treatment of Cushing's syndrome (CS) but medications may also be used as a first-line therapy. Whether preoperative medical treatment (PMT) affects postoperative outcome remains controversial. Objective: (1) Evaluate how frequently PMT is given to CS patients across Europe; (2) examine differences in preoperative characteristics of patients who receive PMT and those who undergo primary surgery and (3) determine if PMT influences postoperative outcome in pituitary-dependent CS (PIT-CS). Patients and methods: 1143 CS patients entered into the ERCUSYN database from 57 centers in 26 countries. Sixty-nine percent had PIT-CS, 25% adrenal-dependent CS (ADR-CS), 5% CS from an ectopic source (ECT-CS) and 1% were classified as having CS from other causes (OTH-CS). Results: Twenty per cent of patients took PMT. ECT-CS and PIT-CS were more likely to receive PMT compared to ADR-CS (P < 0.001). Most commonly used drugs were ketoconazole (62%), metyrapone (16%) and a combination of both (12%). Median (interquartile range) duration of PMT was 109 (98) days. PIT-CS patients treated with PMT had more severe clinical features at diagnosis and poorer quality of life compared to those undergoing primary surgery (SX) (P < 0.05). Within 7 days of surgery, PIT-CS patients treated with PMT were more likely to have normal cortisol (P < 0.01) and a lower remission rate (P < 0.01). Within 6 months of surgery, no differences in morbidity or remission rates were observed between SX and PMT groups. Conclusions: PMT may confound the interpretation of immediate postoperative outcome. Follow-up is recommended to definitely evaluate surgical results

A many-analysts approach to the relation between religiosity and well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

Using meta-predictions to identify experts in the crowd when past performance is unknown.

A common approach to improving probabilistic forecasts is to identify and leverage the forecasts from experts in the crowd based on forecasters' performance on prior questions with known outcomes. However, such information is often unavailable to decision-makers on many forecasting problems, and thus it can be difficult to identify and leverage expertise. In the current paper, we propose a novel algorithm for aggregating probabilistic forecasts using forecasters' meta-predictions about what other forecasters will predict. We test the performance of an extremised version of our algorithm against current forecasting approaches in the literature and show that our algorithm significantly outperforms all other approaches on a large collection of 500 binary decision problems varying in five levels of difficulty. The success of our algorithm demonstrates the potential of using meta-predictions to leverage latent expertise in environments where forecasters' expertise cannot otherwise be easily identified