The effect of bee pollen on secretion activity, markers of proliferation and apoptosis of porcine ovarian granulosa cells in vitro

The general objective of this in vitro study was to examine the effect of bee pollen on the release of insulin-like growth factor I (IGF-I) and steroid hormone progesterone, and expression of markers of proliferation (PCNA) and apoptosis (caspase-3) in porcine ovarian granulosa cells. Concentrations of IGF-I and progesterone were determined by RIA method and expression of PCNA and caspase-3 by immunocytochemistry. Bee pollen addition at the dose of 10 ng/mL significantly (P<0.05) inhibited IGF-I release by porcine ovarian granulosa cells. This growth factor was not influenced by 100 and 1000 ng/mL doses of bee pollen. Progesterone release by cells was not influenced by bee pollen addition at the doses of 10, 100 and 1000 ng/mL as used in our study. Similarly expression of PCNA and caspase-3 was not affected by bee pollen addition. The present study shows dose-dependent regulation of IGF-I by experimental bee pollen addition in vitro. Progesterone release, expression of PCNA and caspase-3 in porcine ovarian granulosa cells was not induced by pollen. Our results contribute to new insights regarding the possible effect of bee pollen on IGF-I release, which is important for regulation of porcine ovarian functions.     

Geospatial patterns of hospitalization rates for stroke with comorbid hypertension in relation to environmental sources of persistent organic pollutants: results from a 12-year population-based study

Purpose  

Recent evidence indicates that exposure to persistent organic pollutants (POPs) is emerging as an important risk factor for atherosclerosis-related diseases, including stroke. Hypertension (HTN) is a major risk factor for stroke, and some studies suggest that exposure to POPs is also a risk factor for HTN. We hypothesized that POPs increase the environmental burden of stroke with comorbid HTN.     

Retrospective exposure assessment in a chemical research and development facility

The objective of this exposure assessment was to reconstruct cumulative historical exposures for workers who have been exposed to multiple chemicals and chemical groups to better understand a cluster of brain cancers within a research and development lab. Chemicals of interest, including acrylates, bis-chloromethyl ether (BCME), chloromethyl methyl ether (CMME), isothiazolones and nitrosoamines, were selected on the basis of the plausibility of penetrating the blood-brain barrier and the uniqueness of the chemical's biological activity.In a complicated exposure setting such as a chemical R&D facility, multiple exposure estimation methods were needed. First, similarly exposure groups (SEGs) were created for these materials based on department group, time period of the department's existence and function associated with job titles. A probabilistic framework for assessing exposures was developed using Bayesian analysis of historical monitoring data, mathematical exposure modeling and professional judgments of current and former industrial hygienists at the facility were used to reconstruct the exposure history for acrylates, BCME and CMME for each SEG over the time period of interest. Since sufficient measurement data for isothiazolones and nitrosoamines were not available, the exposure histories for each SEG for these chemicals were estimated. This was done using objective formaldehyde levels and subjective employee interviews. The interviews assessed workplace determinants of exposure as distinct surrogates for estimating inhalation and dermal exposures. The exposure assessments by these methods were compared against each other to estimate the potential for exposure misclassification. A job exposure matrix (JEM) was constructed that contained the exposures obtained from above multiple approaches for each of these chemical groups for each SEG for each year of interest. The combination of methods used in this work is a unique and potentially helpful framework that can be used in analogous workplace settings involving multiple exposures with incomplete objective measurement information.     

Evaluation of biotargeting and ecotoxicity of Co²⁺-containing nanoscale polymeric complex by applying multi-marker approach in bivalve mollusk Anodonta cygnea

Cobalt (Co²⁺) is present in many nanoscaled materials created for various applications. The key goal of our study was to develop sensitive approaches for assessing the bio-risks associated with using novel Co²⁺-containing nanoscaled polymeric complex (Co-NC). Freshwater bivalve mollusk Anodonta cygnea (Unionidae) was subjected to 14 d action of the developed Co-NC, as well as of Co²⁺ applied in the corresponding concentration (50 μg L⁻¹) or polymeric substance (PS). All experimental groups under study have demonstrated signs of toxic targeting, notably changes in DNA characteristics, oxidative stress (with particularities in each exposed group) and activation of anaerobiosis (Co²⁺ and Co-NC). However, the group exposed to Co-NC showed some advantages that can be related to the activation of metallothionein (MT) function (increase in the level of MT-related SH-groups (MT-SH)): low level of oxyradical formation, no increase in protein carbonylation and vitellogenin-like proteins concentration unlike in Co²⁺ and PS exposed groups. On the other hand, Co²⁺ increased metal (Co, Cu, Zn and Cd) binding to MT (MT-Me) without changes in MT-SH level jointly with activation of oxyradical formation and apoptosis and decreasing of lysosomal membrane stability. PS per se initiated unbalanced changes in activities of the biotransformation enzymes ethoxyresorufin-O-deethylase and glutathione-S-transferase. Thus, Co²⁺ complexing with the developed PS prevented bio-toxic effects of free Co²⁺ ions and PS per se, at least in the studied hydrobiont. The MT-SH was the main distinguishing index of Co-NC group selected by classification and regression tree analysis.     

Exploring the efficiency of bias corrections of regional climate model output for the assessment of future crop yields in Europe

Excessive summer drying and reduced growing season length are expected to reduce European crop yields in future. This may be partly compensated by adapted crop management, increased CO₂ concentration and technological development. For food security, changes in regional to continental crop yield variability may be more important than changes in mean yields. The assessment of changes in regional and larger scale crop variability requires high resolution and spatially consistent future weather, matching a specific climate scenario. Such data could be derived from regional climate models (RCMs), which provide changes in weather patterns. In general, RCM output is heavily biased with respect to observations. Due to the strong nonlinear relation between meteorological input and crop yields, the application of this biased output may result in large biases in the simulated crop yield changes. The use of RCM output only makes sense after sufficient bias correction. This study explores how RCM output can be bias corrected for the assessment of changes in European and subregional scale crop yield variability due to climate change. For this, output of the RCM RACMO of the Royal Netherlands Meteorological Institute was bias corrected and applied within the crop simulation model WOrld FOod STudies to simulate potential and water limited yields of three divergent crops: winter wheat, maize and sugar beets. The bias correction appeared necessary to successfully reproduce the mean yields as simulated with observational data. It also substantially improved the year-to-year variability of seasonal precipitation and radiation within RACMO, but some bias in the interannual variability remained. This is caused by the fact that the applied correction focuses on mean and daily variability. The interannual variability of growing season length, and as a consequence the potential yields too, appeared even deteriorated. Projected decrease in mean crop yields is well in line with earlier studies. No significant change in crop yield variability was found. Yet, only one RCM is analysed in this study, and it is recommended to extend this study with more climate models and a slightly adjusted bias correction taking into account the variability of larger time scales as well.     

Declining coastal piscivore populations in the Baltic Sea: Where and when do sticklebacks matter?

Intraguild predation interactions make fish communities prone to exhibit alternative stable states with either piscivore or prey fish dominance. In the Baltic Sea, local declines of coastal piscivores like perch (Perca fluviatilis) have been observed to coincide with high densities of sticklebacks (Gasterosteus aculeatus). Mechanisms behind this shift between piscivore and stickleback dominance were studied both experimentally and in field. Results showed that predation by sticklebacks has a strong negative effect on perch larvae survival, but this effect rapidly decreases with increasing perch size, likely due to gape limitations and digestion constraints in sticklebacks. Large spatial and temporal variations in patterns of stickleback migration into perch spawning sites were observed. Whether or not high density of sticklebacks will cause declines in coastal piscivore populations is suggested to depend on the availability of spawning sites in which sticklebacks do not migrate into or arrive late in the reproduction season of coastal piscivores.     

A one year investigation of the occurrence of illicit drugs in wastewater from Brussels, Belgium

Daily 24 hour composite influent wastewater samples were collected from the wastewater treatment plant of Brussels-North (Belgium) for eight months to study variations in the concentrations and mass loads of nine illicit drugs and metabolites: cocaine (COC) and its metabolites benzoylecgonine (BE) and ecgonine methyl ester (EME), amphetamine (AMP), methylenedioxymethamphetamine (MDMA), methamphetamine (METH), methadone (MTD) and its metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP) and the specific metabolite of heroin, 6-monoacetylmorphine (6-MAM). Samples were analyzed using a validated analytical method based on solid-phase extraction and hydrophilic interaction liquid chromatography coupled to tandem mass spectrometry. The selected compounds could be detected and quantified in all samples. The measured concentrations were in agreement with earlier published concentrations, when available. All illicit drugs showed significant differences in mass loads between months. Daily variations were observed for MDMA, AMP and COC and metabolites, with higher mass loads observed during the weekend. This is probably related to their recreational consumption pattern and was further confirmed when holiday periods (New Year's Eve, Belgian national holiday) were in detail investigated. For METH, 6-MAM, MTD and EDDP, stable and consistent amounts were observed during the week. In all samples, the ratio of the concentration of the parent compound to its metabolite(s) was calculated to evaluate whether measured concentrations reflect human excretion of the illicit drugs. For MTD, the ratio of parent compound/metabolite was in agreement with the excretion pattern, while for COC some deviations were observed, resulting from excretion pattern uncertainties, and stability and discharge issues.     

Improving effectiveness of systematic conservation planning with density data

Systematic conservation planning aims to design networks of protected areas that meet conservation goals across large landscapes. The optimal design of these conservation networks is most frequently based on the modeled habitat suitability or probability of occurrence of species, despite evidence that model predictions may not be highly correlated with species density. We hypothesized that conservation networks designed using species density distributions more efficiently conserve populations of all species considered than networks designed using probability of occurrence models. To test this hypothesis, we used the Zonation conservation prioritization algorithm to evaluate conservation network designs based on probability of occurrence versus density models for 26 land bird species in the U.S. Pacific Northwest. We assessed the efficacy of each conservation network based on predicted species densities and predicted species diversity. High‐density model Zonation rankings protected more individuals per species when networks protected the highest priority 10‐40% of the landscape. Compared with density‐based models, the occurrence‐based models protected more individuals in the lowest 50% priority areas of the landscape. The 2 approaches conserved species diversity in similar ways: predicted diversity was higher in higher priority locations in both conservation networks. We conclude that both density and probability of occurrence models can be useful for setting conservation priorities but that density‐based models are best suited for identifying the highest priority areas. Developing methods to aggregate species count data from unrelated monitoring efforts and making these data widely available through ecoinformatics portals such as the Avian Knowledge Network will enable species count data to be more widely incorporated into systematic conservation planning efforts.