Common carp (Cyprinus carpio L.) alters its feeding niche in response to changing food resources: direct observations in simulated ponds

We used customized fish tanks as model fish ponds to observe grazing, swimming, and conspecific social behavior of common carp (Cyprinus carpio) under variable food-resource conditions to assess alterations in feeding niche. Different food and feeding situations were created by using only pond water or pond water plus pond bottom sediment or pond water plus pond bottom sediment and artificial feeding. All tanks were fertilized twice, prior to stocking and 2 weeks later after starting the experiment to stimulate natural food production. Common carp preferred artificial feed over benthic macroinvertebrates, followed by zooplankton. Common carp did not prefer any group of phytoplankton in any treatment. Common carp was mainly benthic in habitat choice, feeding on benthic macroinvertebrates when only plankton and benthic macroinvertebrates were available in the system. In the absence of benthic macroinvertebrates, their feeding niche shifted from near the bottom of the tanks to the water column where they spent 85% of the total time and fed principally on zooplankton. Common carp readily switched to artificial feed when available, which led to better growth. Common carp preferred to graze individually. Behavioral observations of common carp in tanks yielded new information that assists our understanding of their ecological niche. This knowledge could be potentially used to further the development of common carp aquaculture

Allelopathic Effects of Water Hyacinth [Eichhornia crassipes]

Eichhornia crassipes (Mart) Solms is an invasive weed known to out-compete native plants and negatively affect microbes including phytoplankton. The spread and population density of E. crassipes will be favored by global warming. The aim here was to identify compounds that underlie the effects on microbes. The entire plant of E. crassipes was collected from El Zomor canal, River Nile (Egypt), washed clean, then air dried. Plant tissue was extracted three times with methanol and fractionated by thin layer chromatography (TLC). The crude methanolic extract and five fractions from TLC (A–E) were tested for antimicrobial (bacteria and fungal) and anti-algal activities (green microalgae and cyanobacteria) using paper disc diffusion bioassay. The crude extract as well as all five TLC fractions exhibited antibacterial activities against both the Gram positive bacteria; Bacillus subtilis and Streptococcus faecalis; and the Gram negative bacteria; Escherichia coli and Staphylococcus aureus. Growth of Aspergillus flavus and Aspergillus niger were not inhibited by either E. crassipes crude extract nor its five fractions. In contrast, Candida albicans (yeast) was inhibited by all. Some antialgal activity of the crude extract and its fractions was manifest against the green microalgae; Chlorella vulgaris and Dictyochloropsis splendida as well as the cyanobacteria; Spirulina platensis and Nostoc piscinale. High antialgal activity was only recorded against Chlorella vulgaris. Identifications of the active antimicrobial and antialgal compounds of the crude extract as well as the five TLC fractions were carried out using gas chromatography combined with mass spectroscopy. The analyses showed the presence of an alkaloid (fraction A) and four phthalate derivatives (Fractions B–E) that exhibited the antimicrobial and antialgal activities

Managing Oil Palm Plantations More Sustainably: Large-Scale Experiments Within the Biodiversity and Ecosystem Function in Tropical Agriculture (BEFTA) Programme

Conversion of tropical forest to agriculture results in reduced habitat heterogeneity, and associated declines in biodiversity and ecosystem functions. Management strategies to increase biodiversity in agricultural landscapes have therefore often focused on increasing habitat complexity; however, the large-scale, long-term ecological experiments that are needed to test the effects of these strategies are rare in tropical systems. Oil palm (Elaeis guineensis Jacq.)—one of the most widespread and important tropical crops—offers substantial potential for developing wildlife-friendly management strategies because of its long rotation cycles and tree-like structure. Although there is awareness of the need to increase sustainability, practical options for how best to manage oil palm plantations, for benefits to both the environment and crop productivity, have received little research attention. In this paper we introduce the Biodiversity and Ecosystem Function in Tropical Agriculture (BEFTA) Programme: a long-term research collaboration between academia and industry in Sumatra, Indonesia. The BEFTA Programme aims to better understand the oil palm agroecosystem and test sustainability strategies. We hypothesise that adjustments to oil palm management could increase structural complexity, stabilize microclimate, and reduce reliance on chemical inputs, thereby helping to improve levels of biodiversity and ecosystem functions. The Programme has established four major components: (1) assessing variability within the plantation under business-as-usual conditions; (2) the BEFTA Understory Vegetation Project, which tests the effects of varying herbicide regimes; (3) the Riparian Ecosystem Restoration in Tropical Agriculture (RERTA) Project, which tests strategies for restoring riparian habitat; and (4) support for additional collaborative projects within the Programme landscape. Across all projects, we are measuring environmental conditions, biodiversity, and ecosystem functions. We also measure oil palm yield and production costs, in order to assess whether suggested sustainability strategies are feasible from an agronomic perspective. Early results show that oil palm plantation habitat is more variable than might be expected from a monoculture crop, and that everyday vegetation management decisions have significant impacts on habitat structure. The BEFTA Programme highlights the value of large-scale collaborative projects for understanding tropical agricultural systems, and offers a highly valuable experimental set-up for improving our understanding of practices to manage oil palm more sustainably.This work was funded by The Isaac Newton Trust Cambridge, Golden Agri Resources, ICOPE (the International Conference on Oil Palm and the Environment), and the Natural Environment Research Council [grant number NE/P00458X/1]

Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges

Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted

Efficacy of Synaptic Inhibition Depends on Multiple, Dynamically Interacting Mechanisms Implicated in Chloride Homeostasis

Chloride homeostasis is a critical determinant of the strength and robustness of inhibition mediated by GABAA receptors (GABAARs). The impact of changes in steady state Cl− gradient is relatively straightforward to understand, but how dynamic interplay between Cl− influx, diffusion, extrusion and interaction with other ion species affects synaptic signaling remains uncertain. Here we used electrodiffusion modeling to investigate the nonlinear interactions between these processes. Results demonstrate that diffusion is crucial for redistributing intracellular Cl− load on a fast time scale, whereas Cl−extrusion controls steady state levels. Interaction between diffusion and extrusion can result in a somato-dendritic Cl− gradient even when KCC2 is distributed uniformly across the cell. Reducing KCC2 activity led to decreased efficacy of GABAAR-mediated inhibition, but increasing GABAAR input failed to fully compensate for this form of disinhibition because of activity-dependent accumulation of Cl−. Furthermore, if spiking persisted despite the presence of GABAAR input, Cl− accumulation became accelerated because of the large Cl− driving force that occurs during spikes. The resulting positive feedback loop caused catastrophic failure of inhibition. Simulations also revealed other feedback loops, such as competition between Cl− and pH regulation. Several model predictions were tested and confirmed by [Cl−]i imaging experiments. Our study has thus uncovered how Cl− regulation depends on a multiplicity of dynamically interacting mechanisms. Furthermore, the model revealed that enhancing KCC2 activity beyond normal levels did not negatively impact firing frequency or cause overt extracellular K− accumulation, demonstrating that enhancing KCC2 activity is a valid strategy for therapeutic intervention

Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study.

Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development

Lung function in adults following in utero and childhood exposure to arsenic in drinking water: preliminary findings

PurposeEvidence suggests that arsenic in drinking water causes non-malignant lung disease, but nearly all data concern exposed adults. The desert city of Antofagasta (population 257,976) in northern Chile had high concentrations of arsenic in drinking water (>800 μg/l) from 1958 until 1970, when a new treatment plant was installed. This scenario, with its large population, distinct period of high exposure, and accurate data on past exposure, is virtually unprecedented in environmental epidemiology. We conducted a pilot study on early-life arsenic exposure and long-term lung function. We present these preliminary findings because of the magnitude of the effects observed.MethodsWe recruited a convenience sample consisting primarily of nursing school employees in Antofagasta and Arica, a city with low drinking water arsenic. Lung function and respiratory symptoms in 32 adults exposed to >800 μg/l arsenic before age 10 were compared to 65 adults without high early-life exposure.ResultsEarly-life arsenic exposure was associated with 11.5% lower forced expiratory volume in 1 s (FEV(1)) (P = 0.04), 12.2% lower forced vital capacity (FVC) (P = 0.04), and increased breathlessness (prevalence odds ratio = 5.94, 95% confidence interval 1.36-26.0). Exposure-response relationships between early-life arsenic concentration and adult FEV(1) and FVC were also identified (P trend = 0.03).ConclusionsEarly-life exposure to arsenic in drinking water may have irreversible respiratory effects of a magnitude similar to smoking throughout adulthood. Given the small study size and non-random recruitment methods, further research is needed to confirm these findings

Clinical practice: Breastfeeding and the prevention of allergy

The increase in allergic disease prevalence has led to heightened interest in the factors determining allergy risk, fuelled by the hope that by influencing these factors one could reduce the prevalence of allergic conditions. The most important modifiable risk factors for allergy are maternal smoking behaviour and the type of feeding. A smoke-free environment for the child (to be), exclusive breastfeeding for 4–6 months and the postponement of supplementary feeding (solids) until 4 months of age are the main measures considered effective. There is no place for restricted diets during pregnancy or lactation. Although meta-analyses suggest that hypoallergenic formula after weaning from breastfeeding grants protection against the development of allergic disease, the evidence is limited and weak. Moreover, all current feeding measures aiming at allergy prevention fail to show effects on allergic manifestations later in life, such as asthma. In conclusion, the allergy preventive effect of dietary interventions in infancy is limited. Counselling of future parents on allergy prevention should pay attention to these limitations

Reassessing the Evidence Hierarchy in Asthma: Evaluating Comparative Effectiveness

Classical randomized controlled trials are the gold standard in medical evidence because of their high internal validity. However, their necessarily strict design can limit their external validity and the ability to extrapolate these data to real world patients. Therefore, alternatively designed studies may play a complementary role in evaluating the comparative effectiveness of therapies in nonidealized patients in more naturalistic, real world settings. Observational studies have high external validity and can evaluate real world outcomes. Their strength lies in hypothesis generation and testing and in identifying areas in which further clinical trials may be required. Pragmatic trials are designed to maximize applicability of trial results to usual care settings by relying on clinically important outcomes and enrolling a wide range of participants. A combination of these approaches is preferable and necessary