Electrochemical sensor based on epoxy-functionalized BEA nanozeolite and graphene oxide modified glassy carbon electrode for bisphenol E determination

An epoxy-functionalized beta type nanozeolite (BEA)/graphene oxide nanocomposite modified glassy carbon electrode (GCE/BEA/APTMS/GA/GO/NF) has been created for the differential pulse voltammetric determination of bisphenol E (BPE). The modified electrode presented an enhanced current response in comparison with bare GCE. A linear dependence of anodic peak current (Ip) and scan rate (ν) was observed, which showed that the electrochemical process was adsorption-controlled. Differential pulse voltammetry (DPV) was employed and optimized for the sensitive determination of BPE. Under the optimized conditions, the anodic peak current was linearly proportional to BPE concentration in the range between 0.07 and 4.81 µM, with a correlation coefficient of 0.995 and limit of detection 0.056 μM (S/N = 3). The electrode showed good repeatability and storage stability, and a low response to interfering compounds. Comparison was made to the determination of bisphenol A. To confirm the electrode analytical performance, recovery tests were performed, and deviations lower than 10% were found. The BEA zeolite-GO nanocomposite proved to be a promising sensing platform for bisphenol determination. Graphical abstract: [Figure not available: see fulltext.]

Ceibinin, a new positional isomer of mangiferin from the inflorescence of Ceiba pentandra (Bombacaceae), elicits similar antioxidant effect but no anti-inflammatory potential compared to mangiferin

Ceiba pentandra (L.) Gaertn. (Bombacaceae) is popular for the quality of its wood. However, its leaf, stem bark and root bark have been popular in ethnomedicine and, apart from the inflorescence, have been subject of extensive phytochemical investigations. In this study, two compounds were isolated from the crude methanol extract of the inflorescence. Through data from UV, NMR, MS, electrochemical studies, differential scanning calorimetry, and thermogravimetric analysis, the structures were elucidated as 3-C-β-D-glucopyranosyl-1,3,6,7-tetrahydroxyxanthone (1) and 2-C-β-D-glucopyranosyl-1,3,6,7-tetrahydroxyxanthone (mangiferin, 2). They were assessed for antioxidant efficacy (DCFDA assay) and for anti-inflammatory efficacy using the lipopolysaccharide (LPS)-induced inflammation model in the RAW 264.7 macrophages (nitrite levels quantified, using Griess Assay, as surrogate for nitric oxide (NO)). Compound 1 (named ceibinin) was established as a novel positional isomer of mangiferin (2). While both 1 and 2 were antioxidant against basal and hydrogen peroxide (100 μM)-induced oxidative stress (6.25 μg/ml abrogated peroxide-induced oxidative stress), ceibinin (1) demonstrated no anti-inflammatory potential, unlike mangiferin (2) which, as previously reported, showed anti-inflammatory effect. Our work reports a positional isomer of mangiferin for the first time in C. pentandra and demonstrates how such isomerism could underlie differences in biological activities and thus the potential for development into therapeutics

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Both habitat change and local lek structure influence patterns of spatial loss and recovery in a black grouse population

The final publication is available at Springer via http://dx.doi.org/10.1007/s10144-015-0484-3Land use change is a major driver of declines in wildlife populations. Where human economic or recreational interests and wildlife share landscapes this problem is exacerbated. Changes in UK black grouse Tetrao tetrix populations are thought to have been strongly influenced by upland land use change. In a long-studied population within Perthshire, lek persistence is positively correlated with lek size, and remaining leks clustered most strongly within the landscape when the population is lowest, suggesting that there may be a demographic and/or spatial context to the reaction of the population to habitat changes. Hierarchical cluster analysis of lek locations revealed that patterns of lek occupancy when the population was declining were different to those during the later recovery period. Response curves from lek-habitat models developed using MaxEnt for periods with a declining population, low population, and recovering population were consistent across years for most habitat measures. We found evidence linking lek persistence with habitat quality changes and more leks which appeared between 1994 and 2008 were in improving habitat than those which disappeared during the same period. Generalised additive models (GAMs) identified changes in woodland and starting lek size as being important indicators of lek survival between declining and low/recovery periods. There may also have been a role for local densities in explaining recovery since the population low point. Persistence of black grouse leks was influenced by habitat, but changes in this alone did not fully account for black grouse declines. Even when surrounded by good quality habitat, leks can be susceptible to extirpation due to isolation

Current and potential geographical distribution of Platymeris biguttatus (Linnaeus, 1767) with description of nymphs

Background: The description of Platymeris biguttatus (Linnaeus 1767) nymphal instars as well as the prediction of the potentially suitable ecological niche was the main goal of this study. Our research was based on 258 specimens of P. biguttatus species of museum collections. A set of 23 environmental predictor variables covering Africa was used at ecological niche modeling - a method performed using the Maxent software to prepare potential distribution maps for this species. Results: The results suggested the most suitable areas seen as potentially suitable ecological niche for P. biguttatus in Africa. A jackknife test showed that temperature seasonality and percentage of tree cover were among the most important environmental variables affecting the distribution of the species. The analysis of climate preferences shows that most of the potentially suitable niches for this species were located in the area of tropical savanna climate, with a small participation of tree vegetation. Conclusions: P. biguttatus was only known to be widely distributed in the tropical part of continental Africa. Thanks to the ecological niche modeling methods and the museum data on the occurrence of the species, we introduced new information about potentially suitable ecological niches and the possible range of distribution

Palaeoclimatic conditions in the Mediterranean explain genetic diversity of Posidonia oceanica seagrass meadows

Past environmental conditions in the Mediterranean Sea have been proposed as main drivers of the current patterns of distribution of genetic structure of the seagrass Posidonia oceanica, the foundation species of one of the most important ecosystems in the Mediterranean Sea. Yet, the location of cold climate refugia (persistence regions) for this species during the Last Glacial Maximum (LGM) is not clear, precluding the understanding of its biogeographical history. We used Ecological Niche Modelling together with existing phylogeographic data to locate Pleistocene refugia in the Mediterranean Sea and to develop a hypothetical past biogeographical distribution able to explain the genetic diversity presently found in P. oceanica meadows. To do that, we used an ensemble approach of six predictive algorithms and two Ocean General Circulation Models. The minimum SST in winter and the maximum SST in summer allowed us to hindcast the species range during the LGM. We found separate glacial refugia in each Mediterranean basin and in the Central region. Altogether, the results suggest that the Central region of the Mediterranean Sea was the most relevant cold climate refugium, supporting the hypothesis that long-term persistence there allowed the region to develop and retain its presently high proportion of the global genetic diversity of P. oceanica.Fundacao para a Ciencia e a Tecnologia (FCT, Portugal) [SFRH/BPD/85040/2012]; FCT [UID/Multi/04326/2013, FCT-BIODIVERSA/004/2015]; Pew foundation (USA)info:eu-repo/semantics/publishedVersio

GENN: A GEneral Neural Network for Learning Tabulated Data with Examples from Protein Structure Prediction

We present a GEneral Neural Network (GENN) for learning trends from existing data and making predictions of unknown information. The main novelty of GENN is in its generality, simplicity of use, and its specific handling of windowed input/output. Its main strength is its efficient handling of the input data, enabling learning from large datasets. GENN is built on a two-layered neural network and has the option to use separate inputs–output pairs or window-based data using data structures to efficiently represent input–output pairs. The program was tested on predicting the accessible surface area of globular proteins, scoring proteins according to similarity to native, predicting protein disorder, and has performed remarkably well. In this paper we describe the program and its use. Specifically, we give as an example the construction of a similarity to native protein scoring function that was constructed using GENN. The source code and Linux executables for GENN are available from Research and Information Systems at http://mamiris.com and from the Battelle Center for Mathematical Medicine at http://mathmed.org. Bugs and problems with the GENN program should be reported to EF

Improving spatial prioritisation for remote marine regions: optimising biodiversity conservation and sustainable development trade-offs

Creating large conservation zones in remote areas, with less intense stakeholder overlap and limited environmental information, requires periodic review to ensure zonation mitigates primary threats and fill gaps in representation, while achieving conservation targets. Follow-up reviews can utilise improved methods and data, potentially identifying new planning options yielding a desirable balance between stakeholder interests. This research explored a marine zoning system in north-west Australia–abiodiverse area with poorly documented biota. Although remote, it is economically significant (i.e. petroleum extraction and fishing). Stakeholder engagement was used to source the best available biodiversity and socio-economic data and advanced spatial analyses produced 765 high resolution data layers, including 674 species distributions representing 119 families. Gap analysis revealed the current proposed zoning system as inadequate, with 98.2% of species below the Convention on Biological Diversity 10% representation targets. A systematic conservation planning algorithm Maxan provided zoning options to meet representation targets while balancing this with industry interests. Resulting scenarios revealed that conservation targets could be met with minimal impacts on petroleum and fishing industries, with estimated losses of 4.9% and 7.2% respectively. The approach addressed important knowledge gaps and provided a powerful and transparent method to reconcile industry interests with marine conservation