In vitro exposure of Ulva lactuca Linnaeus (Chlorophyta) to gasoline - Biochemical and morphological alterations

Refined fuels have considerable share of pollution of marine ecosystems. Gasoline is one of the most consumed fuel worldwide, but its effects on marine benthic primary producers are poorly investigated. In this study, Ulva lactuca was chosen as a biological model due to its cosmopolitan nature and tolerance to high levels and wide range of xenobiotics and our goal was to evaluate the effects of gasoline on ultrastructure and metabolism of that seaweed. The experimental design consisted of in vitro exposure of U. lactuca to four concentrations of gasoline (0.001%, 0.01%, 0.1%, and 1.0%, v/v) over 30 min, 1 h, 12 h, and 24 h, followed by cytochemical, SEM, and biochemical analysis. Increase in the number of cytoplasmic granules, loss of cell turgor, cytoplasmic shrinkage, and alterations in the mucilage were some of the ultrastructural alterations observed in thalli exposed to gasoline. Decrease in carotenoid and polyphenol contents, as well as increase of soluble sugars and starch contents were associated with the time of exposure to the xenobiotic. In combination, the results revealed important morphological and biochemical alterations in the phenotype of U. lactuca upon acute exposure to gasoline. This seaweed contain certain metabolites assigned as candidates to biomarkers of the environmental stress investigated and it is thought to be a promise species for usage in coastal ecosystems perturbation monitoring system. In addition, the findings suggest that U. lactuca is able to metabolize gasoline hydrocarbons and use them as energy source, acting as bioremediator of marine waters contaminated by petroleum derivatives.

[Sufficient high frequency jet ventilation during a period of 2.5 h - Airway management during resection of a tracheaesophageal fistula and tracheal resection]

We present a 54 year old female patient who had undergone a Ross procedure in 2009, and in 2013 again a replacement of the aortic root and arch with bioprothetic material and homograft replacement of the pulmonalis walve. Postoperatively the patient had experienced a functional compromising tracheal stenosis and a persistent esophago-tracheal fistula. Endoscopic attempts to close the fistula were not successful, and the fistula was "bridged" with an endoscopically positioned tracheal stent.We report the anaesthesiological management during the open surgical repair of the esophago-tracheal fistula and resection of 2 tracheal rings with high frequency jet ventilation over a period of 2.5 h. The Patient was discharged from hospital on the 17. postoperative day.

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as nuclear magnetic resonance, gas or liquid chromatography, mass spectrometry, infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

Toward better understanding of postharvest deterioration: biochemical changes in stored cassava (Manihot esculenta Crantz) roots

Food losses can occur during production, postharvest, and processing stages in the supply chain. With the onset of worldwide food shortages, interest in reducing postharvest losses in cassava has been increasing. In this research, the main goal was to evaluate biochemical changes and identify the metabolites involved in the deterioration of cassava roots. We found that high levels of ascorbic acid (AsA), polyphenol oxidase (PPO), dry matter, and proteins are correlated with overall lower rates of deterioration. On the other hand, soluble sugars such as glucose and fructose, as well as organic acids, mainly, succinic acid, seem to be upregulated during storage and may play a role in the deterioration of cassava roots. Cultivar Branco (BRA) was most resilient to postharvest physiological deterioration (PPD), while Oriental (ORI) was the most susceptible. Our findings suggest that PPO, AsA, and proteins may play a distinct role in PPD delay.

Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data

We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of Bacillus subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs, and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches, and small regulatory RNAs. Overall, regulatory information is included in the model for ∼2500 of the ∼4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how ARs for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how ARs can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology.

Early interventions practices in ASD – Maternal behavior in spontaneous interaction with their children

Recent studies point out that the occurrence of Autism Spectrum Disorders (ASD) is up to 1%. In Brazil, providing services of medical diagnosis for children with ASD takes precious time. The time spent waiting for the conclusion of the diagnostic process would be extremely important to the child's development. In this context, the parent–children interaction is an important aspect to be considered as a factor that has a positive impact in this process. The aim of this study was to explore maternal behavior during a spontaneous play situation with their children. All subjects were filmed for 30 minutes during a spontaneous play situation with their mothers that did not receive any special instructions. The subjects were 30 normal children, 3 of each age (respectively 9, 12, 15, 18, 21, 24, 27, 30, 33 and 36 months) and 20 autistic children with ages varying from 2:10 to 10:6 (not expected any chronological association). The whole corpus was analyzed considering of each subject, of each group and the possible associations between the groups. The analysis of the maternal behavior provides data about the importance of mother–child interaction and how everyday life activities and routines can be used to increase the opportunities to improve the ASD child's communicative abilities and their creative use as an important part of the whole development. These findings provide further evidence that families should be included in any plan for intervention designed towards ASD children.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

The effects of alexithymia in the recognition of dynamic emotional faces

Introduction

Alexithymia is a multifactorial personality trait observed in several mental disorders, especially those with poor social functioning. Although it has been proposed that difficulties in interpersonal interactions in highly alexithymic individuals may stem from their reduced ability to express and recognize facial expressions, this still remains controversial.

Aim

In everyday life, faces displaying emotions are dynamic, although most studies have relied on static stimuli. The aim of this study was to investigate whether individuals with high levels of alexithymia differed from a control group in the categorization of emotional faces presented in a dynamic way. Given the highly dynamic nature of facial displays in real life, we used morphed videos depicting faces varying 1% from neutral to angry, disgust or happy faces, with a video presentation of 35 seconds.

Method

Sixty participants (27 males and 33 females) were divided into high (HA) and low levels of alexithymia (LA) by using the Toronto Alexithymia Scale (TAS-20). Participants were instructed to watch the face change from neutral to an emotion and to press a keyboard as soon as they could categorize an emotion expressed in the face.

Results

The results revealed an interaction between alexithymia and emotion showing that HA, compared to LA, were more inaccurate at categorizing angry faces.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Data supporting the role of enzymes and polysaccharides during cassava postharvest physiological deterioration

This data article is referred to the research article entitled The role of ascorbate peroxidase, guaiacol peroxidase, and polysaccharides in cassava (Manihot esculenta Crantz) roots under postharvest physiological deterioration by Uarrota et al. (2015). Food Chemistry 197, Part A, 737–746.

The stress duo to PPD of cassava roots leads to the formation of ROS which are extremely harmful and accelerates cassava spoiling. To prevent or alleviate injuries from ROS, plants have evolved antioxidant systems that include non-enzymatic and enzymatic defence systems such as ascorbate peroxidase, guaiacol peroxidase and polysaccharides. In this data article can be found a dataset called “newdata”, in RData format, with 60 observations and 06 variables. The first 02 variables (Samples and Cultivars) and the last 04, spectrophotometric data of ascorbate peroxidase, guaiacol peroxidase, tocopherol, total proteins and arcsined data of cassava PPD scoring. For further interpretation and analysis in R software, a report is also provided. Means of all variables and standard deviations are also provided in the Supplementary tables (“data.long3.RData, data.long4.RData and meansEnzymes.RData”), raw data of PPD scoring without transformation (PPDmeans.RData) and days of storage (days.RData) are also provided for data analysis reproducibility in R software.

Metabolic Profiling and Classification of Propolis Samples from Southern Brazil: An NMR-Based Platform Coupled with Machine Learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching ∼90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Evaluación de la diversidad en comunidades de tardígrados (Ecdysozoa: Tardigrada) en hábitats urbano y rural de la ciudad de Salta (Argentina)

RESÚMEN Este trabajo se realizó bajo la hipótesis de que existe una pérdida creciente de diversidad en las comunidades de tardígrados, desde las áreas rurales hacia las urbanas, incrementando la homogenización de sus comunidades producto de la urbanización. Para la ciudad de Salta, se tomaron muestras en áreas con tránsito vehicular alto y muestras en áreas rurales circundantes. Se identificaron en total ocho especies/morfoespecies. El inventario tuvo una completitud mayor al 94%. La comunidad rural fue más diversa y estructuralmente más uniforme que la comunidad urbana. Macrobiotus hufelandi Schultze, 1834 resultó especie indicadora y Milnesium sp. como detectora para el hábitat urbano, mientras que Paramacrobiotus areolatus Murray, 1907 resultó indicadora para el hábitat rural.

UV-visible scanning spectrophotometry and chemometric analysis as tools for carotenoids analysis in cassava genotypes (Manihot esculenta Crantz)

In this study, the metabolomics characterization focusing on the carotenoid composition of ten cassava (Manihot esculenta) genotypes cultivated in southern Brazil by UV-visible scanning spectrophotometry and reverse phase-high performance liquid chromatography was performed. Cassava roots rich in β-carotene are an important staple food for populations with risk of vitamin A deficiency. Cassava genotypes with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The data set was used for the construction of a descriptive model by chemometric analysis. The genotypes of yellow-fleshed roots were clustered by the higher concentrations of cis- β-carotene and lutein. Inversely, cream-fleshed roots genotypes were grouped precisely due to their lower concentrations of these pigments, as samples rich in lycopene (redfleshed) differed among the studied genotypes. The analytical approach (UV-Vis, HPLC, and chemometrics) used showed to be efficient for understanding the chemodiversity of cassava genotypes, allowing to classify them according to important features for human health and nutrition.

Google Street View as an alternative method to car surveys in large-scale vegetation assessments

Car surveys (CS) are a common method for assessing the distribution of alien invasive plants. Google Street View (GSV), a free-access web technology where users may experience a virtual travel along roads, has been suggested as a cost-effective alternative to car surveys. We tested if we could replicate the results from a countrywide survey conducted by car in Portugal using GSV as a remote sensing tool, aiming at assessing the distribution of Eucalyptus globulus Labill. wildlings on roadsides adjacent to eucalypt stands. Georeferenced points gathered along CS were used to create road transects visible as lines overlapping the road in GSV environment, allowing surveying the same sampling areas using both methods. This paper presents the results of the comparison between the two methods. Both methods produced similar models of plant abundance, selecting the same explanatory variables, in the same hierarchical order of importance and depicting a similar influence on plant abundance. Even though the GSV model had a lower performance and the GSV survey detected fewer plants, additional variables collected exclusively with GSV improved model performance and provided a new insight into additional factors influencing plant abundance. The survey using GSV required ca. 9 % of the funds and 62 % of the time needed to accomplish the CS. We conclude that GSV may be a cost-effective alternative to CS. We discuss some advantages and limitations of GSV as a survey method. We forecast that GSV may become a widespread tool in road ecology, particularly in large-scale vegetation assessments.

Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach

MOTIVATION

Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters.

RESULTS

In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two-component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CONTACT

julio@iim.csic.es or saezrodriguez@ebi.ac.uk.