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The honeybee (Apis mellifera L.) has been used in several studies for monitoring the environmental health status in terms of pollution, due to its wide-ranging foraging flights. Based on this consideration, this study aimed to analyze heavy metal pollution in Molise Region (Italy), by investigating five sites characterized by different levels of contamination. Furthermore, the authors carried out a sampling activity for a long period, in order to obtain a complete dataset. In this way, detailed information about the status of the environments was able to be obtained. The main purpose of this work was to assess the health status of Molise Region and to confirm the suitability of honeybees as environmental bioindicators of heavy metal pollution, by analyzing their variability over time and space. Furthermore, the study compared the health status associated with contamination in terms of heavy metals with that in two different areas of Italy, using hierarchical cluster analysis and principal component analysis, to evaluate the correlation existing among the three different areas of Italy. Following the findings, the authors suggest the use of honeybees as a bioindicator for heavy metal pollution in air quality studies.

Targeted sequencing of PCR amplicons generated from bisulfite deaminated DNA is a flexible, cost-effective way to study methylation of a sample at single CpG resolution and perform subsequent multi-target, multi-sample comparisons. Currently, no platform specific protocol, support, or analysis solution is provided to perform targeted bisulfite sequencing on a Personal Genome Machine (PGM). Here, we present a novel tool, called TABSAT, for analyzing targeted bisulfite sequencing data generated on Ion Torrent sequencers. The workflow starts with raw sequencing data, performs quality assessment, and uses a tailored version of Bismark to map the reads to a reference genome. The pipeline visualizes results as lollipop plots and is able to deduce specific methylation-patterns present in a sample. The obtained profiles are then summarized and compared between samples. In order to assess the performance of the targeted bisulfite sequencing workflow, 48 samples were used to generate 53 different Bisulfite-Sequencing PCR amplicons from each sample, resulting in 2,544 amplicon targets. We obtained a mean coverage of 282X using 1,196,822 aligned reads. Next, we compared the sequencing results of these targets to the methylation level of the corresponding sites on an Illumina 450k methylation chip. The calculated average Pearson correlation coefficient of 0.91 confirms the sequencing results with one of the industry-leading CpG methylation platforms and shows that targeted amplicon bisulfite sequencing provides an accurate and cost-efficient method for DNA methylation studies, e.g., to provide platform-independent confirmation of Illumina Infinium 450k methylation data. TABSAT offers a novel way to analyze data generated by Ion Torrent instruments and can also be used with data from the Illumina MiSeq platform. It can be easily accessed via the Platomics platform, which offers a web-based graphical user interface along with sample and parameter storage. TABSAT is freely available under a GNU General Public License version 3.0 (GPLv3) at https://github.com/tadkeys/tabsat/ and http://demo.platomics.com/.

Background One of the consequences of the rapid and widespread adoption of high-throughput experimental technologies is an exponential increase of the amount of data produced by genome-wide experiments. Researchers increasingly need to handle very large volumes of heterogeneous data, including both the data generated by their own experiments and the data retrieved from publicly available repositories of genomic knowledge. Integration, exploration, manipulation and interpretation of data and information therefore need to become as automated as possible, since their scale and breadth are, in general, beyond the limits of what individual researchers and the basic data management tools in normal use can handle. This paper describes Genephony, a tool we are developing to address these challenges. Results We describe how Genephony can be used to manage large datesets of genomic information, integrating them with existing knowledge repositories. We illustrate its functionalities with an example of a complex annotation task, in which a set of SNPs coming from a genotyping experiment is annotated with genes known to be associated to a phenotype of interest. We show how, thanks to the modular architecture of Genephony and its user-friendly interface, this task can be performed in a few simple steps. Conclusion Genephony is an online tool for the manipulation of large datasets of genomic information. It can be used as a browser for genomic data, as a high-throughput annotation tool, and as a knowledge discovery tool. It is designed to be easy to use, flexible and extensible. Its knowledge management engine provides fine-grained control over individual data elements, as well as efficient operations on large datasets.

Background Linking genotypic and phenotypic information is one of the greatest challenges of current genetics research. The definition of an Information Technology infrastructure to support this kind of studies, and in particular studies aimed at the analysis of complex traits, which require the definition of multifaceted phenotypes and the integration genotypic information to discover the most prevalent diseases, is a paradigmatic goal of Biomedical Informatics. This paper describes the use of Information Technology methods and tools to develop a system for the management, inspection and integration of phenotypic and genotypic data. Results We present the design and architecture of the Phenotype Miner, a software system able to flexibly manage phenotypic information, and its extended functionalities to retrieve genotype information from external repositories and to relate it to phenotypic data. For this purpose we developed a module to allow customized data upload by the user and a SOAP-based communications layer to retrieve data from existing biomedical knowledge management tools. In this paper we also demonstrate the system functionality by an example application of the system in which we analyze two related genomic datasets. Conclusion In this paper we show how a comprehensive, integrated and automated workbench for genotype and phenotype integration can facilitate and improve the hypothesis generation process underlying modern genetic studies.

Background Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees. Results We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%. Conclusion The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.

Polycyclic aromatic hydrocarbons (PAHs), heavy metals, and plasticizer residues are continuously released into the environment. The use of living organisms, such as Apis mellifera L . and honey, is advantageous as bioindicator of the environmental health status, instead of traditional monitoring methods, showing the ability to record spatial and temporal pollutant variations. The PAHs and heavy metal presence were determined in two sampling years (2017 and 2018) in five different locations in the Molise region (Italy), characterized by different pollution levels. During 2017, most PAHs in all samples were lower than limit of detection (LOD), while in 2018, their mean concentration in bee and honey samples was of 3 μg kg −1 and 35 μg kg −1 , respectively. For heavy metals, lower values were detected in 2017 (Be, Cd, and V below LOD), while in 2018, the mean concentrations were higher, 138 μg kg −1 and 69 μg kg −1 , in bees and honey, respectively. Honey has been used as indicator of the presence of phthalate esters and bisphenol A in the environment. The satisfactory results confirmed that both bees and honey are an important tool for environmental monitoring. The chemometric analysis highlighted the differences in terms of pollutant concentration and variability in the different areas, validating the suitability of these matrices as bioindicators.

Background Kinase over-expression and activation as a consequence of gene amplification or gene fusion events is a well-known mechanism of tumorigenesis. The search for novel rearrangements of kinases or other druggable genes may contribute to understanding the biology of cancerogenesis, as well as lead to the identification of new candidate targets for drug discovery. However this requires the ability to query large datasets to identify rare events occurring in very small fractions (1–3 %) of different tumor subtypes. This task is different from what is normally done by conventional tools that are able to find genes differentially expressed between two experimental conditions. Results We propose a computational method aimed at the automatic identification of genes which are selectively over-expressed in a very small fraction of samples within a specific tissue. The method does not require a healthy counterpart or a reference sample for the analysis and can be therefore applied also to transcriptional data generated from cell lines. In our implementation the tool can use gene-expression data from microarray experiments, as well as data generated by RNASeq technologies. Conclusions The method was implemented as a publicly available, user-friendly tool called KAOS (Kinase Automatic Outliers Search). The tool enables the automatic execution of iterative searches for the identification of extreme outliers and for the graphical visualization of the results. Filters can be applied to select the most significant outliers. The performance of the tool was evaluated using a synthetic dataset and compared to state-of-the-art tools. KAOS performs particularly well in detecting genes that are overexpressed in few samples or when an extreme outlier stands out on a high variable expression background. To validate the method on real case studies, we used publicly available tumor cell line microarray data, and we were able to identify genes which are known to be overexpressed in specific samples, as well as novel ones.

The modern double horn came as the next development at the beginning of the 20th century, in order to overcome some of the problems of the Wiener Horn, namely: 1) the risk of missing notes, especially in the high register; 2) the relative strength and mastery required to play at a professional level; and 3) the relatively slow articulation due to the so-called \"pumpenvalves,\" which give an advantage to achieve smooth legati but a limitation in case of very fast staccati.i This is why the modern double horn presents the combination of an extra complete B-flat circuit of valves and tubes on top of an F base tubing. Nickel silver has a positive effect on the attack, gold brass makes the horn sound beautiful, and yellow brass centers the tones and brings a peculiar mellow quality to the sound. The double horn is usually held freely around the body, with the hand deep inside the bell, obtaining an overall well-balanced weight distribution. The Wiener Horn weight distribution is higher on the left hand; therefore, it is more comfortable (for most players) to lean the bell against the body and keep the right hand more outside of the bell (Figure 6).

Cancer is one of the leading causes of death globally. Anticancer drugs aim to block tumor growth by killing cancerous cells in order to prevent tumor progression and metastasis. Efficient anticancer drugs should also minimize general toxicity towards organs and healthy cells. Tumor growth can also be successfully restrained by targeting and modulating immune response. Cancer immunotherapy is assuming a growing relevance in the fight against cancer and has recently aroused much interest for its wider safety and the capability to complement conventional chemotherapeutic approaches. Natural products are a traditional source of molecules with relevant potential in the pharmacological field. The huge structural diversity of metabolites with low molecular weight (small molecules) from terrestrial and marine organisms has provided lead compounds for the discovery of many modern anticancer drugs. Many natural products combine chemo-protective and immunomodulant activity, thus offering the potential to be used alone or in association with conventional cancer therapy. In this review, we report the natural products known to possess antitumor properties by interaction with immune system, as well as discuss the possible immunomodulatory mechanisms of these molecules.