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Idiopathic torsion dystonia is a motor syndrome characterized by dystonic movements and postures in the absence of other neurologic deficits. The condition involves prolonged spasms of muscle contraction that distort the body into typical postures. Such distortions involving the head and the neck make conventional denture use in edentulous patients very difficult. The present paper reports on a patient with idiopathic torsion dystonia who was treated with a mandibular overdenture supported by endosteal implants, which enabled the establishment of a stable occlusion and improved the dynamics of the masticatory muscles for chewing. [ABSTRACT FROM AUTHOR]

Oromandibular dystonia with blepharospasm (also known as Brueghel's syndrome, Meige's syndrome, or idiopathic orofacial dystonia) is characterized by intense and involuntary spasms of the orofacial muscles, with a frequent loss of teeth and occlusal alterations that worsen the dystonic manifestations and cause mucosal lesions that can lead to complete edentulism. The history of a patient with oromandibular dystonia who was rehabilitated with mandibular overdentures supported by endosteal implants is presented. Oral rehabilitation with implant-supported overdentures improved the situation, despite serious problems with instability. Mandibular overdentures supported by endosteal implants were satisfactorily used to re-establish occlusion, ensuring prosthetic stability and improving the dynamics of the masticatory muscles. [ABSTRACT FROM AUTHOR]

PURPOSE: This study was conducted to evaluate the feasibility of placing fixed prostheses over endosseous implants in edentulous patients with recessive dystrophic epidermolysis bullosa. PATIENTS AND METHODS: Three cases of dental implants with fixed prostheses are presented. All 3 patients had marked oral involvement, with devastating alterations in the soft and hard tissues. The use of fixed prostheses avoids contact between the prosthesis itself and the oral mucosa, thus preventing the formation of blisters. RESULTS: A total of 27 implants (15 maxillary and 12 mandibular) were placed. One maxillary implant failed; the rest integrated successfully, allowing crown placement. The average follow-up after implant placement was 3 years (range, 1 to 5 years). CONCLUSIONS: Our findings suggest that endosseous implants are not contraindicated and can be placed successfully to improve these patients' quality of life. Implants can provide support for complete fixed prosthesis restoration in patients with epidermolysis bullosa. This procedure keeps the prosthesis from rubbing on the oral mucosa and prevents the development of ulcerations, improving mastication and esthetics. [ABSTRACT FROM AUTHOR]

Precision medicine aims at tailoring treatments to individual patient's characteristics. In this regard, recognizing the significance of sex and gender becomes indispensable for meeting the distinct healthcare needs of diverse populations. To this end, continuing a trend of improving data quality observed since 2014, the European Genome-phenome Archive (EGA) established a policy in 2018 that mandates data providers to declare the sex of donor samples, aiming to enhance data accuracy and prevent imbalance in sex classification. We analyzed sex classification imbalance in human data from EGA and the U.S. counterpart, the database of genotypes and phenotypes (dbGaP). Our findings show a significant decrease in samples classified as unknown in EGA, potentially promoting better sex reporting during data collection. Based on our findings, we raise awareness of sample imbalance problems and provide a list of recommendations for enhancing biomedical research practices., Competing Interests: A.J., M.M., J.R. develop, maintain and coordinate the EGA database. D.S., A.M.A., and G.L.H. work for private companies., (© 2024 The Author(s).)

Objective: This study addressed a COVID-19 outbreak in a nursing home, where the residents were vaccinated with two doses of Comirnaty® and thirty of them had previously had the infection. Outbreaks in closed communities are opportunities to study the entire clinical spectrum of the disease and, as in this case, the effect of hybrid immunity on transmission, infection progression and viral load., Methods: A descriptive and retrospective cohort study in the resident population was carried out. Attack rates were calculated for one of four stages of the disease: infection, symptomatic infection, hospitalization, and death. Relative risks (RR) were then estimated using simple and multivariate Poisson regression for each of these stages., Results: The attack rate was 59% (56/95). The clinical spectrum was the same in both sexes. There was a notable protective effect of hybrid immunity against transmission (67%). In terms of progression, those with hybrid immunity had a lower risk of symptomatic infection. Nasopharyngeal viral load was significantly lower in individuals with hybrid immunity and asymptomatic individuals, supporting the idea of lower transmissibility in this group. Age was identified as a risk factor for disease progression. Molecular analysis identified the Delta B.1.617.2 variant in the patients and an air sample, supporting aerosol transmission in closed, poorly ventilated environments., Conclusions: This study provides a comprehensive view of an outbreak in a vaccinated nursing home, highlighting the importance of hybrid immunity. The results support the individual consideration of previous infection history when assessing the risk of COVID-19, contributing to the understanding of the evolution of the pandemic in the future.

Competing Interests: The authors have declared that no competing interests exist.

The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Danis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of 'sick-care' to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single-institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area.Key points• Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata.• Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data.• Developing a common data model for storing all relevant information is a challenge.• Trust of data providers in data sharing initiatives is essential.• An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area., (© 2023. The Author(s).)

Summary: Beacon v2 is an API specification established by the Global Alliance for Genomics and Health initiative (GA4GH) that defines a standard for federated discovery of genomic and phenotypic data. Here, we present the Beacon v2 Reference Implementation (B2RI), a set of open-source software tools that allow lighting up a local Beacon instance 'out-of-the-box'. Along with the software, we have created detailed 'Read the Docs' documentation that includes information on deployment and installation., Availability and Implementation: The B2RI is released under GNU General Public License v3.0 and Apache License v2.0. Documentation and source code is available at: https://b2ri-documentation.readthedocs.io., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant breakthrough infections in nursing home residents following vaccination with Comirnaty® COVID-19 vaccine were characterized. In total, 201 participants (median age, 87 years; range, 64-100; 133 female) from two nursing homes in the Valencian community (Spain) were included. SARS-CoV-2-Spike (S) antibody responses were determined by a lateral flow immunocromatography (LFIC) assay and by quantitative electrochemiluminescent assay in LFIC-negative participants. SARS-CoV-2-S-IFNγ T cells were enumerated by flow cytometry in 10 participants. Nasopharyngeal SARS-CoV-2 RNA loads were quantified by real-time polymerase chain reaction assays. Vaccine breakthrough COVID-19 due to the Delta variant occurred in 39 residents (median age, 87 years; range, 69-96; 31 female) at a median of 6.5 months after vaccination (nine requiring hospitalization). Breakthrough infections occurred at a higher rate (p < 0.0001) in residents who had not been previously infected with SARS-CoV-2 (naïve) (33/108; 18%) than in those with prior diagnosis of SARS-CoV-2 infection (experienced) (6/93; 6.4%), and were more likely (p < 0.0001) to develop in residents who tested negative by LFIC (20/49) at 3 months after vaccination as compared to their LFIC-positive counterparts (19/142). Among LFIC-negative residents, a trend towards lower plasma anti-RBD antibody levels was noticed in those developing breakthrough infection (p = 0.16). SARS-CoV-2 RNA loads in nasopharyngeal specimens were lower in SARS-CoV-2-experienced residents (p < 0.001) and in those testing positive by LFIC (p = 0.13). The frequency of SARS-CoV-2-S-reactive T cells at 3 months was similar in LFIC-negative residents with (n = 7) or without (n = 3) breakthrough infection. Prior history of SARS-CoV-2 infection and detection of S-reactive antibodies by LFIC at 3 months is associated with a lower risk of Delta-variant breakthrough infection in nursing home residents at midterm after Comirnaty® COVID-19 vaccination., (© 2022 Wiley Periodicals LLC.)

Beacon is a basic data discovery protocol issued by the Global Alliance for Genomics and Health (GA4GH). The main goal addressed by version 1 of the Beacon protocol was to test the feasibility of broadly sharing human genomic data, through providing simple "yes" or "no" responses to queries about the presence of a given variant in datasets hosted by Beacon providers. The popularity of this concept has fostered the design of a version 2, that better serves real-world requirements and addresses the needs of clinical genomics research and healthcare, as assessed by several contributing projects and organizations. Particularly, rare disease genetics and cancer research will benefit from new case level and genomic variant level requests and the enabling of richer phenotype and clinical queries as well as support for fuzzy searches. Beacon is designed as a "lingua franca" to bridge data collections hosted in software solutions with different and rich interfaces. Beacon version 2 works alongside popular standards like Phenopackets, OMOP, or FHIR, allowing implementing consortia to return matches in beacon responses and provide a handover to their preferred data exchange format. The protocol is being explored by other research domains and is being tested in several international projects., (© 2022 The Authors. Human Mutation published by Wiley Periodicals LLC.)

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes., (© 2022 The Authors. Human Mutation published by Wiley Periodicals LLC.)

Since its launch in 2008, the European Genome-Phenome Archive (EGA) has been leading the archiving and distribution of human identifiable genomic data. In this regard, one of the community concerns is the potential usability of the stored data, as of now, data submitters are not mandated to perform any quality control (QC) before uploading their data and associated metadata information. Here, we present a new File QC Portal developed at EGA, along with QC reports performed and created for 1 694 442 files [Fastq, sequence alignment map (SAM)/binary alignment map (BAM)/CRAM and variant call format (VCF)] submitted at EGA. QC reports allow anonymous EGA users to view summary-level information regarding the files within a specific dataset, such as quality of reads, alignment quality, number and type of variants and other features. Researchers benefit from being able to assess the quality of data prior to the data access decision and thereby, increasing the reusability of data (https://ega-archive.org/blog/data-upcycling-powered-by-ega/)., (© The Author(s) 2022. Published by Oxford University Press.)

The European Genome-phenome Archive (EGA - https://ega-archive.org/) is a resource for long term secure archiving of all types of potentially identifiable genetic, phenotypic, and clinical data resulting from biomedical research projects. Its mission is to foster hosted data reuse, enable reproducibility, and accelerate biomedical and translational research in line with the FAIR principles. Launched in 2008, the EGA has grown quickly, currently archiving over 4,500 studies from nearly one thousand institutions. The EGA operates a distributed data access model in which requests are made to the data controller, not to the EGA, therefore, the submitter keeps control on who has access to the data and under which conditions. Given the size and value of data hosted, the EGA is constantly improving its value chain, that is, how the EGA can contribute to enhancing the value of human health data by facilitating its submission, discovery, access, and distribution, as well as leading the design and implementation of standards and methods necessary to deliver the value chain. The EGA has become a key GA4GH Driver Project, leading multiple development efforts and implementing new standards and tools, and has been appointed as an ELIXIR Core Data Resource., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Human biomedical datasets that are critical for research and clinical studies to benefit human health also often contain sensitive or potentially identifying information of individual participants. Thus, care must be taken when they are processed and made available to comply with ethical and regulatory frameworks and informed consent data conditions. To enable and streamline data access for these biomedical datasets, the Global Alliance for Genomics and Health (GA4GH) Data Use and Researcher Identities (DURI) work stream developed and approved the Data Use Ontology (DUO) standard. DUO is a hierarchical vocabulary of human and machine-readable data use terms that consistently and unambiguously represents a dataset's allowable data uses. DUO has been implemented by major international stakeholders such as the Broad and Sanger Institutes and is currently used in annotation of over 200,000 datasets worldwide. Using DUO in data management and access facilitates researchers' discovery and access of relevant datasets. DUO annotations increase the FAIRness of datasets and support data linkages using common data use profiles when integrating the data for secondary analyses. DUO is implemented in the Web Ontology Language (OWL) and, to increase community awareness and engagement, hosted in an open, centralized GitHub repository. DUO, together with the GA4GH Passport standard, offers a new, efficient, and streamlined data authorization and access framework that has enabled increased sharing of biomedical datasets worldwide., Competing Interests: M.N.C. is an employee of Foundation Medicine and equity holder of Roche. A.A.P. is a venture partner at GV and an employee of alphabet corporation. He has received funding from MSFT, Verily, IBM, Intel, Bayer, and Novartis. The views expressed by L.L.R. are the author’s own and do not necessarily represent those of her organization., (© 2021 The Author(s).)

We promote a shared vision and guide for how and when to federate genomic and health-related data sharing, enabling connections and insights across independent, secure databases. The GA4GH encourages a federated approach wherein data providers have the mandate and resources to share, but where data cannot move for legal or technical reasons. We recommend a federated approach to connect national genomics initiatives into a global network and precision medicine resource., Competing Interests: DECLARATION OF INTERESTS M. Brudno holds financial interest in PhenoTips. E.B. is a consultant to Oxford Nanopore Technologies and Dovetail Inc. and a member of the Cell Genomics advisory board. H.L.R. is a member of the Cell Genomics advisory board. All other authors have no interests to declare.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

Due to their effects on reducing recombination, chromosomal inversions may play an important role in speciation by establishing and/or maintaining linked blocks of genes causing reproductive isolation (RI) between populations. This view fits empirical data indicating that inversions typically harbor loci involved in RI. However, previous computer simulations of infinite populations with two to four loci involved in RI implied that, even with gene flux as low as 10 -8 per gamete, per generation between alternative arrangements, inversions may not have large, qualitative advantages over collinear regions in maintaining population differentiation after secondary contact. Here, we report that finite population sizes can help counteract the homogenizing consequences of gene flux, especially when several fitness-related loci reside within the inversion. In these cases, the persistence time of differentiation after secondary contact can be similar to when gene flux is absent and notably longer than the persistence time without inversions. Thus, despite gene flux, population differentiation may be maintained for up to 100,000 generations, during which time new incompatibilities and/or local adaptations might accumulate and facilitate progress toward speciation. How often these conditions are met in nature remains to be determined., (© 2021 The Authors. Evolution published by Wiley Periodicals LLC on behalf of The Society for the Study of Evolution.)

Motivation: Association studies based on SNP arrays and Next Generation Sequencing technologies have enabled the discovery of thousands of genetic loci related to human diseases. Nevertheless, their biological interpretation is still elusive, and their medical applications limited. Recently, various tools have been developed to help bridging the gap between genomes and phenomes. To our knowledge, however none of these tools allows users to retrieve the phenotype-wide list of genetic variants that may be linked to a given disease or to visually explore the joint genetic architecture of different pathologies., Results: We present the Genome-Phenome Explorer (GePhEx), a web-tool easing the visual exploration of phenotypic relationships supported by genetic evidences. GePhEx is primarily based on the thorough analysis of linkage disequilibrium between disease-associated variants and also considers relationships based on genes, pathways or drug-targets, leveraging on publicly available variant-disease associations to detect potential relationships between diseases. We demonstrate that GePhEx does retrieve well-known relationships as well as novel ones, and that, thus, it might help shedding light on the patho-physiological mechanisms underlying complex diseases. To this end, we investigate the potential relationship between schizophrenia and lung cancer, first detected using GePhEx and provide further evidence supporting a functional link between them., Availability and Implementation: GePhEx is available at: https://gephex.ega-archive.org/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Human genomics is undergoing a step change from being a predominantly research-driven activity to one driven through health care as many countries in Europe now have nascent precision medicine programmes. To maximize the value of the genomic data generated, these data will need to be shared between institutions and across countries. In recognition of this challenge, 21 European countries recently signed a declaration to transnationally share data on at least 1 million human genomes by 2022. In this Roadmap, we identify the challenges of data sharing across borders and demonstrate that European research infrastructures are well-positioned to support the rapid implementation of widespread genomic data access.

In the version of this article initially published, Lena Dolman's second affiliation was given as Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. The correct second affiliation is Ontario Institute for Cancer Research, Toronto, Ontario, Canada. The error has been corrected in the HTML and PDF versions of the article.

The availability of high-throughput molecular profiling techniques has provided more accurate and informative data for regular clinical studies. Nevertheless, complex computational workflows are required to interpret these data. Over the past years, the data volume has been growing explosively, requiring robust human data management to organise and integrate the data efficiently. For this reason, we set up an ELIXIR implementation study, together with the Translational research IT (TraIT) programme, to design a data ecosystem that is able to link raw and interpreted data. In this project, the data from the TraIT Cell Line Use Case (TraIT-CLUC) are used as a test case for this system. Within this ecosystem, we use the European Genome-phenome Archive (EGA) to store raw molecular profiling data; tranSMART to collect interpreted molecular profiling data and clinical data for corresponding samples; and Galaxy to store, run and manage the computational workflows. We can integrate these data by linking their repositories systematically. To showcase our design, we have structured the TraIT-CLUC data, which contain a variety of molecular profiling data types, for storage in both tranSMART and EGA. The metadata provided allows referencing between tranSMART and EGA, fulfilling the cycle of data submission and discovery; we have also designed a data flow from EGA to Galaxy, enabling reanalysis of the raw data in Galaxy. In this way, users can select patient cohorts in tranSMART, trace them back to the raw data and perform (re)analysis in Galaxy. Our conclusion is that the majority of metadata does not necessarily need to be stored (redundantly) in both databases, but that instead FAIR persistent identifiers should be available for well-defined data ontology levels: study, data access committee, physical sample, data sample and raw data file. This approach will pave the way for the stable linkage and reuse of data., Competing Interests: Competing interests: No competing interests were disclosed.

High-throughput molecular profiling techniques are routinely generating vast amounts of data for translational medicine studies. Secure access controlled systems are needed to manage, store, transfer and distribute these data due to its personally identifiable nature. The European Genome-phenome Archive (EGA) was created to facilitate access and management to long-term archival of bio-molecular data. Each data provider is responsible for ensuring a Data Access Committee is in place to grant access to data stored in the EGA. Moreover, the transfer of data during upload and download is encrypted. ELIXIR, a European research infrastructure for life-science data, initiated a project (2016 Human Data Implementation Study) to understand and document the ELIXIR requirements for secure management of controlled-access data. As part of this project, a full ecosystem was designed to connect archived raw experimental molecular profiling data with interpreted data and the computational workflows, using the CTMM Translational Research IT (CTMM-TraIT) infrastructure http://www.ctmm-trait.nl as an example. Here we present the first outcomes of this project, a framework to enable the download of EGA data to a Galaxy server in a secure way. Galaxy provides an intuitive user interface for molecular biologists and bioinformaticians to run and design data analysis workflows. More specifically, we developed a tool -- ega&#95;download&#95;streamer - that can download data securely from EGA into a Galaxy server, which can subsequently be further processed. This tool will allow a user within the browser to run an entire analysis containing sensitive data from EGA, and to make this analysis available for other researchers in a reproducible manner, as shown with a proof of concept study.  The tool ega&#95;download&#95;streamer is available in the Galaxy tool shed: https://toolshed.g2.bx.psu.edu/view/yhoogstrate/ega&#95;download&#95;streamer., Competing Interests: Competing interests: No competing interests were disclosed.

We set out to investigate potential differences and similarities between the selective forces acting upon the coding and noncoding regions of five different sets of genes defined according to functional and evolutionary criteria: 1) two reference gene sets presenting accelerated and slow rates of protein evolution (the Complement and Actin pathways); 2) a set of genes with evidence of accelerated evolution in at least one of their introns; and 3) two gene sets related to neurological function (Parkinson's and Alzheimer's diseases). To that effect, we combine human-chimpanzee divergence patterns with polymorphism data obtained from target resequencing 20 central chimpanzees, our closest relatives with largest long-term effective population size. By using the distribution of fitness effect-alpha extension of the McDonald-Kreitman test, we reproduce inferences of rates of evolution previously based only on divergence data on both coding and intronic sequences and also obtain inferences for other classes of genomic elements (untranslated regions, promoters, and conserved noncoding sequences). Our results suggest that 1) the distribution of fitness effect-alpha method successfully helps distinguishing different scenarios of accelerated divergence (adaptation or relaxed selective constraints) and 2) the adaptive history of coding and noncoding sequences within the gene sets analyzed is decoupled., (© The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

This work describes a rapid analytical method based on direct sample injection of water samples for the simultaneous identification/quantification of 40 emerging compounds, including pharmaceuticals and drugs of abuse. The water samples were analyzed by ultra-high-performance liquid chromatography coupled to hybrid triple quadrupole mass spectrometer (UHPLC-MS/MS QqQ). Taking profit of the increasing sensitivity of nowadays tandem mass spectrometers, direct sample injection of large volumes has been an attractive alternative to pre-concentration steps. In this work, the developed methodology has been validated at three concentration levels (10, 100 and 1000 ng/L) in 10 different water samples of different types (5 effluent wastewater and 5 surface water samples). The majority of compounds could be satisfactory validated at these concentrations, showing good recoveries and precision. With only few exceptions, the limits of quantification (LOQs), estimated from the sample chromatogram at lowest spiked level tested, were below 3 ng/L. The method was applied to the analysis of 10 effluent wastewater and 10 surface water samples. Venlafaxine was the compound most frequently detected (80%) in surface water, followed by acetaminophen (70%). Regarding effluent wastewater, valsartan and 4-acetyl aminoantipyrine were detected in 9 out of 10 samples analyzed. These two compounds together with 4-formyl aminoantipyrine and naproxen showed the highest concentrations (>2000 ng/L). In these cases, a dilution step was required for a correct quantification. As an additional evaluation of the method performance, the same water samples were analyzed in another laboratory by a second analytical methodology, based on on-line solid-phase-extraction coupled to LC-MS/MS (QqQ)., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Diversity and abundance of benthic macrofauna associated to Thalassia testudinum were studied at Ensenada de Reyes, Mochima Bay, in the northeastern coast of Venezuela. Samples were taken monthly in six stations, three at 1 m in depth and three at 6 m, between December 1992 and February 1994, using a quadrat of 0.25 m2 for collecting plants and sediment; each sample was washed with seawater through a 1 mm sieve. The specimens were fixed in 6% formaldehyde. A total of 1722 organisms (6 888 ind x m2) and 127 species of macroinvertebrates were collected. Mollusks dominated with 53 species, followed by polychaetes (40), crustaceans (18) and echinoderms (8). Remaining groups were represented by 1-2 species. The highest abundance was in October (214 specimens), and the lowest in December 1993 (79 specimens). Specific richness was between 47 species in October and 18 in May 1993. Mean species diversity was 2.79-1.36 bits/ind. There were differences (ANOVA p<0.01) in number of specimens at the two depths but not throughout the 15 month study period (p>0.05). There were more specimens and species at the lowest depth and in stations with higher Thalassia testudinum biomass.