The Ginevra de\u2019 Benci Effect: Competence, Morality, and Attractiveness Inferred From Faces Predict Hiring Decisions for Women

The present study examined the role of morality, competence, and attractiveness as perceived from faces in predicting hiring decisions for men and women. Results showed that for both female and male applicants, facial competence significantly predicted the hiring decision directly and indirectly, through the mediation of the overall impression. Decisions concerning female applicants were, however, significantly predicted by multiple dimensions\u2014that is, facial morality, facial competence, and attractiveness\u2014with the mediation of the overall impression. Facial competence was the only significant predictor of impression and, in turn, hiring decision about men. These findings resonate the motto Virtutem forma decorat, \u201cBeauty adorns virtue,\u201d painted by Leonardo da Vinci on the reverse side of the portrait of Ginevra de\u2019 Benci, and suggest that women\u2019s chances of getting a job are less than those of men whenever they do not show a moral and competent and attractive face

SEAL: a distributed short read mapping and duplicate removal tool

Summary: SEAL is a scalable tool for short read pair mapping and duplicate removal. It computes mappings that are consistent with those produced by BWA and removes duplicates according to the same criteria employed by Picard MarkDuplicates. On a 16-node Hadoop cluster, it is capable of processing about 13 GB per hour in map+rmdup mode, while reaching a throughput of 19 GB per hour in mapping-only mode

Nanosuspensions and microneedles roller as a combined approach to enhance diclofenac topical bioavailability

Topical application of the anti-inflammatory drug diclofenac (DCF) reduces the severity of systemic unwanted effects compared to its oral administration. A number of transdermal formulations are available on the market and routinely used in clinical and home-care settings. However, the amount of DCF delivered across the skin remains limited and often insufficient, thus making the oral route still necessary for achieving sufficient drug concentration at the inflamed site. In attempting to improve the transdermal penetration, we explored the combined use of DCF nanosuspensions with a microneedle roller. Firstly, DCF nanosuspensions were prepared by a top-down media milling method and characterized by spectroscopic, thermal and electron microscopy analyses. Secondly, the pore-forming action of microneedle rollers on skin specimens (ex vivo) was described by imaging at different scales. Finally, DCF nanosuspensions were applied on newborn pig skin (in vitro) in combination with microneedles roller treatment, assessing the DCF penetration and distribution in the different skin layers. The relative contribution of microneedle length, nanosuspension stabilizer and application sequence could be identified by systemically varying these parameters

The deficit bias: Candidate gender differences in the relative importance of facial stereotypic qualities for leadership hiring

This is the final version. Available on open access from Wiley via the DOI in this recordData availability: The authors confirm that the data supporting the main findings of this research are available online within the supporting information.Recent findings highlight two facets of the two fundamental stereotype content dimensions of agency (i.e., ‘dominance’ and ‘competence’) and communality (i.e., ‘morality’ and ‘sociability’; e.g., Abele et al., 2016) with implications for understanding gender inequality in the workplace (e.g., Prati et al., 2019). Extending this research and contributing to the facial first impressions literature, we examined how these facets of agency and communality when inferred from White men’s and women’s faces, along with attractiveness, influence their leadership suitability. In three studies in the UK (total N = 424), using student and working samples and two managerial descriptions, we found an unexpected pattern of results, supported by an internal meta-analysis: attractiveness and competence were the most important predictors of hirability for all candidates. For women, dominance was the next most important predictor; for men, morality and sociability were more important than dominance. Moreover, morality and sociability were more important in evaluating men than women, whilst dominance was more important in evaluating women than men. Findings are discussed in terms of a ‘deficit bias’, whereby the qualities women and men are considered to lack – dominance for women, morality and sociability for men – may be given more weight when evaluating their leadership suitability.European CommissionItalian Ministry of Education, Universities and Research (MIUR)European Association of Social Psycholog

PPCAS: Implementation of a Probabilistic Pairwise Model for Consistency-Based Multiple Alignment in Apache Spark

Large-scale data processing techniques, currently known as Big-Data, are used to manage the huge amount of data that are generated by sequencers. Although these techniques have significant advantages, few biological applications have adopted them. In the Bioinformatic scientific area, Multiple Sequence Alignment (MSA) tools are widely applied for evolution and phylogenetic analysis, homology and domain structure prediction. Highly-rated MSA tools, such as MAFFT, ProbCons and T-Coffee (TC), use the probabilistic consistency as a prior step to the progressive alignment stage in order to improve the final accuracy. In this paper, a novel approach named PPCAS (Probabilistic Pairwise model for Consistency-based multiple alignment in Apache Spark) is presented. PPCAS is based on the MapReduce processing paradigm in order to enable large datasets to be processed with the aim of improving the performance and scalability of the original algorithm.This work was supported by the MEyC-Spain [contract TIN2014-53234-C2-2-R]

Molecular-biology-driven treatment for metastatic colorectal cancer

Background: Metastatic CRC (mCRC) is a molecular heterogeneous disease. The aim of this review is to give an overview of molecular-driven treatment of mCRC patients. Methods: A review of clinical trials, retrospective studies and case reports was performed regarding molecular biomarkers with therapeutic implications. Results: RAS wild-type status was confirmed as being crucial for anti-epidermal growth factor receptor (EGFR) monoclonal antibodies and for rechallenge strategy. Antiangiogenic therapies improve survival in first- and second-line settings, irrespective of RAS status, while tyrosine kinase inhibitors (TKIs) remain promising in refractory mCRC. Promising results emerged from anti-HER2 drugs trials in HER2-positive mCRC. Target inhibitors were successful for BRAFV600E mutant mCRC patients, while immunotherapy was successful for microsatellite instability-high/defective mismatch repair (MSI-H/dMMR) or DNA polymerase epsilon catalytic subunit (POLE-1) mutant patients. Data are still lacking on NTRK, RET, MGMT, and TGF-β, which require further research. Conclusion: Several molecular biomarkers have been identified for the tailored treatment of mCRC patients and multiple efforts are currently ongoing to increase the therapeutic options. In the era of precision medicine, molecular-biology-driven treatment is the key to impro patient selection and patient outcomes. Further research and large phase III trials are required to ameliorate the therapeutic management of these patients

A markov classification model for metabolic pathways

<p>Abstract</p> <p>Background</p> <p>This paper considers the problem of identifying pathways through metabolic networks that relate to a specific biological response. Our proposed model, HME3M, first identifies frequently traversed network paths using a Markov mixture model. Then by employing a hierarchical mixture of experts, separate classifiers are built using information specific to each path and combined into an ensemble prediction for the response.</p> <p>Results</p> <p>We compared the performance of HME3M with logistic regression and support vector machines (SVM) for both simulated pathways and on two metabolic networks, glycolysis and the pentose phosphate pathway for <it>Arabidopsis thaliana</it>. We use AltGenExpress microarray data and focus on the pathway differences in the developmental stages and stress responses of <it>Arabidopsis</it>. The results clearly show that HME3M outperformed the comparison methods in the presence of increasing network complexity and pathway noise. Furthermore an analysis of the paths identified by HME3M for each metabolic network confirmed known biological responses of <it>Arabidopsis</it>.</p> <p>Conclusions</p> <p>This paper clearly shows HME3M to be an accurate and robust method for classifying metabolic pathways. HME3M is shown to outperform all comparison methods and further is capable of identifying known biologically active pathways within microarray data.</p

Interoperable and scalable data analysis with microservices: applications in metabolomics.

Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science. The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. Supplementary data are available at Bioinformatics online

ComPath: comparative enzyme analysis and annotation in pathway/subsystem contexts

<p>Abstract</p> <p>Background</p> <p>Once a new genome is sequenced, one of the important questions is to determine the presence and absence of biological pathways. Analysis of biological pathways in a genome is a complicated task since a number of biological entities are involved in pathways and biological pathways in different organisms are not identical. Computational pathway identification and analysis thus involves a number of computational tools and databases and typically done in comparison with pathways in other organisms. This computational requirement is much beyond the capability of biologists, so information systems for reconstructing, annotating, and analyzing biological pathways are much needed. We introduce a new comparative pathway analysis workbench, ComPath, which integrates various resources and computational tools using an interactive spreadsheet-style web interface for reliable pathway analyses.</p> <p>Results</p> <p>ComPath allows users to compare biological pathways in multiple genomes using a spreadsheet style web interface where various sequence-based analysis can be performed either to compare enzymes (e.g. sequence clustering) and pathways (e.g. pathway hole identification), to search a genome for <it>de novo </it>prediction of enzymes, or to annotate a genome in comparison with reference genomes of choice. To fill in pathway holes or make <it>de novo </it>enzyme predictions, multiple computational methods such as FASTA, Whole-HMM, CSR-HMM (a method of our own introduced in this paper), and PDB-domain search are integrated in ComPath. Our experiments show that FASTA and CSR-HMM search methods generally outperform Whole-HMM and PDB-domain search methods in terms of sensitivity, but FASTA search performs poorly in terms of specificity, detecting more false positive as E-value cutoff increases. Overall, CSR-HMM search method performs best in terms of both sensitivity and specificity. Gene neighborhood and pathway neighborhood (global network) visualization tools can be used to get context information that is complementary to conventional KEGG map representation.</p> <p>Conclusion</p> <p>ComPath is an interactive workbench for pathway reconstruction, annotation, and analysis where experts can perform various sequence, domain, context analysis, using an intuitive and interactive spreadsheet-style interface. </p