264 research outputs found

    HSAF-induced antifungal effects in Candida albicans through ROS-mediated apoptosis

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    Heat-stable antifungal factor (HSAF) belongs to polycyclic tetramate macrolactams (PTMs), which inhibits many fungal pathogens and is effective in inhibiting Candida albicans (C. albicans). In this study, we found that HSAF induced the apoptosis of C. albicans SC5314 through inducing the production of reactive oxygen species (ROS). Nevertheless, we validated the efficacy of HSAF against candidiasis caused by C. albicans in a murine model in vivo,and HSAF significantly improved survival and reduced fungal burden compared to vehicles. A molecular dynamics (MD) simulation was also investigated, revealing the theoretical binding mode of HSAF to the β-tubulin of C. albicans. This study first found PTMs-induced fungal apoptosis through ROS accumulation in C. albicans and its potential as a novel agent for fungicides

    Mutations causing medullary cystic kidney disease type 1 (MCKD1) lie in a large VNTR in MUC1 missed by massively parallel sequencing

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    While genetic lesions responsible for some Mendelian disorders can be rapidly discovered through massively parallel sequencing (MPS) of whole genomes or exomes, not all diseases readily yield to such efforts. We describe the illustrative case of the simple Mendelian disorder medullary cystic kidney disease type 1 (MCKD1), mapped more than a decade ago to a 2-Mb region on chromosome 1. Ultimately, only by cloning, capillary sequencing, and de novo assembly, we found that each of six MCKD1 families harbors an equivalent, but apparently independently arising, mutation in sequence dramatically underrepresented in MPS data: the insertion of a single C in one copy (but a different copy in each family) of the repeat unit comprising the extremely long (~1.5-5 kb), GC-rich (>80%), coding VNTR in the mucin 1 gene. The results provide a cautionary tale about the challenges in identifying genes responsible for Mendelian, let alone more complex, disorders through MPS

    Mutations causing medullary cystic kidney disease type 1 lie in a large VNTR in MUC1 missed by massively parallel sequencing

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    Although genetic lesions responsible for some mendelian disorders can be rapidly discovered through massively parallel sequencing of whole genomes or exomes, not all diseases readily yield to such efforts. We describe the illustrative case of the simple mendelian disorder medullary cystic kidney disease type 1 (MCKD1), mapped more than a decade ago to a 2-Mb region on chromosome 1. Ultimately, only by cloning, capillary sequencing and de novo assembly did we find that each of six families with MCKD1 harbors an equivalent but apparently independently arising mutation in sequence markedly under-represented in massively parallel sequencing data: the insertion of a single cytosine in one copy (but a different copy in each family) of the repeat unit comprising the extremely long (~1.5–5 kb), GC-rich (>80%) coding variable-number tandem repeat (VNTR) sequence in the MUC1 gene encoding mucin 1. These results provide a cautionary tale about the challenges in identifying the genes responsible for mendelian, let alone more complex, disorders through massively parallel sequencing.National Institutes of Health (U.S.) (Intramural Research Program)National Human Genome Research Institute (U.S.)Charles University (program UNCE 204011)Charles University (program PRVOUK-P24/LF1/3)Czech Republic. Ministry of Education, Youth, and Sports (grant NT13116-4/2012)Czech Republic. Ministry of Health (grant NT13116-4/2012)Czech Republic. Ministry of Health (grant LH12015)National Institutes of Health (U.S.) (Harvard Digestive Diseases Center, grant DK34854

    Challenge clusters facing LCA in environmental decision-making—what we can learn from biofuels

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    Purpose Bioenergy is increasingly used to help meet greenhouse gas (GHG) and renewable energy targets. However, bioenergy’s sustainability has been questioned, resulting in increasing use of life cycle assessment (LCA). Bioenergy systems are global and complex, and market forces can result in significant changes, relevant to LCA and policy. The goal of this paper is to illustrate the complexities associated with LCA, with particular focus on bioenergy and associated policy development, so that its use can more effectively inform policymakers. Methods The review is based on the results from a series of workshops focused on bioenergy life cycle assessment. Expert submissions were compiled and categorized within the first two workshops. Over 100 issues emerged. Accounting for redundancies and close similarities in the list, this reduced to around 60 challenges, many of which are deeply interrelated. Some of these issues were then explored further at a policyfacing workshop in London, UK. The authors applied a rigorous approach to categorize the challenges identified to be at the intersection of biofuels/bioenergy LCA and policy. Results and discussion The credibility of LCA is core to its use in policy. Even LCAs that comply with ISO standards and policy and regulatory instruments leave a great deal of scope for interpretation and flexibility. Within the bioenergy sector, this has led to frustration and at times a lack of obvious direction. This paper identifies the main challenge clusters: overarching issues, application and practice and value and ethical judgments. Many of these are reflective of the transition from application of LCA to assess individual products or systems to the wider approach that is becoming more common. Uncertainty in impact assessment strongly influences planning and compliance due to challenges in assigning accountability, and communicating the inherent complexity and uncertainty within bioenergy is becoming of greater importance. Conclusions The emergence of LCA in bioenergy governance is particularly significant because other sectors are likely to transition to similar governance models. LCA is being stretched to accommodate complex and broad policy-relevant questions, seeking to incorporate externalities that have major implications for long-term sustainability. As policy increasingly relies on LCA, the strains placed on the methodology are becoming both clearer and impedimentary. The implications for energy policy, and in particular bioenergy, are large

    An English cover-up: masks, murders, and English cruelty in Goncourt, Lorrain, and Schwob

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    Fin-de-siècle writers from diverse disciplines were drawn to the seductive potential of masks and disguise; mask-wearing characters of indefinite identity, indeterminate gender, and insecure psychology proliferate in their texts. However, when characters are designated as English in such stories, they are also, and with remarkable frequency, associated with cruelty or murder: the mask-wielding murderers of Marcel Schwob’s ‘MM. Burke et Hare, Assassins’ carry out their crimes in Britain upon British victims; Edmond de Goncourt weaves his theatrical narrative around the mask-like demeanour of Lord Annandale in La Faustin; and Jean Lorrain’s malicious Lord Ethal exacerbates the Duc de Fréneuse’s perverse obsessions with masks in Monsieur de Phocas. This article explores this unexpected correlation, and examines the ways that English masks are used as narrative devices – at once to mould and play with national distinctions, and to reflect upon the psychological state of the French subject

    Event reconstruction for KM3NeT/ORCA using convolutional neural networks

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    The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches
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