Localization of Actin mRNA during the Establishment of Cell Polarity and Early Cell Divisions in Fucus Embryos

Localization of mRNA is a well-described mechanism to account for the asymmetric distribution of proteins in polarized somatic cells and embryos of animals. In zygotes of the brown alga Fucus, F-actin is localized at the site of polar growth and accumulates at the cell plates of the first two divisions of the embryo. We used a nonradioactive, whole-mount in situ hybridization protocol to show the pattern of actin mRNA localization. Until the first cell division, the pattern of actin mRNA localization is identical to that of total poly(A)+ RNA, that is, a symmetrical distribution in the zygote followed by an actin-dependent accumulation at the thallus pole at the time of polar axis fixation. At the end of the first division, actin mRNA specifically is redistributed from the thallus pole to the cell plates of the first two divisions in the rhizoid. This specific pattern of localization in the zygote and embryo involves the redistribution of previously synthesized actin mRNA. The initial asymmetry of actin mRNA at the thallus pole of the zygote requires polar axis fixation and microfilaments but not microtubules, cell division, or polar growth. However, redistribution of actin mRNA from the thallus pole to the first cell plate is insensitive to cytoskeletal inhibitors but is dependent on cell plate formation. The F-actin that accumulates at the rhizoid tip is not accompanied by the localization of actin mRNA. However, maintenance of an accumulation of actin protein at the cell plates of the rhizoid could be explained, at least partially, by a mechanism involving localization of actin mRNA at these sites. The pattern and requirements for actin mRNA localization in the Fucus embryo may be relevant to polarization of the embryo and asymmetric cell divisions in higher plants as well as in other tip-growing plant cells

Trade‐offs between carbon stocks and biodiversity in European temperate forests

Abstract Policies to mitigate climate change and biodiversity loss often assume that protecting carbon-rich forests provides co-benefits in terms of biodiversity, due to the spatial congruence of carbon stocks and biodiversity at biogeographic scales. However, it remains unclear whether this holds at the scales relevant for management, and particularly large knowledge gaps exist for temperate forests and for taxa other than trees. We built a comprehensive dataset of Central European temperate forest structure and multi-taxonomic diversity (beetles, birds, bryophytes, fungi, lichens, and plants) across 352 plots. We used Boosted Regression Trees (BRTs) to assess the relationship between above-ground live carbon stocks and (a) taxon-specific richness, (b) a unified multidiversity index. We used Threshold Indicator Taxa ANalysis to explore individual species? responses to changing above-ground carbon stocks and to detect change-points in species composition along the carbon-stock gradient. Our results reveal an overall weak and highly variable relationship between richness and carbon stock at the stand scale, both for individual taxonomic groups and for multidiversity. Similarly, the proportion of win-win and trade-off species (i.e., species favored or disadvantaged by increasing carbon stock, respectively) varied substantially across taxa. Win-win species gradually replaced trade-off species with increasing carbon, without clear thresholds along the above-ground carbon gradient, suggesting that community-level surrogates (e.g., richness) might fail to detect critical changes in biodiversity. Collectively, our analyses highlight that leveraging co-benefits between carbon and biodiversity in temperate forest may require stand-scale management that prioritizes either biodiversity or carbon in order to maximize co-benefits at broader scales. Importantly, this contrasts with tropical forests, where climate and biodiversity objectives can be integrated at the stand scale, thus highlighting the need for context-specificity when managing for multiple objectives. Accounting for critical change-points of target taxa can help to deal with this specificity, by defining a safe operating space to manipulate carbon while avoiding biodiversity losses

SLO-ML:A Language for Service Level Objective Modelling in Multi-cloud applications

Cloud modelling languages (CMLs) are designed to assist customers in tackling the diversity of services in the current cloud market. While many CMLs have been proposed in the literature, they lack practical support for automating the selection of services based on the specific service level objectives of a customer's application. We put forward SLO-ML, a novel and generative CML to capture service level requirements. Subsequently, SLO-ML selects the services to honour the customer's requirements and generates the deployment code appropriate to these services. We present the architectural design of SLO-ML and the associated broker that realises the deployment operations. We evaluate SLO-ML using an experimental case study with a group of researchers and developers using a real-world cloud application. We also assess SLO-ML's overheads through empirical scalability tests. We express the promises of SLO-ML in terms of gained productivity and experienced usability, and we highlight its limitations by analysing it as application requirements grow

Robustness of circadian clocks to daylight fluctuations: hints from the picoeucaryote Ostreococcus tauri

The development of systemic approaches in biology has put emphasis on identifying genetic modules whose behavior can be modeled accurately so as to gain insight into their structure and function. However most gene circuits in a cell are under control of external signals and thus quantitative agreement between experimental data and a mathematical model is difficult. Circadian biology has been one notable exception: quantitative models of the internal clock that orchestrates biological processes over the 24-hour diurnal cycle have been constructed for a few organisms, from cyanobacteria to plants and mammals. In most cases, a complex architecture with interlocked feedback loops has been evidenced. Here we present first modeling results for the circadian clock of the green unicellular alga Ostreococcus tauri. Two plant-like clock genes have been shown to play a central role in Ostreococcus clock. We find that their expression time profiles can be accurately reproduced by a minimal model of a two-gene transcriptional feedback loop. Remarkably, best adjustment of data recorded under light/dark alternation is obtained when assuming that the oscillator is not coupled to the diurnal cycle. This suggests that coupling to light is confined to specific time intervals and has no dynamical effect when the oscillator is entrained by the diurnal cycle. This intringuing property may reflect a strategy to minimize the impact of fluctuations in daylight intensity on the core circadian oscillator, a type of perturbation that has been rarely considered when assessing the robustness of circadian clocks

Glioblastoma surgery imaging—reporting and data system: Standardized reporting of tumor volume, location, and resectability based on automated segmentations

Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software

Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection

Discovery pipelines for marine resources : an ocean of opportunity for biotechnology?

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant agreement No 645884. CABI is an international intergovernmental organisation, and we gratefully acknowledge the core financial support from our member countries (and lead agencies) including the United Kingdom (Department for International Development), China (Chinese Ministry of Agriculture), Australia (Australian Centre for International Agricultural Research), Canada (Agriculture and Agri-Food Canada), Netherlands (Directorate-General for International Cooperation),and Switzerland (Swiss Agency for Development and Cooperation). See https://www.cabi.org/about-cabi/who-we-work-with/key-donors/ for full details.Marine microbial diversity offers enormous potential for discovery of compounds of crucial importance in healthcare, food security and bioindustry. However, access to it has been hampered by the difficulty of accessing and growing the organisms for study. The discovery and exploitation of marine bioproducts for research and commercial development requires state-of-the-art technologies and innovative approaches. Technologies and approaches are advancing rapidly and keeping pace is expensive and time consuming. There is a pressing need for clear guidance that will allow researchers to operate in a way that enables the optimal return on their efforts whilst being fully compliant with the current regulatory framework. One major initiative launched to achieve this, has been the advent of European Research Infrastructures. Research Infrastructures (RI) and associated centres of excellence currently build harmonized multidisciplinary workflows that support academic and private sector users. The European Marine Biological Research Infrastructure Cluster (EMBRIC) has brought together six such RIs in a European project to promote the blue bio-economy. The overarching objective is to develop coherent chains of high-quality services for access to biological, analytical and data resources providing improvements in the throughput and efficiency of workflows for discovery of novel marine products. In order to test the efficiency of this prototype pipeline for discovery, 248 rarely-grown organisms were isolated and analysed, some extracts demonstrated interesting biochemical properties and are currently undergoing further analysis. EMBRIC has established an overarching and operational structure to facilitate the integration of the multidisciplinary value chains of services to access such resources whilst enabling critical mass to focus on problem resolution.Publisher PDFPeer reviewe