Identification of microbial signatures linked to oilseed rape yield decline at the landscape scale

Background: The plant microbiome plays a vital role in determining host health and productivity. However, we lack real-world comparative understanding of the factors which shape assembly of its diverse biota, and crucially relationships between microbiota composition and plant health. Here we investigated landscape scale rhizosphere microbial assembly processes in oilseed rape (OSR), the UK’s third most cultivated crop by area and the world's third largest source of vegetable oil, which suffers from yield decline associated with the frequency it is grown in rotations. By including 37 conventional farmers’ fields with varying OSR rotation frequencies, we present an innovative approach to identify microbial signatures characteristic of microbiomes which are beneficial and harmful to the host. Results: We show that OSR yield decline is linked to rotation frequency in real-world agricultural systems. We demonstrate fundamental differences in the environmental and agronomic drivers of protist, bacterial and fungal communities between root, rhizosphere soil and bulk soil compartments. We further discovered that the assembly of fungi, but neither bacteria nor protists, was influenced by OSR rotation frequency. However, there were individual abundant bacterial OTUs that correlated with either yield or rotation frequency. A variety of fungal and protist pathogens were detected in roots and rhizosphere soil of OSR, and several increased relative abundance in root or rhizosphere compartments as OSR rotation frequency increased. Importantly, the relative abundance of the fungal pathogen Olpidium brassicae both increased with short rotations and was significantly associated with low yield. In contrast, the root endophyte Tetracladium spp. showed the reverse associations with both rotation frequency and yield to O. brassicae, suggesting that they are signatures of a microbiome which benefits the host. We also identified a variety of novel protist and fungal clades which are highly connected within the microbiome and could play a role in determining microbiome composition. Conclusions: We show that at the landscape scale, OSR crop yield is governed by interplay between complex communities of both pathogens and beneficial biota which is modulated by rotation frequency. Our comprehensive study has identified signatures of dysbiosis within the OSR microbiome, grown in real-world agricultural systems, which could be used in strategies to promote crop yield. [MediaObject not available: see fulltext.

Influence of humidity on the breakdown-mechanism of small rod-plane gaps stressed by positive impulse voltages

Μη διαθέσιμη περίληψηNot available summarizationPresented on

Exploring how microbiome signatures change across inflammatory bowel disease conditions and disease locations

Understanding the variables that influence microbiome studies is critical for successful translational research. Inflammatory bowel disease (IBD) is a complex group of diseases that can present at multiple locations within the Gastrointestinal tract. Here, using the FAMISHED study cohort, we aimed to investigate the relationship between IBD condition, IBD disease location, and the microbiome. Signatures of the microbiome, including measures of diversity, taxonomy, and functionality, all significantly differed across the three different IBD conditions, Crohn’s disease (CD), ulcerative colitis (UC), and microscopic colitis (MC). Notably, when stratifying by disease location, patients with CD in the terminal ileum were more similar to healthy controls than patients with CD in the small bowel or colon, however no differences were observed at different disease locations across patients with UC. Change in taxonomic composition resulted in changes in function, with CD at each disease location, UC and MC all having unique functional dysbioses. CD patients in particular had deficiencies in Short-Chain Fatty Acid (SCFA) pathways. Our results demonstrate the complex relationship between IBD and the microbiome and highlight the need for consistent strategies for the stratification of clinical cohorts and downstream analysis to ensure results across microbiome studies and clinical trials are comparable

Robotic System Reliability Analysis and RUL Estimation Using an Iterative Approach

This paper presents a novel methodology to evaluate robotic system reliability and Remaining Useful Life (RUL) integrating FMECA (Failure Modes, Effects and Criticality Analysis), life data analysis and data-driven & model-based methods. Starting from the FMECA analysis, the methodology proposes to identify the main critical components of new parts or systems, using life data analysis. A database collects and shares data directly from the field on similar systems and applications. Data are stored and managed via a web-based interface, the user may obtain them in real time as needed, when a modification in the robot or production cells occurs. Information are captured through a set of appropriate sensors, selected and located studying historical life data. From this dataset, RUL of components may be estimated using data-driven methods and model-based approaches. Then, the RUL results are shared with ERP systems to optimize production resources and maintenance activities and with FMECA again, to improve new projects in a closed loop. A preliminary application of the methodology is proposed on an anthropomorphic robot integrated in a production cell. This research is a part of PROGRAMs: PROGnostics based Reliability Analysis for Maintenance Scheduling, H2020-FOF-09-2017-767287

Multiyear power distribution planning considering voltage regulator placement

Summarization: This paper proposes a multistage power distribution planning (PDP) method. The optimization method considers various planning alternatives that include the reinforcement of the existing feeders/substations, the construction of new distribution lines, and the placement of voltage regulators. The objective function aims at minimizing the net present value (NPV) of the network investment costs ensuring the safe operation of the distribution network throughout the planning period. The solution methodology consists of two phases. First, a mixed-integer quadratically constrained programming (MIQCP) model is formulated that determines the network investments, i.e., the reinforcement of substations and distribution lines, the network expansion plan and the voltage regulator (VR) placement at the final year of the planning period. Afterwards, a back-propagation method is adopted in order to define the year of commissioning the network investments that have been computed in the first phase. The efficiency of the proposed methodology is tested on a 22-bus distribution network under three different load growth scenarios.Presented on

Application of ANN and ANFIS for detection of brain tumors in MRIs by using DWT and GLCM texture analysis

Summarization: In this work we combine different methodologies in order to develop algorithms for Computer-Aided Diagnosis (CAD) for brain tumors from the axial plane (T2 MRI). All methods utilize texture analysis by extracting features from raw data, without post-processing, based on different techniques, such as Gray Level Co-Occurrence Matrix (GLCM), or Discrete Wavelet Transform (DWT) and different classification methods, based on ANN or ANFIS. All of our proposed methodologies are developed, validated and verified on various sub data including 65% non-healthy MRIS. The total used database consists of 202 MRIs from non-healthy patients and 18 from healthy, segmented visually by an experienced neurosurgeon. Combining different subsets of features, our best results are by using 4 GLCM features for a 4 input and two hidden layers ANN, giving sensitivity 100%, specificity 77.8% accuracy 94.3%. It is proved that the input data to train such a CAD are considered to be unbiased if the ratio between healthy/un-healthy tissue MRIs is about 35%/65%, respectively.Παρουσιάστηκε στο: International Conference on Imaging Systems and Technique