diffBUM-HMM:A robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data

Advancing RNA structural probing techniques with next-generation sequencing has generated demands for complementary computational tools to robustly extract RNA structural information amidst sampling noise and variability. We present diffBUM-HMM, a noise-aware model that enables accurate detection of RNA flexibility and conformational changes from high-throughput RNA structure-probing data. diffBUM-HMM is widely compatible, accounting for sampling variation and sequence coverage biases, and displays higher sensitivity than existing methods while robust against false positives. Our analyses of datasets generated with a variety of RNA probing chemistries demonstrate the value of diffBUM-HMM for quantitatively detecting RNA structural changes and RNA-binding protein binding sites

Automatic detection of Ionospheric Alfvén Resonances in magnetic spectrograms using U-net

Ionospheric Alfven Resonances (IARs) are weak discrete non-stationary Alfven waves along magnetic field lines, at periods of ~0.5-20 Hz, that occur during local night-time, particularly during low geomagnetic activity. They are detectable through time-frequency analysis (spectrograms) of measurements made by sensitive search coil magnetometers. The IARs are generated by the interaction of electromagnetic energy partially trapped in the Earth-ionosphere cavity with the main geomagnetic field and their behavior provides proxy information about atmospheric ion density between 100-1000 km altitude. Limited methods exist to automatically detect and analyse their properties and behavior as they are difficult to extract using standard image and signal processing techniques. We present a new method for the detection of IARs based on the fully convolutional neural network U-net. U-net was chosen as it is able to perform accurate image segmentation and it can be trained in a supervised fashion on a relatively small labeled dataset utilizing data augmentation. We show that the resulting predictive model generated by training the U-net is able to detect IAR signals while mislabelling considerably less noise than other data analysis methods. We achieved our best results by using a training set of 178 hand-digitized examples from high-quality spectrograms measured at the Eskdalemuir Geophysical Observatory (UK). We find that the network converges in ten iterations with a final intersection over union (IoU) metric of 0.9 and a training loss of below 0.2. We use the trained network to extract IARs from over 2300 images, covering six years of search coil magnetometer data measured at the Eskdalemuir Observatory. U-net can also automatically handle missing data or days without IARs, giving a null result as expected. This constitutes the first use of a neural network for pattern recognition of unstructured image data such as spectrograms containing IAR signals, though the method is applicable to other types of resonances or geophysical features in the time-frequency domain

Open Source Framework for Enabling HPC and Cloud Geoprocessing Services

[EN] Geoprocessing is a set of tools that can be used to efficiently address several pressing chal-lenges for the global economy ranging from agricultural productivity, the design of transport networks, to the prediction of climate change and natural disasters. This paper describes an Open Source Framework developed, within three European projects, for Ena-bling High-Performance Computing (HPC) and Cloud geoprocessing services applied to agricultural challenges. The main goals of the European Union projects EUXDAT (EUro-pean e-infrastructure for eXtreme Data Analytics in sustainable developmenT), CYBELE (fostering precision agriculture and livestock farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable big data analytics), and EOPEN (opEn interOperable Platform for unified access and analysis of Earth observatioN data) are to enable the use of large HPC systems, as well as big data management, user-friendly access and visualization of results. In addition, these projects focus on the development of software frameworks, and fuse Earth-observation data, such as Copernicus data, with non-Earth-observation data, such as weather, environmental and social media information. In this paper, we describe the agroclimatic-zones pilot used to validate the framework. Finally, performance metrics collected during the execution (up to 182 times speedup with 256 MPI processes) of the pilot are presented.This work has been carried out within the context of the following projects: European e-infrastructure for extreme data ana-lytics in sustainable development (EUXDAT); Fostering precision agricul-ture and livestock farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable big data analytic (CYBELE); Open interoperable platform for unified access and analysis of Earth observation data (EOPEN). Further information about the projects is available at the respective web pages (Nieto et al., 2020; Vingione et al., 2020; Davy et al., 2020). The research leading to these results has received funding from the European Unions Horizon 2020 Research and Innovation Programme, grant agreements n. 777549, 825355, 776019, respectively.Montañana, JM.; Marangio, P.; Hervás, A. (2020). Open Source Framework for Enabling HPC and Cloud Geoprocessing Services. Agris on-line Papers in Economics and Informatics. 12(4):61-76. https://doi.org/10.7160/aol.2020.120405S617612

Open Source Framework for Enabling HPC and Cloud Geoprocessing Services

Geoprocessing is a set of tools that can be used to efficiently address several pressing chal-lenges for the global economy ranging from agricultural productivity, the design of transport networks, to the prediction of climate change and natural disasters. This paper describes an Open Source Framework developed, within three European projects, for Ena-bling High-Performance Computing (HPC) and Cloud geoprocessing services applied to agricultural challenges. The main goals of the European Union projects EUXDAT (EUro-pean e-infrastructure for eXtreme Data Analytics in sustainable developmenT), CYBELE (fostering precision agriculture and livestock farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable big data analytics), and EOPEN (opEn interOperable Platform for unified access and analysis of Earth observatioN data) are to enable the use of large HPC systems, as well as big data management, user-friendly access and visualization of results. In addition, these projects focus on the development of software frameworks, and fuse Earth-observation data, such as Copernicus data, with non-Earth-observation data, such as weather, environmental and social media information. In this paper, we describe the agroclimatic-zones pilot used to validate the framework. Finally, performance metrics collected during the execution (up to 182 times speedup with 256 MPI processes) of the pilot are presented

Automatic detection of Ionospheric Alfvén Resonances in magnetic spectrograms using U-net

Ionospheric Alfvén Resonances (IARs) are weak discrete non-stationary Alfvén waves along magnetic field lines, at periods of ∼0.5–20 Hz, that occur during local night-time, particularly during low geomagnetic activity. They are detectable through time–frequency analysis (spectrograms) of measurements made by sensitive search coil magnetometers. The IARs are generated by the interaction of electromagnetic energy partially trapped in the Earth–ionosphere cavity with the main geomagnetic field and their behaviour provides proxy information about atmospheric ion density between 100–1000 km altitude. Limited methods exist to automatically detect and analyse their properties and behaviour as they are difficult to extract using standard image and signal processing techniques. We present a new method for the detection of IARs based on the fully convolutional neural network U-net. U-net was chosen as it is able to perform accurate image segmentation and it can be trained in a supervised fashion on a relatively small labelled dataset utilizing data augmentation. We show that the resulting predictive model generated by training the U-net is able to detect IAR signals while mislabelling considerably less noise than other data analysis methods. We achieved our best results by using a training set of 178 hand-digitized examples from high-quality spectrograms measured at the Eskdalemuir Geophysical Observatory (UK). We find that the network converges in ten iterations with a final intersection over union (IoU) metric of 0.9 and a training loss of below 0.2. We use the trained network to extract IARs from over 2300 images, covering six years of search coil magnetometer data measured at the Eskdalemuir Observatory. U-net can also automatically handle missing data or days without IARs, giving a null result as expected. This constitutes the first use of a neural network for pattern recognition of unstructured image data such as spectrograms containing IAR signals, though the method is applicable to other types of resonances or geophysical features in the time–frequency domain.</p

Artificial Intelligence in Dermatopathology: New Insights and Perspectives

In recent years, an increasing enthusiasm has been observed towards artificial intelligence and machine learning, involving different areas of medicine. Among these, although still in the embryonic stage, the dermatopathological field has also been partially involved, with the attempt to develop and train algorithms that could assist the pathologist in the differential diagnosis of complex melanocytic lesions. In this article, we face this new challenge of the modern era, carry out a review of the literature regarding the state of the art and try to determine promising future perspectives

Pediatric Headache in Primary Care and Emergency Departments: Consensus with RAND/UCLA Method

Headache is the most frequent neurological symptom in childhood and the main reason for admission to pediatric emergency departments. The aim of this consensus document is to define a shared clinical pathway between primary care pediatricians (PCP) and hospitals for the management of children presenting with headache. For the purposes of the study, a group of hospital pediatricians and a group of PCP from the Emilia Romagna's health districts were selected to achieve consensus using the RAND/UCLA appropriateness method. Thirty-nine clinical scenarios were developed: for each scenario, participants were asked to rank the appropriateness of each option from 1 to 9. Agreement was reached if &gt;= 75% of participants ranked within the same range of appropriateness. The answers, results, and discussion helped to define the appropriateness of procedures with a low level of evidence regarding different steps of the diagnostic-therapeutic process: primary care evaluation, emergency department evaluation, hospital admission, acute therapy, prophylaxis, and follow-up. The RAND proved to be a valid method to value appropriateness of procedures and define a diagnostic-therapeutic pathway suitable to the local reality in the management of pediatric headache. From our results, some useful recommendations were developed for optimizing the healthcare professionals' network among primary care services and hospitals

The hologenome of <i>Daphnia magna</i> reveals possible DNA methylation and microbiome-mediated evolution of the host genome

Properties that make organisms ideal laboratory models in developmental and medical research are often the ones that also make them less representative of wild relatives. The waterflea Daphnia magna is an exception, by both sharing many properties with established laboratory models and being a keystone species, a sentinel species for assessing water quality, an indicator of environmental change and an established ecotoxicology model. Yet, Daphnia's full potential has not been fully exploited because of the challenges associated with assembling and annotating its gene-rich genome. Here, we present the first hologenome of Daphnia magna, consisting of a chromosomal-level assembly of the D. magna genome and the draft assembly of its metagenome. By sequencing and mapping transcriptomes from exposures to environmental conditions and from developmental morphological landmarks, we expand the previously annotates gene set for this species. We also provide evidence for the potential role of gene-body DNA-methylation as a mutagen mediating genome evolution. For the first time, our study shows that the gut microbes provide resistance to commonly used antibiotics and virulence factors, potentially mediating Daphnia's environmental-driven rapid evolution. Key findings in this study improve our understanding of the contribution of DNA methylation and gut microbiota to genome evolution in response to rapidly changing environments.NERC highlights grant [NE/N016777/1]; European Union's Horizon 2020 research and innovation programme [965406]; the work presented in this publication was performed as part of ASPIS; the results and conclusions reflect only the author's view and that the European Commission cannot be held responsible for any use that may be made of the information contained therein; This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101028700; China−UK Research of Safeguarding Natural Water project, funded by the Royal Society International Collaboration Award [IC160121]. Funding for open access charge: Natural Environment Research Council