Transcriptome innovations in primates revealed by single-molecule long-read sequencing

Transcriptomic diversity greatly contributes to the fundamentals of disease, lineage-specific biology, and environmental adaptation. However, much of the actual isoform repertoire contributing to shaping primate evolution remains unknown. Here, we combined deep long- and short-read sequencing complemented with mass spectrometry proteomics in a panel of lymphoblastoid cell lines (LCLs) from human, three other great apes, and rhesus macaque, producing the largest full-length isoform catalog in primates to date. Around half of the captured isoforms are not annotated in their reference genomes, significantly expanding the gene models in primates. Furthermore, our comparative analyses unveil hundreds of transcriptomic innovations and isoform usage changes related to immune function and immunological disorders. The confluence of these evolutionary innovations with signals of positive selection and their limited impact in the proteome points to changes in alternative splicing in genes involved in immune response as an important target of recent regulatory divergence in primates. changes in alternative splicing in genes involved in immune response as an important target of recent regulatory divergence in primates.This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB31020000); National Key R&D Program of China (China's Ministry of Science and Technology [MoST]) grant 2018YFC1406901; the International Partnership Program of the Chinese Academy of Sciences (no. 152453KYSB20170002); the Carlsberg Foundation (CF16-0663); the Villum Foundation (no. 25900) to G.Z.; and the La Caixa Foundation (ID 100010434) Fellowship Code LCF/BQ/DE16/11570011 (L.F.-P.). The Center for Genomic Regulation (CRG) / Universitat Pompeu Fabra (UPF) Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (National Map of Unique Scientific and Technical Infrastructures [ICTS] OmicsTech) and a member of the ProteoRed PRB3 Consortium, which is supported by grant PT17/0019 of the PE I + D + i 2013–2016 from the Instituto de Salud Carlos III (ISCIII), European Regional Development Fund (ERDF), and “Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya” (2017SGR595). T.M.-B. is supported by funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 864203), BFU2017-86471-P (MINECO/FEDER, UE); “Unidad de Excelencia María de Maeztu,” funded by the Agencia Estatal de Investigación (AEI) (CEX2018-000792-M); Howard Hughes International Early Career; National Institutes of Health 1R01HG010898-01A1; and Secretaria d'Universitats i Recerca and Centres de Recerca de Catalunya (CERCA) Programme del Departament d'Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 880)

Dynamics of charges and solitons

We first show that trajectories traced by charges moving in rotational magnetic fields are, precisely, the geodesics of surfaces of revolution with a coincident axis. Thus, people living on a surface of revolution are not able to sense the magnetic Hall effect induced by the surrounding magnetic field and perceive charges as influenced, exclusively, by the gravity action on the surface of revolution. Secondly, the extended Hasimoto transformations are introduced and then used to identify trajectories of charges moving through Killing rotational magnetic fields in terms of non-circular elastic curves. As a consequence, we see that in this case, charges evolve along trajectories which are obtained as extended Hasimoto transforms of solitons of the filament equation

Dynamics of charges and solitons

We first show that trajectories traced by charges moving in rotational magnetic fields are are, basically, the non-parallel geodesics of surfaces of revolution with coincident axis. Thus, people living in a surface of revolution are not able to sense the magnetic Hall e↵ect induced by the surrounding magnetic field and perceive charges as influenced, exclusively, by the gravity action on the surface of revolution. Secondly, the extended Hasimoto transformations are introduced and then used to identify trajectories of charges moving through a Killing rotational magnetic fields in terms of non-circular elastic curves. As a consequence, we see that in this case charges evolve along trajectories which are obtained as extended Hasimoto transforms of solitons of the filament equation

NERUA: sistema de detección y clasificación de entidades utilizando aprendizaje automático

In this paper we present a Named Entity Recognition system developed for Spanish by combining different machine learning techniques. A language independent approach for NE detection and evaluation of the influence of the training corpus size have been made. NERUA obtained 92.96% f-score for detection and 78.59% for classificationEste artículo presenta un sistema de reconocimiento de entidades para Español combinando diferentes algoritmos de aprendizaje. Se propone una detección de entidades independiente del lenguaje y se estudia la influencia del tamaño del corpus de entrenamiento en los resultados. NERUA obtuvo 92.96% f-score en la detección y 78.59% en la clasificación de entidade

EEG-Based Detection of Braking Intention Under Different Car Driving Conditions

The anticipatory recognition of braking is essential to prevent traffic accidents. For instance, driving assistance systems can be useful to properly respond to emergency braking situations. Moreover, the response time to emergency braking situations can be affected and even increased by different driver's cognitive states caused by stress, fatigue, and extra workload. This work investigates the detection of emergency braking from driver's electroencephalographic (EEG) signals that precede the brake pedal actuation. Bioelectrical signals were recorded while participants were driving in a car simulator while avoiding potential collisions by performing emergency braking. In addition, participants were subjected to stress, workload, and fatigue. EEG signals were classified using support vector machines (SVM) and convolutional neural networks (CNN) in order to discriminate between braking intention and normal driving. Results showed significant recognition of emergency braking intention which was on average 71.1% for SVM and 71.8% CNN. In addition, the classification accuracy for the best participant was 80.1 and 88.1% for SVM and CNN, respectively. These results show the feasibility of incorporating recognizable driver's bioelectrical responses into advanced driver-assistance systems to carry out early detection of emergency braking situations which could be useful to reduce car accidents

Evaluating current automatic de-identification methods with Veteran’s health administration clinical documents

Abstract Background The increased use and adoption of Electronic Health Records (EHR) causes a tremendous growth in digital information useful for clinicians, researchers and many other operational purposes. However, this information is rich in Protected Health Information (PHI), which severely restricts its access and possible uses. A number of investigators have developed methods for automatically de-identifying EHR documents by removing PHI, as specified in the Health Insurance Portability and Accountability Act “Safe Harbor” method. This study focuses on the evaluation of existing automated text de-identification methods and tools, as applied to Veterans Health Administration (VHA) clinical documents, to assess which methods perform better with each category of PHI found in our clinical notes; and when new methods are needed to improve performance. Methods We installed and evaluated five text de-identification systems “out-of-the-box” using a corpus of VHA clinical documents. The systems based on machine learning methods were trained with the 2006 i2b2 de-identification corpora and evaluated with our VHA corpus, and also evaluated with a ten-fold cross-validation experiment using our VHA corpus. We counted exact, partial, and fully contained matches with reference annotations, considering each PHI type separately, or only one unique ‘PHI’ category. Performance of the systems was assessed using recall (equivalent to sensitivity) and precision (equivalent to positive predictive value) metrics, as well as the F2-measure. Results Overall, systems based on rules and pattern matching achieved better recall, and precision was always better with systems based on machine learning approaches. The highest “out-of-the-box” F2-measure was 67% for partial matches; the best precision and recall were 95% and 78%, respectively. Finally, the ten-fold cross validation experiment allowed for an increase of the F2-measure to 79% with partial matches. Conclusions The “out-of-the-box” evaluation of text de-identification systems provided us with compelling insight about the best methods for de-identification of VHA clinical documents. The errors analysis demonstrated an important need for customization to PHI formats specific to VHA documents. This study informed the planning and development of a “best-of-breed” automatic de-identification application for VHA clinical text.</p

Image_1_EEG-Based Detection of Braking Intention Under Different Car Driving Conditions.PDF

<p>The anticipatory recognition of braking is essential to prevent traffic accidents. For instance, driving assistance systems can be useful to properly respond to emergency braking situations. Moreover, the response time to emergency braking situations can be affected and even increased by different driver's cognitive states caused by stress, fatigue, and extra workload. This work investigates the detection of emergency braking from driver's electroencephalographic (EEG) signals that precede the brake pedal actuation. Bioelectrical signals were recorded while participants were driving in a car simulator while avoiding potential collisions by performing emergency braking. In addition, participants were subjected to stress, workload, and fatigue. EEG signals were classified using support vector machines (SVM) and convolutional neural networks (CNN) in order to discriminate between braking intention and normal driving. Results showed significant recognition of emergency braking intention which was on average 71.1% for SVM and 71.8% CNN. In addition, the classification accuracy for the best participant was 80.1 and 88.1% for SVM and CNN, respectively. These results show the feasibility of incorporating recognizable driver's bioelectrical responses into advanced driver-assistance systems to carry out early detection of emergency braking situations which could be useful to reduce car accidents.</p

Transcriptome innovations in primates revealed by single-molecule long-read sequencing

Transcriptomic diversity greatly contributes to the fundamentals of disease, lineage-specific biology, and environmental adaptation. However, much of the actual isoform repertoire contributing to shaping primate evolution remains unknown. Here, we combined deep long- and short-read sequencing complemented with mass spectrometry proteomics in a panel of lymphoblastoid cell lines (LCLs) from human, three other great apes, and rhesus macaque, producing the largest full-length isoform catalog in primates to date. Around half of the captured isoforms are not annotated in their reference genomes, significantly expanding the gene models in primates. Furthermore, our comparative analyses unveil hundreds of transcriptomic innovations and isoform usage changes related to immune function and immunological disorders. The confluence of these evolutionary innovations with signals of positive selection and their limited impact in the proteome points to changes in alternative splicing in genes involved in immune response as an important target of recent regulatory divergence in primates.This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB31020000); National Key R&D Program of China (China's Ministry of Science and Technology [MoST]) grant 2018YFC1406901; the International Partnership Program of the Chinese Academy of Sciences (no. 152453KYSB20170002); the Carlsberg Foundation (CF16-0663); the Villum Foundation (no. 25900) to G.Z.; and the La Caixa Foundation (ID 100010434) Fellowship Code LCF/BQ/DE16/11570011 (L.F.-P.). The Center for Genomic Regulation (CRG) / Universitat Pompeu Fabra (UPF) Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (National Map of Unique Scientific and Technical Infrastructures [ICTS] OmicsTech) and a member of the ProteoRed PRB3 Consortium, which is supported by grant PT17/0019 of the PE I + D + i 2013–2016 from the Instituto de Salud Carlos III (ISCIII), European Regional Development Fund (ERDF), and “Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya” (2017SGR595). T.M.-B. is supported by funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 864203), BFU2017-86471-P (MINECO/FEDER, UE); “Unidad de Excelencia María de Maeztu,” funded by the Agencia Estatal de Investigación (AEI) (CEX2018-000792-M); Howard Hughes International Early Career; National Institutes of Health 1R01HG010898-01A1; and Secretaria d'Universitats i Recerca and Centres de Recerca de Catalunya (CERCA) Programme del Departament d'Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 880)