Unexpected delayed death after manual strangulation: need for careful examination in the emergency room

The aim of this case report was to demonstrate the importance of detailed clinical assessment in victims of attempted manual strangulation, to prevent the occurrence of delayed death due to an airway collapse; and to provide an update on clinical management of these patients. An elderly male patient presented with a sore throat and speech impairment after attempting manual strangulation. Physical examination showed reddish skin of the neck, an extensive haematoma of the hard palate and anterior tongue. Flexible laryngoscopy failed to show any swelling of the hypopharynx or larynx. A few hours after presentation, the patient developed acute dyspnoea and died. Autopsy and post-mortem CT scan showed a haematoma in the thyro-epiglottal space. In conclusion, victims of manual strangulation can survive despite internal neck injury which can lead to delayed fatal airway collapse. This is because often there are few or no signs of assault, therefore medical evaluation should be thorough and timely. Sometimes immediate tracheotomy can be life saving

LYMPHANGIOMA OF THE TONGUE: CASE REPORT

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JUVENILE DIFFUSE LARYNGEAL PAPILLOMATOSIS: CASE REPORT

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The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update

YesGalaxy (https://galaxyproject.org) is deployed globally, predominantly through free-to-use services, supporting user-driven research that broadens in scope each year. Users are attracted to public Galaxy services by platform stability, tool and reference dataset diversity, training, support and integration, which enables complex, reproducible, shareable data analysis. Applying the principles of user experience design (UXD), has driven improvements in accessibility, tool discoverability through Galaxy Labs/subdomains, and a redesigned Galaxy ToolShed. Galaxy tool capabilities are progressing in two strategic directions: integrating general purpose graphical processing units (GPGPU) access for cutting-edge methods, and licensed tool support. Engagement with global research consortia is being increased by developing more workflows in Galaxy and by resourcing the public Galaxy services to run them. The Galaxy Training Network (GTN) portfolio has grown in both size, and accessibility, through learning paths and direct integration with Galaxy tools that feature in training courses. Code development continues in line with the Galaxy Project roadmap, with improvements to job scheduling and the user interface. Environmental impact assessment is also helping engage users and developers, reminding them of their role in sustainability, by displaying estimated CO2 emissions generated by each Galaxy job.NIH [U41 HG006620, U24 HG010263, U24 CA231877, U01 CA253481]; US National Science Foundation [1661497, 1758800, 2216612]; computational resources are provided by the Advanced Cyberinfrastructure Coordination Ecosystem (ACCESS-CI), Texas Advanced Computing Center, and the JetStream2 scientific cloud. Funding for open access charge: NIH. ELIXIR IS and Travel grants; EU Horizon Europe [HORIZON-INFRA-2021-EOSC-01-04, 101057388]; EU Horizon Europe under the Biodiversity, Circular Economy and Environment program (REA.B.3, BGE 101059492); German Federal Ministry of Education and Research, BMBF [031 A538A de.NBI-RBC]; Ministry of Science, Research and the Arts Baden-Württemberg (MWK) within the framework of LIBIS/de.NBI Freiburg. Galaxy Australia is supported by the Australian BioCommons which is funded through Australian Government NCRIS investments from Bioplatforms Australia and the Australian Research Data Commons, as well as investment from the Queensland Government RICF program.Please note, contributors are listed in alphabetical order

The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update

YesGalaxy (https://galaxyproject.org) is deployed globally, predominantly through free-to-use services, supporting user-driven research that broadens in scope each year. Users are attracted to public Galaxy services by platform stability, tool and reference dataset diversity, training, support and integration, which enables complex, reproducible, shareable data analysis. Applying the principles of user experience design (UXD), has driven improvements in accessibility, tool discoverability through Galaxy Labs/subdomains, and a redesigned Galaxy ToolShed. Galaxy tool capabilities are progressing in two strategic directions: integrating general purpose graphical processing units (GPGPU) access for cutting-edge methods, and licensed tool support. Engagement with global research consortia is being increased by developing more workflows in Galaxy and by resourcing the public Galaxy services to run them. The Galaxy Training Network (GTN) portfolio has grown in both size, and accessibility, through learning paths and direct integration with Galaxy tools that feature in training courses. Code development continues in line with the Galaxy Project roadmap, with improvements to job scheduling and the user interface. Environmental impact assessment is also helping engage users and developers, reminding them of their role in sustainability, by displaying estimated CO2 emissions generated by each Galaxy job.NIH [U41 HG006620, U24 HG010263, U24 CA231877, U01 CA253481]; US National Science Foundation [1661497, 1758800, 2216612]; computational resources are provided by the Advanced Cyberinfrastructure Coordination Ecosystem (ACCESS-CI), Texas Advanced Computing Center, and the JetStream2 scientific cloud. Funding for open access charge: NIH. ELIXIR IS and Travel grants; EU Horizon Europe [HORIZON-INFRA-2021-EOSC-01-04, 101057388]; EU Horizon Europe under the Biodiversity, Circular Economy and Environment program (REA.B.3, BGE 101059492); German Federal Ministry of Education and Research, BMBF [031 A538A de.NBI-RBC]; Ministry of Science, Research and the Arts Baden-Württemberg (MWK) within the framework of LIBIS/de.NBI Freiburg. Galaxy Australia is supported by the Australian BioCommons which is funded through Australian Government NCRIS investments from Bioplatforms Australia and the Australian Research Data Commons, as well as investment from the Queensland Government RICF program

Editorial for special issue “fine art pattern extraction and recognition”

: Cultural heritage, especially the fine arts, plays an invaluable role in the cultural, historical, and economic growth of our societies [...]

Density-based clustering with fully-convolutional networks for crowd flow detection from drones

Crowd analysis from drones has attracted increasing attention in recent times due to the ease of use and affordable cost of these devices. However, how this technology can provide a solution to crowd flow detection is still an unexplored research question. To this end, we propose a crowd flow detection method for video sequences shot by a drone. The method is based on a fully-convolutional network that learns to perform crowd clustering in order to detect the centroids of crowd-dense areas and track their movement in consecutive frames. The proposed method proved effective and efficient when tested on the Crowd Counting datasets of the VisDrone challenge, characterized by video sequences rather than still images. The encouraging results show that the proposed method could open up new ways of analyzing high-level crowd behavior from drones

Recognizing the Emotions Evoked by Artworks Through Visual Features and Knowledge Graph-Embeddings

Recognizing the emotion an image evokes in the observer has long attracted the interest of the community for its many potential applications. However, it is a challenging task mainly due to the inherent complexity and subjectivity of human feelings. Such a difficulty is exacerbated in the domain of visual arts, mainly because of their abstract nature. In this work, we propose a new version of the artistic knowledge graph we were working on, namely ArtGraph, obtained by integrating the emotion labels provided by the ArtEmis dataset. The proposed graph enables emotion-based information retrieval and knowledge discovery even without training a learning model. In addition, we propose an artwork emotion classification system that jointly exploits visual features and knowledge graph-embeddings. Experimental evaluation revealed that while improvements in emotion classification depend mainly on the use of visual features, the prediction of style, genre and emotion can benefit from the simultaneous exploitation of visual and contextual features and can assist each other in a synergistic way

Unexpected delayed death after manual strangulation: need for careful examination in the emergency room.

The aim of this case report was to demonstrate the importance of detailed clinical assessment in victims of attempted manual strangulation, to prevent the occurrence of delayed death due to an airway collapse; and to provide an update on clinical management of these patients. An elderly male patient presented with a sore throat and speech impairment after attempting manual strangulation. Physical examination showed reddish skin of the neck, an extensive haematoma of the hard palate and anterior tongue. Flexible laryngoscopy failed to show any swelling of the hypopharynx or larynx. A few hours after presentation, the patient developed acute dyspnoea and died. Autopsy and post-mortem CT scan showed a haematoma in the thyro-epiglottal space. In conclusion, victims of manual strangulation can survive despite internal neck injury which can lead to delayed fatal airway collapse. This is because often there are few or no signs of assault, therefore medical evaluation should be thorough and timely. Sometimes immediate tracheotomy can be life saving