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

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

LARGE SUPERFICIAL BASAL CELL CARCINOMA OF THE UPPERLIMB SUCCESSFULLY TREATED WITH IMIQUIMOD 5% CREAM

Superficial basal cell carcinoma (sBCC) usually appears as multiple, slow-growing, shiny pink or red, slightly scaly lesions that most often develop on the trunk and shoulders. It may attain giant proportions due primarily to recurrence and neglect, causing extensive local invasion and disfigurement. Large lesions of sBCC, may be difficult to treat due to the potentially large resulting defect after removal. Imiquimod is an immune response modifier approved by the FDA for the treatment of superficial BCC lesions and its use is gradually expanded to various off-label precancerous and cancerous skin lesions. We report a case of large sBCC present for over the previous 4 years successfully treated with imiquimod 5% cream once daily 5x/week for a 16-week period

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

PLENARY: Explaining black-box models in natural language through fuzzy linguistic summaries

We introduce an approach called PLENARY (exPlaining bLack-box modEls in Natural lAnguage thRough fuzzY linguistic summaries), which is an explainable classifier based on a data-driven predictive model. Neural learning is exploited to derive a predictive model based on two levels of labels associated with the data. Then, model explanations are derived through the popular SHapley Additive exPlanations (SHAP) tool and conveyed in a linguistic form via fuzzy linguistic summaries. The linguistic summarization allows translating the explanations of the model outputs provided by SHAP into statements expressed in natural language. PLENARY accounts for the imprecision related to model outputs by summarizing them into simple linguistic statements and for the imprecision related to the data labeling process by including additional domain knowledge in the form of middle-layer labels. PLENARY is validated on preprocessed speech signals collected from smartphones from patients with bipolar disorder and on publicly available mental health survey data. The experiments confirm that fuzzy linguistic summarization is an effective technique to support meta-analyses of the outputs of AI models. Also, PLENARY improves explainability by aggregating low-level attributes into high-level information granules, and by incorporating vague domain knowledge into a multi-task sequential and compositional multilayer perceptron. SHAP explanations translated into fuzzy linguistic summaries significantly improve understanding of the predictive modelling process and its outputs

VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results

Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint. However, there are few algorithms focusing on crowd counting on the drone-captured data due to the lack of comprehensive datasets. To this end, we collect a large-scale dataset and organize the Vision Meets Drone Crowd Counting Challenge (VisDrone-CC2020) in conjunction with the 16th European Conference on Computer Vision (ECCV 2020) to promote the developments in the related fields. The collected dataset is formed by 3,360 images, including 2,460 images for training, and 900 images for testing. Specifically, we manually annotate persons with points in each video frame. There are 14 algorithms from 15 institutes submitted to the VisDrone-CC2020 Challenge. We provide a detailed analysis of the evaluation results and conclude the challenge. More information can be found at the website: http://www.aiskyeye.com/. 2020, Springer Nature Switzerland AG.National Natural Science Foundation of China;Natural Science Foundation of Tianjin CityScopu