Recent advances in computer vision and deep learning techniques have facilitated significant progress in video scene understanding, thus helping film and television practitioners achieve accurate video editing. However, so far, publicly available semantic segmentation datasets are mostly limited to indoor scenes, city streets, and natural images, often ignoring example objects in action movies, which is a research gap that needs to be urgently filled. In this paper, we introduce a large-scale, high-precision semantic segmentation dataset of props in Chinese martial arts movie clips, named ChineseMPD. Specifically, this dataset first establishes segmentation rules and general review criteria for audiovisual data, and then provides semantic segmentation annotations for six weapon props (Gun, Sword, Stick, Knife, Hook, and Arrow) with a summary of 32,992 objects.To the best of our knowledge, this dataset is the largest semantic segmentation dataset for movie props to date. ChineseMPD dataset not only significantly expands the application of traditional tasks of computer vision such as object detection and scene understanding, but also opens up new avenues for interdisciplinary research. <<<

Heiko Schuster, Wenguang Shao, Tobias Weiss, Patrick G A Pedrioli, Patrick Roth, Michael Weller, David S Campbell, Eric W Deutsch, Robert L Moritz, Oliver Planz, Hans-Georg Rammensee, Ruedi Aebersold, Etienne Caron <<<

The large array of peptides presented to CD8+ T cells by major histocompatibility complex (MHC) class I molecules is referred to as the MHC class I immunopeptidome. Although the MHC class I immunopeptidome is ubiquitous in mammals and represents a critical component of the immune system, very little is known, in any species, about its composition across most tissues and organs in vivo. We applied mass spectrometry (MS) technologies to draft the first tissue-based atlas of the murine MHC class I immunopeptidome in health. Peptides were extracted from 19 normal tissues from C57BL/6 mice and prepared for MS injections, resulting in a total number of 28,448 high-confidence H2D/K-associated peptides identified and annotated in the atlas. This atlas provides initial qualitative data to explore the tissue-specificity of the immunopeptidome and serves as a guide to identify potential tumor-associated antigens from various cancer models. Our data were shared via PRIDE (PXD008733), SysteMHC Atlas (SYSMHC00018) and SWATH Atlas. We anticipate that this unique dataset will be expanded in the future and will find wide applications in basic and translational immunology. <<<

We share data from N = 217 healthy adults (mean age 29 years, range 20-41; 109 females, 108 males) who underwent extensive cognitive assessment and neuroimaging to examine the neural basis of individual differences, with a particular focus on a brain structure called the hippocampus. Cognitive data were collected using a wide array of questionnaires, naturalistic tests that examined imagination, autobiographical memory recall and spatial navigation, traditional laboratory-based tests such as recalling word pairs, and comprehensive characterisation of the strategies used to perform the cognitive tests. 3 Tesla MRI data were also acquired and include multi-parameter mapping to examine tissue microstructure, diffusion-weighted MRI, T2-weighted high-resolution partial volume structural MRI scans (with the masks of hippocampal subfields manually segmented from these scans), whole brain resting state functional MRI scans and partial volume high resolution resting state functional MRI scans. This rich dataset will be of value to cognitive and clinical neuroscientists researching individual differences, real-world cognition, brain-behaviour associations, hippocampal subfields and more. All data are freely available on Dryad. <<<

Bálint Ármin Pataki, Alex Olar, Dezső Ribli, Adrián Pesti, Endre Kontsek, Benedek Gyöngyösi, Ágnes Bilecz, Tekla Kovács, Kristóf Attila Kovács, Zsófia Kramer, András Kiss, Miklós Szócska, Péter Pollner, István Csabai <<<

Histopathology is the gold standard method for staging and grading human tumors and provides critical information for the oncoteam's decision making. Highly-trained pathologists are needed for careful microscopic analysis of the slides produced from tissue taken from biopsy. This is a time-consuming process. A reliable decision support system would assist healthcare systems that often suffer from a shortage of pathologists. Recent advances in digital pathology allow for high-resolution digitalization of pathological slides. Digital slide scanners combined with modern computer vision models, such as convolutional neural networks, can help pathologists in their everyday work, resulting in shortened diagnosis times. In this study, 200 digital whole-slide images are published which were collected via hematoxylin-eosin stained colorectal biopsy. Alongside the whole-slide images, detailed region level annotations are also provided for ten relevant pathological classes. The 200 digital slides, after pre-processing, resulted in 101,389 patches. A single patch is a 512 × 512 pixel image, covering 248 × 248 μm tissue area. Versions at higher resolution are available as well. Hopefully, HunCRC, this widely accessible dataset will aid future colorectal cancer computer-aided diagnosis and research. <<<