Microtubule asters must be positioned precisely within cells. How forces generated by molecular motors such as dynein are integrated in space and time to enable such positioning remains unclear. In particular, whereas aster movements depend on the drag caused by cytoplasm viscosity, in vivo drag measurements are lacking, precluding a thorough understanding of the mechanisms governing aster positioning. Here, we investigate this fundamental question during the migration of asters and pronuclei in C. elegans zygotes, a process essential for the mixing of parental genomes. Detailed quantification of these movements using the female pronucleus as an in vivo probe establish that the drag coefficient of the male-asters complex is approximately five times that of the female pronucleus. Further analysis of embryos lacking cortical dynein, the connection between asters and male pronucleus, or the male pronucleus altogether, uncovers the balance of dynein-driven forces that accurately position microtubule asters in C. elegans zygotes.
Genome stability relies notably on the integrity of centrosomes and on the mitotic spindle they organize. Structural and numerical centrosome aberrations are frequently observed in human cancer, and there is increasing evidence that centrosome amplification can promote tumorigenesis. Here, we use C. elegans seam cells as a model system to analyze centrosome homeostasis in the context of a stereotyped stem like lineage. We found that overexpression of the Plk4-related kinase ZYG-1 leads to the formation of one supernumerary centriolar focus per parental centriole during the cell cycle that leads to the sole symmetric division in the seam lineage. In the following cell cycle, such supernumerary foci function as microtubule organizing centers, but do not cluster during mitosis, resulting in the formation of a multipolar spindle and then aneuploid daughter cells. Intriguingly, we found also that supernumerary centriolar foci do not assemble in the asymmetric cell divisions that precedes or that follows the symmetric seam cell division, despite the similar presence of GFP::ZYG-1. Furthermore, we established that supernumerary centrioles form earlier during development in animals depleted of the heterochronic gene lin-14, in which the symmetric division is precocious. Conversely, supernumerary centrioles are essentially not observed in animals depleted of lin-28, in which the symmetric division is lacking. These findings lead us to conclude that ZYG-1 promotes limited centriole amplification solely during the symmetric division in the C. elegans seam lineage.
Progress in digital pathology is hindered by highresolution images and the prohibitive cost of exhaustive localized annotations. The commonly used paradigm to categorize pathology images is patch-based processing, which often incorporates multiple instance learning (MIL) to aggregate local patch-level representations yielding imagelevel prediction. Nonetheless, diagnostically relevant regions may only take a small fraction of the whole tissue, and MIL-based aggregation operation assumes that all patch representations are independent and thus mislays the contextual information from adjacent cell and tissue microenvironments. Consequently, the computational resources dedicated to a specific region are independent of its information contribution. This paper proposes a transformer-based architecture specifically tailored for histopathological image classification, which combines fine-grained local attention with a coarse global attention mechanism to learn meaningful representations of high-resolution images at an efficient computational cost. More importantly, based on the observation above, we propose a novel mixing-based dataaugmentation strategy, namely ScoreMix, by leveraging the distribution of the semantic regions of images during the training and carefully guiding the data mixing via sampling the locations of discriminative image content. Thorough experiments and ablation studies on three challenging representative cohorts of Haematoxylin & Eosin (H&E) tumour regions-of-interest (TRoIs) datasets have validated the superiority of our approach over existing state-of-theart methods and effectiveness of our proposed components, e.g., data augmentation in improving classification performance. We also demonstrate our method's interpretability, robustness, and cross-domain generalization capability.
Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow. Supervised deep networks take for granted a large number of annotations by radiologists, which is often prohibitively very time-consuming to acquire. Moreover, supervised systems are tailored to closed set scenarios, e.g., trained models suffer from overfitting to previously seen rare anomalies at training. Instead, our approach's rationale is to use task agnostic pretext tasks to leverage unlabeled data based on a cross-sample similarity measure. Besides, we formulate a complex distribution of data from normal class within our framework to avoid a potential bias on the side of anomalies. Through extensive experiments, we show that our method outperforms baselines across unsupervised and self-supervised anomaly detection settings on a real-world medical dataset, the MURA dataset. We also provide rich ablation studies to analyze each training stage's effect and loss terms on the final performance.
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