Deep Clustering Survival Machines with Interpretable Expert Distributions

Conventional survival analysis methods are typically ineffective to characterize heterogeneity in the population while such information can be used to assist predictive modeling. In this study, we propose a hybrid survival analysis method, referred to as deep clustering survival machines, that combines the discriminative and generative mechanisms. Similar to the mixture models, we assume that the timing information of survival data is generatively described by a mixture of certain numbers of parametric distributions, i.e., expert distributions. We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions. This method also facilitates interpretable subgrouping/clustering of all instances according to their associated expert distributions. Extensive experiments on both real and synthetic datasets have demonstrated that the method is capable of obtaining promising clustering results and competitive time-to-event predicting performance

Automated diagnosing primary open-angle glaucoma from fundus image by simulating human\u27s grading with deep learning

Primary open-angle glaucoma (POAG) is a leading cause of irreversible blindness worldwide. Although deep learning methods have been proposed to diagnose POAG, it remains challenging to develop a robust and explainable algorithm to automatically facilitate the downstream diagnostic tasks. In this study, we present an automated classification algorithm, GlaucomaNet, to identify POAG using variable fundus photographs from different populations and settings. GlaucomaNet consists of two convolutional neural networks to simulate the human grading process: learning the discriminative features and fusing the features for grading. We evaluated GlaucomaNet on two datasets: Ocular Hypertension Treatment Study (OHTS) participants and the Large-scale Attention-based Glaucoma (LAG) dataset. GlaucomaNet achieved the highest AUC of 0.904 and 0.997 for POAG diagnosis on OHTS and LAG datasets. An ensemble of network architectures further improved diagnostic accuracy. By simulating the human grading process, GlaucomaNet demonstrated high accuracy with increased transparency in POAG diagnosis (comprehensiveness scores of 97% and 36%). These methods also address two well-known challenges in the field: the need for increased image data diversity and relying heavily on perimetry for POAG diagnosis. These results highlight the potential of deep learning to assist and enhance clinical POAG diagnosis. GlaucomaNet is publicly available on https://github.com/bionlplab/GlaucomaNet

Phylotranscriptomic insights into a Mesoproterozoic-Neoproterozoic origin and early radiation of green seaweeds (Ulvophyceae)

The Ulvophyceae, a major group of green algae, is of particular evolutionary interest because of its remarkable morphological and ecological diversity. Its phylogenetic relationships and diversification timeline, however, are still not fully resolved. In this study, using an extensive nuclear gene dataset, we apply coalescent- and concatenation-based approaches to reconstruct the phylogeny of the Ulvophyceae and to explore the sources of conflict in previous phylogenomic studies. The Ulvophyceae is recovered as a paraphyletic group, with the Bryopsidales being a sister group to the Chlorophyceae, and the remaining taxa forming a clade (Ulvophyceae sensu stricto). Molecular clock analyses with different calibration strategies emphasize the large impact of fossil calibrations, and indicate a Meso-Neoproterozoic origin of the Ulvophyceae (sensu stricto), earlier than previous estimates. The results imply that ulvophyceans may have had a profound influence on oceanic redox structures and global biogeochemical cycles at the Mesoproterozoic-Neoproterozoic transition

Magnetic Skyrmions with Unconventional Helicity Polarization in a Van Der Waals Ferromagnet.

Skyrmion helicity, which defines the spin swirling direction, is a fundamental parameter that may be utilized to encode data bits in future memory devices. Generally, in centrosymmetric ferromagnets, dipole skyrmions with helicity of -π/2 and π/2 are degenerate in energy, leading to equal populations of both helicities. On the other hand, in chiral materials where the Dzyaloshinskii-Moriya interaction (DMI) prevails and the dipolar interaction is negligible, only a preferred helicity is selected by the type of DMI. However, whether there is a rigid boundary between these two regimes remains an open question. Herein, the observation of dipole skyrmions with unconventional helicity polarization in a van der Waals ferromagnet, Fe5- δ GeTe2 , is reported. Combining magnetometry, Lorentz transmission electron microscopy, electrical transport measurements, and micromagnetic simulations, the short-range superstructures in Fe5- δ GeTe2 resulting in a localized DMI contribution, which breaks the degeneracy of the opposite helicities and leads to the helicity polarization, is demonstrated. Therefore, the helicity feature in Fe5- δ GeTe2 is controlled by both the dipolar interaction and DMI that the former leads to Bloch-type skyrmions with helicity of ±π/2 whereas the latter breaks the helicity degeneracy. This work provides new insights into the skyrmion topology in van der Waals materials

Chiral Helimagnetism and One‐Dimensional Magnetic Solitons in a Cr‐Intercalated Transition Metal Dichalcogenide

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