Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing

The current neuron reconstruction pipeline for electron microscopy (EM) data usually includes automatic image segmentation followed by extensive human expert proofreading. In this work, we aim to reduce human workload by predicting connectivity between over-segmented neuron pieces, taking both microscopy image and 3D morphology features into account, similar to human proofreading workflow. To this end, we first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain, which is three orders of magnitude larger than existing datasets for neuron segment connection. To learn sophisticated biological imaging features from the connectivity annotations, we propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding. The learned embeddings can be easily incorporated with any point or voxel-based morphological representations for automatic neuron tracing. Extensive comparisons of different combination schemes of image and morphological representation in identifying split errors across the whole fly brain demonstrate the superiority of the proposed approach, especially for the locations that contain severe imaging artifacts, such as section missing and misalignment. The dataset and code are available at https://github.com/Levishery/Flywire-Neuron-Tracing.Comment: 9 pages, 6 figures, AAAI 2024 accepte

Unraveling the Molecular Magic of Witchweed

The root parasitic plant, Striga, is a serious agricultural pest causing damage to the agricultural products, such as sorghum, rice and cowpea, especially in developing countries. The parasitic plant can produce thousands of seeds which remain undetected under soil for decades, waiting for a suitable host plant. Striga hermonthica is one of the most damaging species in the genus Striga. When a field is infested by the growth of the parasite, which competes for nutrients with the host plant, the crop suffers damage and cannot be prevented by any previously known techniques. The family of plant hormones Strigolactones are responsible for the germination of the parasite. The Strigolactone mediated activation mechanism of the Strigolactone receptor proteins is not studied extensively. The recently obtained crystal structures of the receptor in Striga hermonthica provided a unique opportunity to explore the functional mechanism of strigolactone signaling in plants. We used computational modelling and simulations to understand the conformational changes associated with the activation of this receptor. Our simulations are performed on the Blue Waters supercomputer and are able to capture long timescale dynamics of the receptor protein. In our MD simulations of the apo and the holo receptor, we are able to identify multiple intermediate states as well as significant differences in the activation mechanism of apo and holo receptors. Our findings pave the path for the molecular design for chemical control of Striga infestations.Ope

Bulk photovoltaic effect in two-dimensional ferroelectric semiconductor α\alpha-In2_2Se3_3

Bulk photovoltaic effect, which arises from crystal symmetry-driven charge carrier separation, is an intriguing physical phenomenon that has attracted extensive interest in photovoltaic application due to its junction-free photovoltaic and potential to surpass Shockley-Queisser limit. Whereas conventional ferroelectric materials mostly suffer from extremely low photocurrent density and weak photovoltaic response at visible light wavelengths. Emerging two-dimensional ferroelectric semiconductors with coupled visible light absorption and spontaneous polarization characteristics are a promising alternative for making functional photoferroelectrics. Herein, we report the experimental demonstration of the bulk photovoltaic effect behavior based on the 2D ferroelectric semiconductor {$\alpha$-InSe caused by an out-of-plane polarization induced depolarization field. The {$\alpha$-InSe device exhibits enhanced bulk photovoltaic response in the visible light spectrum owing to its narrow bandgap. It was demonstrated that the generated photovoltaic current density was nearly two orders of magnitude greater than conventional bulk ferroelectric materials. These findings highlight the potential of 2D ferroelectric semiconductor materials for bulk photovoltaic applications in a broad spectral region

Non-ancient solution of the Ricci flow

For any complete noncompact K$\ddot{a}$hler manifold with nonnegative and bounded holomorphic bisectional curvature,we provide the necessary and sufficient condition for non-ancient solution to the Ricci flow in this paper.Comment: seven pages, latex fil

CG-fusion CAM: Online segmentation of laser-induced damage on large-aperture optics

Online segmentation of laser-induced damage on large-aperture optics in high-power laser facilities is challenged by complicated damage morphology, uneven illumination and stray light interference. Fully supervised semantic segmentation algorithms have achieved state-of-the-art performance, but rely on plenty of pixel-level labels, which are time-consuming and labor-consuming to produce. LayerCAM, an advanced weakly supervised semantic segmentation algorithm, can generate pixel-accurate results using only image-level labels, but its scattered and partially under-activated class activation regions degrade segmentation performance. In this paper, we propose a weakly supervised semantic segmentation method with Continuous Gradient CAM and its nonlinear multi-scale fusion (CG-fusion CAM). The method redesigns the way of back-propagating gradients and non-linearly activates the multi-scale fused heatmaps to generate more fine-grained class activation maps with appropriate activation degree for different sizes of damage sites. Experiments on our dataset show that the proposed method can achieve segmentation performance comparable to that of fully supervised algorithms