Semisimple FJRW theory of polynomials with two variables

We study the Dubrovin-Frobenius manifold in the Fan-Jarvis-Ruan-Witten theory of Landau-Ginzburg pairs (W, \), where $W$ is an invertible nondegenerate quasihomogeneous polynomial with two variables and $\$ is the minimal admissible group of$W$. We conjecture that the Dubrovin-Frobenius manifolds from these FJRW theory are semisimple. We show the conjecture holds true for simple singularities and almost all Brieskorn-Pham polynomials. For Brieskorn-Pham polynomials, the result follows from the calculation of a quantum Euler class in the FJRW theory. As a consequence, our result shows that for the FJRW theory of these Landau-Ginzburg pairs, both a Dubrovin type conjecture and a Virasoro conjecture hold true.Comment: 2nd version, 24 pages, a reference of Habermann is adde

mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to joint learning of multimodal images. However, in clinical practice, it is not always possible to acquire a complete set of MRIs, and the problem of missing modalities causes severe performance degradation in existing multimodal segmentation methods. In this work, we present the first attempt to exploit the Transformer for multimodal brain tumor segmentation that is robust to any combinatorial subset of available modalities. Concretely, we propose a novel multimodal Medical Transformer (mmFormer) for incomplete multimodal learning with three main components: the hybrid modality-specific encoders that bridge a convolutional encoder and an intra-modal Transformer for both local and global context modeling within each modality; an inter-modal Transformer to build and align the long-range correlations across modalities for modality-invariant features with global semantics corresponding to tumor region; a decoder that performs a progressive up-sampling and fusion with the modality-invariant features to generate robust segmentation. Besides, auxiliary regularizers are introduced in both encoder and decoder to further enhance the model's robustness to incomplete modalities. We conduct extensive experiments on the public BraTS $2018$ dataset for brain tumor segmentation. The results demonstrate that the proposed mmFormer outperforms the state-of-the-art methods for incomplete multimodal brain tumor segmentation on almost all subsets of incomplete modalities, especially by an average 19.07% improvement of Dice on tumor segmentation with only one available modality. The code is available at https://github.com/YaoZhang93/mmFormer.Comment: Accepted to MICCAI 202

Heterogeneous ice nucleation correlates with bulk-like interfacial water

Establishing a direct correlation between interfacial water and heterogeneous ice nucleation (HIN) is essential for understanding the mechanism of ice nucleation. Here, we study the HIN efficiency of polyvinyl alcohol (PVA) surfaces with different densities of hydroxyl groups. We find that the HIN efficiency increases with the decrease of the hydroxyl group density. By explicitly considering that interfacial water molecules of PVA films consist of ‘tightly bound water’, ‘bound water’ and ‘bulk-like water’, we reveal that ‘bulk-like water’ can be correlated directly to the HIN efficiency of surfaces. As the density of hydroxyl groups decreases, ‘bulk-like water’ molecules can rearrange themselves with a reduced energy barrier into ice due to the diminishing constraint by the hydroxyl groups on the PVA surface. Our study not only provides a new strategy on experimentally controlling HIN efficiency but also gives another perspective in understanding the mechanism of ice nucleation, i.e., the phase change efficiency of ‘bulk-like’ interfacial water of a film is a predictor for the HIN efficiency of that film

Improving GAN Training via Feature Space Shrinkage

Due to the outstanding capability for data generation, Generative Adversarial Networks (GANs) have attracted considerable attention in unsupervised learning. However, training GANs is difficult, since the training distribution is dynamic for the discriminator, leading to unstable image representation. In this paper, we address the problem of training GANs from a novel perspective, \emph{i.e.,} robust image classification. Motivated by studies on robust image representation, we propose a simple yet effective module, namely AdaptiveMix, for GANs, which shrinks the regions of training data in the image representation space of the discriminator. Considering it is intractable to directly bound feature space, we propose to construct hard samples and narrow down the feature distance between hard and easy samples. The hard samples are constructed by mixing a pair of training images. We evaluate the effectiveness of our AdaptiveMix with widely-used and state-of-the-art GAN architectures. The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples. We also show that our AdaptiveMix can be further applied to image classification and Out-Of-Distribution (OOD) detection tasks, by equipping it with state-of-the-art methods. Extensive experiments on seven publicly available datasets show that our method effectively boosts the performance of baselines. The code is publicly available at https://github.com/WentianZhang-ML/AdaptiveMix.Comment: Accepted by CVPR'2023. Code and Demo are available at https://github.com/WentianZhang-ML/AdaptiveMi

Characterization of Zur-dependent genes and direct Zur targets in Yersinia pestis

<p>Abstract</p> <p>Background</p> <p>The zinc uptake regulator Zur is a Zn²⁺-sensing metalloregulatory protein involved in the maintenance of bacterial zinc homeostasis. Up to now, regulation of zinc homeostasis by Zur is poorly understood in <it>Y. pestis</it>.</p> <p>Results</p> <p>We constructed a <it>zur </it>null mutant of <it>Y. pestis </it>biovar <it>microtus </it>strain 201. Microarray expression analysis disclosed a set of 154 Zur-dependent genes of <it>Y. pestis </it>upon exposure to zinc rich condition. Real-time reverse transcription (RT)-PCR was subsequently used to validate the microarray data. Based on the 154 Zur-dependent genes, predicted regulatory Zur motifs were used to screen for potential direct Zur targets including three putative operons <it>znuA, znuCB </it>and <it>ykgM</it>-<it>RpmJ2</it>. The LacZ reporter fusion analysis verified that Zur greatly repressed the promoter activity of the above three operons. The subsequent electrophoretic mobility shift assay (EMSA) demonstrated that a purified Zur protein was able to bind to the promoter regions of the above three operons. The DNase I footprinting was used to identify the Zur binding sites for the above three operons, verifying the Zur box sequence as predicted previously in γ-Proteobacteria. The primer extension assay was further used to determine the transcription start sites for the above three operons and to localize the -10 and -35 elements. Zur binding sites overlapped the -10 sequence of its target promoters, which was consistent with the previous observation that Zur binding would block the entry of the RNA polymerase to repress the transcription of its target genes.</p> <p>Conclusion</p> <p>Zur as a repressor directly controls the transcription of <it>znuA, znuCB </it>and <it>ykgM</it>-<it>RpmJ2 </it>in <it>Y. pestis </it>by employing a conserved mechanism of Zur-promoter DNA association as observed in γ-Proteobacteria. Zur contributes to zinc homeostasis in <it>Y. pestis </it>likely through transcriptional repression of the high-affinity zinc uptake system ZnuACB and two alternative ribosomal proteins YkgM and RpmJ2.</p

Folic acid therapy reduces the first stroke risk associated with hypercholesterolemia among hypertensive patients

Background and Purpose - We sought to determine whether folic acid supplementation can independently reduce the risk of first stroke associated with elevated total cholesterol levels in a subanalysis using data from the CSPPT (China Stroke Primary Prevention Trial), a double-blind, randomized controlled trial. Methods - A total of 20 702 hypertensive adults without a history of major cardiovascular disease were randomly assigned to a double-blind daily treatment of an enalapril 10-mg and a folic acid 0.8-mg tablet or an enalapril 10-mg tablet alone. The primary outcome was first stroke. Results - The median treatment duration was 4.5 years. For participants not receiving folic acid treatment (enalapril-only group), high total cholesterol (≥ 200 mg/dL) was an independent predictor of first stroke when compared with low total cholesterol (\u3c200 mg/dL; 4.0% versus 2.6%; hazard ratio, 1.52; 95% confidence interval, 1.18-1.97; P=0.001). Folic acid supplementation significantly reduced the risk of first s roke among participants with high total cholesterol (4.0% in the enalapril-only group versus 2.7% in the enalapril-folic acid group; hazard ratio, 0.69; 95% confidence interval, 0.56-0.84 P\u3c0.001; number needed to treat, 78; 95% confidence interval, 52-158), independent of baseline folate levels and other important covariates. By contrast, among participants with low total cholesterol, the risk of stroke was 2.6% in the enalapril-only group versus 2.5% in the enalapril-folic acid group (hazard ratio, 1.00; 95% confidence interval, 0.75-1.30; P=0.982). The effect was greater among participants with elevated total cholesterol (P for interaction=0.024). Conclusions - Elevated total cholesterol levels may modify the benefits of folic acid therapy on first stroke. Folic acid supplementation reduced the risk of first stroke associated with elevated total cholesterol by 31% among hypertensive adults without a history of major cardiovascular diseases

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts