Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems

Recently, data-driven techniques have demonstrated remarkable effectiveness in addressing challenges related to MR imaging inverse problems. However, these methods still exhibit certain limitations in terms of interpretability and robustness. In response, we introduce Convex Latent-Optimized Adversarial Regularizers (CLEAR), a novel and interpretable data-driven paradigm. CLEAR represents a fusion of deep learning (DL) and variational regularization. Specifically, we employ a latent optimization technique to adversarially train an input convex neural network, and its set of minima can fully represent the real data manifold. We utilize it as a convex regularizer to formulate a CLEAR-informed variational regularization model that guides the solution of the imaging inverse problem on the real data manifold. Leveraging its inherent convexity, we have established the convergence of the projected subgradient descent algorithm for the CLEAR-informed regularization model. This convergence guarantees the attainment of a unique solution to the imaging inverse problem, subject to certain assumptions. Furthermore, we have demonstrated the robustness of our CLEAR-informed model, explicitly showcasing its capacity to achieve stable reconstruction even in the presence of measurement interference. Finally, we illustrate the superiority of our approach using MRI reconstruction as an example. Our method consistently outperforms conventional data-driven techniques and traditional regularization approaches, excelling in both reconstruction quality and robustness

Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI

Recently, regularization model-driven deep learning (DL) has gained significant attention due to its ability to leverage the potent representational capabilities of DL while retaining the theoretical guarantees of regularization models. However, most of these methods are tailored for supervised learning scenarios that necessitate fully sampled labels, which can pose challenges in practical MRI applications. To tackle this challenge, we propose a self-supervised DL approach for accelerated MRI that is theoretically guaranteed and does not rely on fully sampled labels. Specifically, we achieve neural network structure regularization by exploiting the inherent structural low-rankness of the $k$-space data. Simultaneously, we constrain the network structure to resemble a nonexpansive mapping, ensuring the network's convergence to a fixed point. Thanks to this well-defined network structure, this fixed point can completely reconstruct the missing $k$-space data based on matrix completion theory, even in situations where full-sampled labels are unavailable. Experiments validate the effectiveness of our proposed method and demonstrate its superiority over existing self-supervised approaches and traditional regularization methods, achieving performance comparable to that of supervised learning methods in certain scenarios

Cold tolerance identification of nine Rosa L. materials and expression patterns of genes related to cold tolerance in Rosa hybrida

Members of the Rosa genus have a high ornamental value, but their cultivation area is limited by their sensitivity to cold temperatures. The aim of this study was to evaluate the cold tolerance of a range of Rosa materials, and then determine which genes were related to cold tolerance. Nine Rosa materials were subjected to a cold treatment. To identify genes related to cold tolerance, R. hybrida was treated at −15°C for 10 min, and leaves collected before and after this treatment were collected for RNA-Seq analyses. The transcript profiles of four DEGs (POD17, NDUFA9, PMA1, and b-Amy1) in R. hybrida were determined by qRT-PCR at 0 h, 1 h, 2 h, and 3 h at −15°C. Nine Rosa materials were subjected to a cold treatment, and the most cold-tolerant materials were identified as those that showed the lowest levels of electrolyte leakage and the best recovery after 30 d of growth. The most cold-tolerant materials were Rosa hybrida, Rosa rugosa ‘Pingyin 12’, and Rosa rugosa. In total, 204 significantly differentially expressed genes (DEGs) were identified, of which 88 were significantly up-regulated and 116 were significantly down-regulated under cold conditions. Gene Ontology classification and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that the DEGs were enriched in 57 pathways, especially starch and sucrose metabolism, phenylpropane biosynthesis, MAPK signaling, fructose and mannose metabolism, and oxidative phosphorylation. By transcriptional analysis, PMA1, which was related to H+ ATPase activity, was continuously up-regulated, but the transcript levels of POD17, NDUFA9, and β-Amy1 fluctuated during the freezing treatment. This research uncovered scarce cold-resistant materials and layed the foundation for further research on the cold tolerance mechanism of Rosa plants and the breeding of cold-tolerant varieties

Complete chloroplast genomes of 11 Sabia samples: Genomic features, comparative analysis, and phylogenetic relationship

The genus Sabia is a woody climber belonging to the family Sabiaceae, order Proteales. Several species of this genus have been utilized as medicines for treating diseases, such as rheumatic arthritis, traumatism, hepatitis, etc. However, the lack of molecular data has prevented the accurate identification and refinement of taxonomic relationships in this genus. In this study, chloroplast genomes of 11 samples of the genus Sabia were assembled and analyzed. These chloroplast genomes showed a typical quadripartite structure and ranged in length from 160,956 to 162,209 bp. The structure of the genomes was found to be relatively conserved, with 130 genes annotated, including 85 coding genes, 37 tRNA genes, and eight rRNA genes. A total of 78–98 simple sequence repeats and 52–61 interspersed repeats were detected. Sequence alignment revealed 11 highly variable loci in chloroplast genomes. Among these loci, ndhF-ndhD achieved a remarkably higher resolution than the other regions. In addition, phylogenetic analysis indicated that Sect. Pachydiscus and Sect. Sabia of Sabia did not form two separate monophyletic groups. The divergence time calculated based on the Reltime method indicated that the evolutionary branches of Sabia and Meliosma started to form approximately 85.95 million years ago (Mya), and the species within Sabia began to diverge approximately 7.65 Mya. In conclusion, our study provides a basis for comprehensively exploring the phylogenetic relationships of Sabia. It also provides a methodological basis and data support for establishing a standardized and scientific identification system for this genus

MUX64, an analogue 64-to-1 multiplexer ASIC for the ATLAS High Granularity Timing Detector

We present the design and the performance of MUX64, a 64-to-1 analogue multiplexer ASIC for the ATLAS High Granularity Timing Detector (HGTD). The MUX64 transmits one of its 64 inputs selected by six address lines for the voltages or temperatures being monitored to an lpGBT ADC channel. The prototype ASICs fabricated in TSMC 130 nm CMOS technology were prepared in wire-bonding and QFN88 packaging format. A total of 280 chips was examined for functionality and quality assurance. The accelerated aging test conducted at 85 degrees celsius shows negligible degradation over 16 days