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

The effect of cage ventilation rate on the health of mice housed in Individually Ventilated Cages

The number of air changes per hour (ACH), an important index for individually ventilated cages (IVC), strongly affects the cage microenvironment and the health of laboratory animals. The objective of this study was to determine whether high or low cage ventilation adversely affects the health of mice housed in IVC systems and to identify cage ventilation rates suitable for the welfare of mice. We tested three different cage ventilation rates (40, 60, and 80 ACH) for 3 weeks in an IVC system. The temperature, relative humidity and ammonia concentrations in the cages were measured daily. The indices used to assess mouse health at specific time points throughout the study were body weight, stress hormones, T lymphocyte subsets (CD4 and CD8), immunoglobulins (IgG, IgM and IgA) and immune cells. There were no significant differences in body weight, growth hormones, immunoglobulin and T lymphocyte subsets in the IVC groups compared with the control group. The concentrations of corticosterone and epinephrine on day 7 of cage ventilation at 80 ACH were significantly higher than those in the control group (P < 0.05). Mice housed in 80 ACH cages had the lowest immune cell counts among all groups, and the numbers of lymphocytes and neutrophils were significantly lower than those in the control group (P < 0.05). In summary, cage ventilation at 60 ACH provided an optimum cage microenvironment for mouse health and welfare

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

MYH9 is an Essential Factor for Porcine Reproductive and Respiratory Syndrome Virus Infection

Porcine reproductive and respiratory syndrome (PRRS) caused by the PRRS virus (PRRSV) is an important swine disease worldwide. PRRSV has a limited tropism for certain cells, which may at least in part be attributed to the expression of the necessary cellular molecules serving as the virus receptors or factors on host cells for virus binding or entry. However, these molecules conferring PRRSV infection have not been fully characterized. Here we show the identification of non-muscle myosin heavy chain 9 (MYH9) as an essential factor for PRRSV infection using the anti-idiotypic antibody specific to the PRRSV glycoprotein GP5. MYH9 physically interacts with the PRRSV GP5 protein via its C-terminal domain and confers susceptibility of cells to PRRSV infection. These findings indicate that MYH9 is an essential factor for PRRSV infection and provide new insights into PRRSV-host interactions and viral entry, potentially facilitating development of control strategies for this important swine disease

Aspect of Clusters Correlation at Light Nuclei Excited State

The correlation of $\alpha\alpha$ was probed via measuring the transverse momentum $p_{T}$ and width $\delta p_{T}$ of one $\alpha$, for the first time, which represents the spatial and dynamical essentialities of the initial coupling state in $^{8}$Be nucleus. The weighted interaction vertex of 3$\alpha$ reflected by the magnitudes of their relative momentums and relative emission angles proves the isosceles triangle configuration for 3$\alpha$ at the high excited energy analogous Hoyle states.Comment: 8 pages, 9 figure

Variation of Tensor Force due to Nuclear Medium Effect

The enhancement of $J^{\pi}(T)$=3$^{+}$(0) state with isospin $T=0$ excited by the tensor force in the free $^{6}$Li nucleus has been observed, for the first time, relative to a shrinkable excitation in the $^{6}$Li cluster component inside its host nucleus. Comparatively, the excitation of $J^{\pi}(T)$=0$^{+}$(1) state with isospin $T=1$ for these two $^{6}$Li formations take on an approximately equal excitation strength. The mechanism of such tensor force effect was proposed due to the intensive nuclear medium role on isospin $T$=0 state.Comment: 6 pages, 4 figure

Multi-alpha Boson Gas state in Fusion Evaporation Reaction and Three-body Force

The experimental evidence for the $\alpha$ Boson gas state in the $^{11}$C+$^{12}$C$\rightarrow$$^{23}$Mg$^{\ast}$ fusion evaporation reaction is presented. By measuring the $\alpha$ emission spectrum with multiplicity 2 and 3, we provide insight into the existence of a three-body force among $\alpha$ particles. The observed spectrum exhibited distinct tails corresponding to $\alpha$ particles emitted in pairs and triplets consistent well with the model-calculations of AV18-UX and chiral effective field theory of NV2-3-la*, indicating the formation of $\alpha$ clusters with three-body force in the Boson gas state.Comment: 7 pages, 6 figure