Multi-Contrast Magnetic Resonance Imaging (MCMRI) utilizes the short-time reference image to facilitate the reconstruction of the long-time target one, providing a new solution for fast MRI. Although ...various methods have been proposed, they still have certain limitations. (1) Existing methods featuring the preset under-sampling patterns give rise to redundancy between multi-contrast images and limit their model performance. (2) Most methods focus on the information in the image domain, prior knowledge in the k-space domain has not been fully explored. (3) Most networks are manually designed and lack certain physical interpretability. To address these issues, we propose a joint optimization of the under-sampling pattern and a deep-unfolding dual-domain network for accelerating MCMRI. Firstly, to reduce the redundant information and sample more contrast-specific information, we propose a new framework to learn the optimal under-sampling pattern for MCMRI. Secondly, a dual-domain model is established to reconstruct the target image in both the image domain and the k-space frequency domain. The model in the image domain introduces a spatial transformation to explicitly model the inconsistent and unaligned structures of MCMRI. The model in the k-space learns prior knowledge from the frequency domain, enabling the model to capture more global information from the input images. Thirdly, we employ the proximal gradient algorithm to optimize the proposed model and then unfold the iterative results into a deep-unfolding network, called MC-DuDoN. We evaluate the proposed MC-DuDoN on MCMRI super-resolution and reconstruction tasks. Experimental results give credence to the superiority of the current model. In particular, since our approach explicitly models the inconsistent structures, it shows robustness on spatially misaligned MCMRI. In the reconstruction task, compared with conventional masks, the learned mask restores more realistic images, even under an ultra-high acceleration ratio (×30). Code is available at https://github.com/lpcccc-cv/MC-DuDoNet.
Ferguson plot was used to characterize the multiple intermediate species of bovine serum albumin (BSA) upon thermal unfolding. Differential scanning calorimetry showed an irreversible melting of BSA ...in Tris-HCl and phosphate buffers with a mid-transition temperature, Tm, of ∼68 °C. Thermally unfolded BSA was analyzed by agarose native gel electrophoresis stained by Coomassie blue and SYPRO Orange staining as a function of pH or protein concentration. SYPRO Orange was used to stain unfolded proteins. BSA heated at 70 and 80 °C, i.e., above the Tm, formed multiple intermediate species, which depended on the pH between 7.0 and 8.0, protein concentration and which buffer was used. These intermediate species were analyzed by Ferguson plot, which showed that BSA heated at 60 °C had a similar size to the native BSA, indicating that they are either native or native-like state consistent with no SYPRO Orange staining. The intermediate species observed at higher temperatures with the mobility less than that of the native BSA showed a steeper Ferguson plot and were stained by SYPRO Orange, indicating that these species had a larger hydrodynamic size than the native BSA and were unfolded.
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•Thermal unfolding of BSA was characterized by agarose native gel electrophoresis.•Ferguson plot was used to determine the size of the unfolded BSA oligomers.•The oligomers had higher Ferguson plot slopes.•DSC and SYPRO Orange staining indicated that these oligomers are unfolded.•Larger aggregates were formed more in Tris-HCl buffer than in sodium phosphate buffer at lower pH.
Molecular chaperones act on non-native proteins in the cell to prevent their aggregation, premature folding or misfolding. Different chaperones often exert distinct effects, such as acceleration or ...delay of folding, on client proteins via mechanisms that are poorly understood. Here we report the solution structure of SecB, a chaperone that exhibits strong antifolding activity, in complex with alkaline phosphatase and maltose-binding protein captured in their unfolded states. SecB uses long hydrophobic grooves that run around its disk-like shape to recognize and bind to multiple hydrophobic segments across the length of non-native proteins. The multivalent binding mode results in proteins wrapping around SecB. This unique complex architecture alters the kinetics of protein binding to SecB and confers strong antifolding activity on the chaperone. The data show how the different architectures of chaperones result in distinct binding modes with non-native proteins that ultimately define the activity of the chaperone.
The VERDI detector is a passive neutron detector, based on the multi-foil activation technique, developed for measurements in harsh environments, such as those encountered in fusion devices. During ...the 2019 Joint European Torus (JET) Deuterium-Deuterium (DD) campaign, irradiation studies of dosimetry foils and ITER material samples were performed at the JET Long Term Irradiation Station (LTIS). VERDI detectors were used to determine the neutron fluence and energy spectrum at the irradiation position of the samples. In the present work, the VERDI detector results are used as input to FISPACT-II to predict the radionuclide product inventory of the irradiated samples. The results of the calculations are compared against activities determined by gamma spectrometry. The application of the VERDI detector is demonstrated and data for the validation of activation predictions in ITER materials irradiated in a DD fusion environment are provided.
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•Self-folded boron nitride nanosheets are unfolded under external deformation.•Unfolding behavior is studied by molecular dynamics simulations.•A continuum model gives insights into ...the initiation of unfolding behavior.•The unfolding of boron nitride nanosheets improves the ductility of concrete.
A classical molecular dynamics simulation is adopted to study unfolding behavior of self-folded boron nitride nanosheets (BNNSs) in the cementitious composite. The simulation results show that unfoldings of the self-folded BNNSs initiate at the interface between the interlayers of the self-folded BNNSs because this interface is weaker than the BNNS-tobermorite interface under individual tensile and shear deformation. The interfacial microstructure shows that the ends of the self-folded BNNSs are embedded into the rugged surfaces of tobermorite, which exhibits a mechanical interlocking effect on the BNNSs-tobermorite interface. Furthermore, the existence of the hydrogen bonds at the BNNS-tobermorite interface yield a robust interfacial interaction between BNNSs and tobermorite. The critical peeling forces required to peel the adhesion between BNNS and tobermorite and between interlayers of self-folded BNNSs are obtained from a continuum model, which gives insights into the unfolding initiation of the self-folded BNNSs. The progressive peeling at the BNNS-tobermorite interface allows the self-folded BNNSs to maintain mechanical reinforcement in tobermorite under a large deformation at the atomistic scale. The atomistic scale reinforcement of the self-folded BNNSs in the deformation capacity and mechanical properties of cementitious composites significantly improves the ductility of cementitious composites.
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Drug product development of therapeutic antibody formulations is still dictated by the risk of protein particle formation during processing or storage, which can lead to loss of ...potency and potential immunogenic reactions. Since structural perturbations are the main driver for irreversible protein aggregation, the conformational integrity of antibodies should be closely monitored. The present study evaluated the applicability of a plate reader-based high throughput method for Intrinsic Tryptophan Fluorescence Emission (ITFE) spectroscopy to detect protein aggregation due to protein unfolding in high-concentrated therapeutic antibody samples. The impact of fluorophore concentration on the ITFE signal in microplate readers was investigated by analysis of dilution series of two therapeutic antibodies and pure tryptophan. At low antibody concentrations (< 5 mg/mL, equivalent to 0.8 mM tryptophan), the low inner filter effect suggests a quasi-linear relationship between antibody concentration and ITFE intensity. In contrast, the constant ITFE intensity at high protein concentrations (> 40 mg/mL, equivalent to 6.1 mM tryptophan) indicate that ITFE spectroscopy measurements of IgG1 antibodies are feasible in therapeutically relevant concentrations (up to 223 mg/mL). Furthermore, the capability of the method to detect low levels of unfolding (around 1 %) was confirmed by limit of detection (LOD) determination with temperature-stressed antibody samples as degradation standards. Change of fluorescence intensity at the maximum (ΔIaM) was identified as sensitive descriptor for protein degradation, providing the lowest LOD values. The results demonstrate that ITFE spectroscopy performed in a microplate reader is a valuable tool for high-throughput monitoring of protein degradation in therapeutic antibody formulations.
Solving linear inverse problems plays a crucial role in numerous applications. Algorithm unfolding based, model-aware data-driven approaches have gained significant attention for effectively ...addressing these problems. Learned iterative soft-thresholding algorithm (LISTA) and alternating direction method of multipliers compressive sensing network (ADMM-CSNet) are two widely used such approaches, based on ISTA and ADMM algorithms, respectively. In this work, we study optimization guarantees, i.e., achieving near-zero training loss with the increase in the number of learning epochs, for finite-layer unfolded networks such as LISTA and ADMM-CSNet with smooth soft-thresholding in an over-parameterized (OP) regime. We achieve this by leveraging a modified version of the Polyak-Łojasiewicz, denoted PL<inline-formula><tex-math notation="LaTeX">{}^{*}</tex-math></inline-formula>, condition. Satisfying the PL<inline-formula><tex-math notation="LaTeX">{}^{*}</tex-math></inline-formula> condition within a specific region of the loss landscape ensures the existence of a global minimum and exponential convergence from initialization using gradient descent based methods. Hence, we provide conditions, in terms of the network width and the number of training samples, on these unfolded networks for the PL<inline-formula><tex-math notation="LaTeX">{}^{*}</tex-math></inline-formula> condition to hold, by deriving the Hessian spectral norm. Additionally, we show that the threshold on the number of training samples increases with the increase in the network width. Furthermore, we compare the threshold on training samples of unfolded networks with that of a standard fully-connected feed-forward network (FFNN) with smooth soft-thresholding non-linearity. We prove that unfolded networks have a higher threshold value than FFNN. Consequently, one can expect a better expected error for unfolded networks than FFNN.
Cells assemble dynamic protein-based nanostructures far from equilibrium, such as microtubules, in a process referred to as dissipative assembly. Synthetic analogues have utilized chemical fuels and ...reaction networks to form transient hydrogels and molecular assemblies from small molecule or synthetic polymer building blocks. Here, we demonstrate dissipative cross-linking of transient protein hydrogels using a redox cycle, which exhibit protein unfolding-dependent lifetimes and mechanical properties. Fast oxidation of cysteine groups on bovine serum albumin by hydrogen peroxide, the chemical fuel, formed transient hydrogels with disulfide bond cross-links that degraded over hours by a slow reductive back reaction. Interestingly, despite increased cross-linking, the hydrogel lifetime decreased as a function of increasing denaturant concentration. Experiments showed that the solvent-accessible cysteine concentration increased with increasing denaturant concentration due to unfolding of secondary structures. The increased cysteine concentration consumed more fuel, which led to less direction oxidation of the reducing agent and affected a shorter hydrogel lifetime. Increased hydrogel stiffness, disulfide cross-linking density, and decreased oxidation of redox-sensitive fluorescent probes at a high denaturant concentration provided evidence supporting the unveiling of additional cysteine cross-linking sites and more rapid consumption of hydrogen peroxide at higher denaturant concentrations. Taken together, the results indicate that the protein secondary structure mediated the transient hydrogel lifetime and mechanical properties by mediating the redox reactions, a feature unique to biomacromolecules that exhibit a higher order structure. While prior works have focused on the effects of the fuel concentration on dissipative assembly of non-biological molecules, this work demonstrates that the protein structure, even in nearly fully denatured proteins, can exert similar control over reaction kinetics, lifetime, and resulting mechanical properties of transient hydrogels.
Numerous learning-based methods have been proposed to address the challenging task of solving inverse scattering problems (ISPs), with the requirement of ample labeled training datasets. To overcome ...the difficulty of collecting fully labeled datasets, we present a physically guided deep unfolding parallel network with self-supervised learning strategy to solve nonlinear ISPs, called SSISP-Net. The proposed method incorporates a physical model and the iterative optimization algorithm of ISPs into the network architecture and, thus, forms a deep unfolding network. First, here, two subsets of scattered fields are generated from fully measured scattered field data using receiver antenna positions. Second, we construct a parallel network by using two deep unfolding networks and then train this parallel network using only these two subsets of the scattered field. Finally, to constrain the two unfolding networks in the parallel network, we design two scattered field crossing losses and a contrast difference loss in the network. In this way, the reconstructed model can be properly trained using only scattered field data. The numerical and experimental results demonstrate that the proposed self-supervised learning method outperforms traditional and several other supervised learning methods under similar conditions. Moreover, it achieves competitive reconstruction performance compared with supervised learning methods known for their superior reconstruction capabilities.
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