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• A novel energy finite element method (EFEM) considering aerothermoelastic effects is proposed. • The proposed EFEM can predict the high-frequency vibration response of heated panels in supersonic airflow. • The proposed EFEM is applicable to panels in various states of aerothermoelastic response. • Effects of aerothermoelasticity on the high-frequency vibration response are revealed. Vibration analysis of heated panels in supersonic airflow under aerothermoelastic effects has attracted wide attention, but the high-frequency vibration characteristics have not yet been thoroughly studied. A novel energy finite element method (EFEM), which can consider the aerothermoelastic effects, is proposed to investigate the high-frequency vibration response of heated panels in supersonic airflow. By separating the high-frequency response from the static or low-frequency aerothermoelastic response, a linearized high-frequency forced vibration equation is derived from the nonlinear motion equation of the panel. On this basis, the effects of in-plane and aerodynamic forces on the propagation properties of elastic waves in the panel are analyzed theoretically. By introducing these effects and the equivalent loss factors of elastic waves into EFEM theory, the energy density governing equation and the corresponding energy finite element model are established. Through numerical comparative studies, it is demonstrated that the proposed EFEM can well capture the aerothermoelastic effects and efficiently predict the high-frequency vibration response of heated panels in supersonic airflow. Particularly, the present EFEM is valid for panels in different aerothermoelastic states such as thermal buckling and flutter. In addition, the effects of aerothermoelasticity on the high-frequency vibration response are studied in detail, and the corresponding effect mechanism is revealed. [ABSTRACT FROM AUTHOR]

• The dynamic stability of a lossy locally resonant metamaterial panel (LLRMP) in supersonic flow is studied. • The impact of bandgap and metadamping properties on the dynamic stability of LLRMP is discussed. • The instability frequency mainly affected by the bandgap position is revealed. • The instability dynamic pressure mainly affected by the metadamping is expounded. • The ability of LLRMP in changing the critical instability dynamic pressure is investigated. In this paper, aimed at a lossy locally resonant metamaterial panel (LLRMP) in supersonic flow, the impact of changing bandgap and metadamping properties on dynamic stability considering the fluid-structure interaction (FSI) is studied for the first time. The analytical model for studying the transmission coefficient and dynamic stability is established by using the Galerkin method, while the complex energy band is obtained based on the Bloch theorem. The bandgap properties mainly affect the instability frequency, which is attributed to the change of coupling modes that dominate the dynamic instability. The metadamping properties play an important role in affecting the critical instability dynamic pressure, which can be increased/decreased by positive/negative metadamping. The mechanism of metadamping properties changing the critical instability dynamic pressure is that the influences of damping of the host panel and locally resonant unit on the root loci are different. Besides, the ability of LLRMP in changing the critical instability dynamic pressure is investigated, which demonstrates that the critical instability dynamic pressure can be increased by 16.13 % even though the mass ratio is only 0.1. Our work broadens the potential applications of locally resonant metamaterials and provides a fresh perspective on anti-flutter design of panels in supersonic flow. [ABSTRACT FROM AUTHOR]

• An advanced time-marching PINN method (AT-PINN) is proposed in this work. • The method is valid for structural vibration analysis in long-duration simulation. • Four key techniques are incorporated into the AT-PINN approach. • The method can offer accurate solutions with lower computational cost for dynamic analysis. Solving partial differential equations through deep learning has recently received wide attention, with physics-informed neural networks (PINNs) being successfully used and showing great potential. This study focuses on the development of an efficient PINN approach for structural vibration analysis in " long-duration " simulation that is still a technical but unresolved issue of PINN. The accuracies of the standard PINN (STD-PINN) and conventional time-marching PINN (CT-PINN) methods in solving vibration equations, especially free-vibration equations, are shown to decrease to varying degrees with the simulation time. To resolve this problem, an advanced time-marching PINN (AT-PINN) approach is proposed. This method is used to solve structural vibration problems over successive time segments by adopting four key techniques: normalization of the spatiotemporal domain in each time segment, a reactivating optimization algorithm, transfer learning and the sine activation function. To illustrate the advantages of the AT-PINN approach, numerical simulations for the forced and free vibration analysis of strings, beams and plates are performed. In addition, the vibration analysis of plates under multi-physics loads is also studied. The results show that the AT-PINN approach can provide accurate solutions with lower computational cost even in long-duration simulation. The techniques adopted are verified to effectively avoid the offset of the spatiotemporal domain, reduce the accumulative error and enhance the training efficiency. The present one overcomes the drawback of the existing PINN methods and is expected to become an effective method for solving time-dependent partial differential equations in long-duration simulation. [ABSTRACT FROM AUTHOR]

• An energy flow model of two-dimensional panels in supersonic airflow is developed. • Equivalent damping due to supersonic airflow is introduced to derive the energy flow model. • The impedance of the panel in supersonic airflow is derived to calculate the input power. • Supersonic airflow leads to different equivalent damping for elastic waves propagating in different directions. • An energy finite element method for high-frequency analysis of elastic waves with different damping is developed. Many energy flow models have been proposed for high-frequency forced vibration analysis of structures. In this paper, a novel energy flow model is developed to predict the high-frequency vibration response of panels in supersonic airflow and quantify the effects of supersonic airflow on high-frequency forced vibration characteristics. The additional damping due to supersonic airflow is derived from the motion equation of a two-dimensional panel. The relationship between the wavenumber and the group velocity is introduced to describe the energy transmission property considering the effects of supersonic airflow. Then the energy density governing equation (i.e. energy flow model) is established and solved by the energy flow analysis (EFA) and the energy finite element method (EFEM). Finally, comparing the vibration responses obtained by the present energy flow model with the corresponding exact analytical solutions, the developed energy flow model is verified to be effective for high-frequency vibration analysis of panels in supersonic airflow. Furthermore, the numerical simulations indicate that supersonic airflow can affect the equivalent damping of the propagating elastic waves, and thus change the energy density distribution of the panel. [ABSTRACT FROM AUTHOR]

In order to predict the high frequency vibration response of beams with axial force, an energy finite element method (EFEM) is developed. An Euler–Bernoulli beam with constant axial force is considered. The energy density and energy intensity of the beam with axial force are introduced, and the relationship between the wavenumber and group velocity is derived, then the energy density governing equation is established. In addition, the impedance of beam with axial force is derived to calculate the input power, and the energy density is evaluated by solving the governing equation using finite element method. Finally, the feasibility and correctness of the present EFEM are verified by comparing the results obtained by the EFEM with the corresponding exact solutions. The effects of axial force on the energy density response and the limitations of EFEM are also discussed. [ABSTRACT FROM AUTHOR]

Background: Chemotherapy has been widely used to treat cancer, but the appearance of multidrug resistance (MDR) is the biggest obstacle to successful chemotherapy. One of the conventional mechanisms of MDR is overexpression of ATP-binding cassette (ABC) transporters such as P-glycoprotein (P-gp/ABCB1) and multidrug resistance-associated proteins (MRPs/ABCCs) that limits the prolonged and efficient use of chemotherapeutic drugs. To enhance the chemosensitivity of tumor cells, attentions have been focused on effective MDR modulators.Purpose: This study aimed to investigate the reversal effect of quercetin on MDR, and explored its mechanism of action in vitro.Study Design/methods: The effect and mechanism of quercetin on MDR was examined by using MTT assay, flow cytometry, real-time PCR and western blot analysis in human hepatocellular carcinoma cells.Results: Our data found that the intracellular accumulation of rhodamine-123 (Rh123) and doxorubicin (ADR) were increased, the sensitivity of BEL/5-FU cells to chemotherapeutic drugs were increased, and the expressions of ABCB1, ABCC1 and ABCC2 were all down-regulated, which indicated that the functions and expressions of ABCB1, ABCC1 and ABCC2 efflux pump were inhibited by quercetin treatment. Moreover, the suppression of ABCB1, ABCC1 and ABCC2 by quercetin was dependent on the FZD7 through the Wnt/β-catenin pathway. Further research revealed that reduction of FZD7 by RNA interference (siFZD7) enhanced the sensitivity to chemotherapeutic drugs, increased the cellular accumulation of Rh123 and ADR, and induced inhibitory effects on the expression of FZD7, ABCB1, ABCC1, ABCC2 and β-catenin, similar to quercetin. In the meanwhile, overexpression of FZD7 showed the inversely effect on the expressions. Interesting, it was confirmed that quercetin could inhibit the expression levels of FZD7, ABCB1, ABCC1, ABCC2 and β-catenin in BEL-7402 cells; furthermore, treatment by quercetin combined with siFZD7 in BEL/5-FU cells, the expressions of these genes were effectively decreased in comparison to quercetin combined with siRNA negative control (sncRNA).Conclusion: Overall, these data suggested the effectiveness of using quercetin, at least in part, via inhibiting FZD7 to combat chemoresistance and showed that quercetin could be developed into an efficient natural sensitizer for resistant human hepatocellular carcinoma. [ABSTRACT FROM AUTHOR]

• A novel petal-type battery thermal management system (BTMS) with dual phase change materials (PCMs) is proposed. • The thermal performance of the proposed BTMS at different ambient temperatures and discharge rates is investigated. • The environmental adaptability of 18650 Li-ion batteries is significantly improved. • The proposed BTMS is optimized by fin-reinforced heat transfer technology. Phase change material (PCM) cooling is a prominent approach for battery thermal management. However, its application scenario is severely limited by the narrow range of phase change temperature. When the temperature of PCM does not reach the phase change temperature range, it is either a pure thermal conductive material or a completely molten liquid material, neither of which can play a role in battery thermal management. The present study proposes a novel petal-type battery thermal management system (BTMS) with dual PCMs, which has significantly enhanced the environmental adaptability of the battery based on PCM cooling. The thermal performance of the BTMS at different ambient temperatures and discharge rates is investigated by numerical simulation methods. The results show that when the battery is discharged at 1∼5C at an ambient temperature of 25°C, the maximum battery temperature (T max) is 29.87°C, 32.89°C, 34.69°C, 36.84°C, and 39.41°C, respectively. When the battery is discharged at 2C at 20°C, 30°C and 40°C, the T max is 30.49°C, 34.16°C and 43.70°C, respectively. Compared with the other two conventional BTMSs using a single type of PCM, the thermal performance and environmental adaptability of the proposed BTMS is the best under the three typical ambient temperatures. Furthermore, the above BTMS is optimized using fin-reinforced heat transfer technology in this paper. Compared to the BTMS without fins, the optimized solution 2 with asymmetric fin arrangement reduces the maximum temperature difference (Δ T max) by 5.53% and 29.19% and the maximum temperature rise (Δ T mrise) by 36.15% and 42.76% at ambient temperatures of 30°C and 40°C. [ABSTRACT FROM AUTHOR]

[Display omitted] • 571 DEmRNAs and 61 DElncRNAs were LAAIS specific DEmRNAs and DElncRNAs, respectively. • 325 DEmRNAs and 66 DElncRNAs were respectively SAOIS specific DEmRNAs and DElncRNAs. • 3086 LAAIS specific DElncRNA-DEmRNA and 661 SAOIS specific DElncRNA-DEmRNA co-expression pairs were obtained. • Oxidative phosphorylation and Alzheimer's disease were enriched pathways in both LAAIS specific and LAAIS specific DEmRNAs. The goal of our study is to uncover the pathogenesis of large-artery atherosclerotic ischemic stroke (LAAIS) and small-artery occlusion ischemic stroke (SAOIS) and analyze their difference using RNA sequencing. RNA sequencing was used to filtrate differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) in LAAIS and SAOIS. Specific DEmRNAs and DElncRNAs in LAAIS and SAOIS were further found. Functional annotation and DElncRNA-DEmRNA co-expression network were built to reveal biological function of DEmRNAs. A total of 832 DEmRNAs and 96 DElncRNAs were identified in LAAIS vs normal controls. 587 DEmRNAs and 105 DElncRNAs were identified in SAOIS vs normal controls. In LAAIS vs SAOIS, 636 DEmRNAs and 112 DElncRNAs were identified. Among which, 571 DEmRNAs and 61 DElncRNAs were LAAIS specific DEmRNAs and DElncRNAs, respectively. 325 DEmRNAs and 66 DElncRNAs were respectively SAOIS specific DEmRNAs and DElncRNAs. We also obtained 3086 LAAIS specific DElncRNA-DEmRNA co-expression pairs and 661 SAOIS specific DElncRNA-DEmRNA co-expression pairs. Oxidative phosphorylation and Alzheimer's disease were significantly enriched pathways in both LAAIS specific DEmRNAs and DEmRNAs in LAAIS specific DElncRNA-DEmRNA co-expression network. ECM-receptor interaction, hypertrophic cardiomyopathy and dilated cardiomyopathy were significantly enriched pathways in both SAOIS specific DEmRNAs and DEmRNAs in SAOIS specific DElncRNA-DEmRNA co-expression network. This finding may help to understand the mechanisms of LAAIS and SAOIS and offer novel clues for finding specific biomarkers for LAAIS and SAOIS. [ABSTRACT FROM AUTHOR]

Multidrug resistance (MDR) is a serious phenomenon employed by cancer cells which hampers the success of cancer pharmacotherapy. One of the common mechanisms of MDR is the overexpression of ATP-binding cassette (ABC) efflux transporters in cancer cells such as P-glycoprotein (P-gp/ABCB1), multidrug resistance-associated protein 2 (MRP2/ABCC2), and breast cancer resistance protein (BCRP/ABCG2) that limits the prolonged and effective use of chemotherapeutic drugs. Researchers have found that developing inhibitors of ABC efflux transporters as chemosensitizers could overcome MDR. But the clinical trials have shown that most of these chemosensitizers are merely toxic and only show limited or no benefits to cancer patients, thus new inhibitors are being explored. Recent findings also suggest that efflux pumps of the ABC transporter family are subject to epigenetic gene regulation. In this review, we summarize recent findings of the role of ABC efflux transporters in MDR. [ABSTRACT FROM AUTHOR]

Abstract: Chemotherapy has been widely used to treat cancer, however, the appearance of multiple drug resistance (MDR) in cancer patients is regarded as a major clinical obstacle to successful chemotherapy. MicroRNAs (miRNAs) are evolutionary conserved small RNAs that regulate gene expression at the post-transcriptional level and have been shown to regulate cell differentiation, development, proliferation and apoptosis. Nevertheless, the involvement of miRNAs and their roles in the development of MDR in liver cancer are not fully understood. Our study found that the expression of miR-27a was down-regulated in the multidrug-resistant hepatocellular carcinoma cell line BEL-7402/5-fluorouracil (BEL/5-FU) compared with its parental BEL-7402 cell line, while the MDR1/P-glycoprotein expression was elevated. Overexpression of miR-27a by transfecting with miR-27a mimics in the BEL/5-FU cells could reduce the MDR1/P-glycoprotein and β-catenin expressions, enhance the sensitivity of these cells to 5-fluorouracil and 5-fluorouracil-induced apoptosis. Moreover, up-regulation of miR-27a did not decrease the FZD7 mRNA level, but significantly reduce its protein expression in BEL/5-FU cells. It was also confirmed that reduction of FZD7 by RNA interference induced inhibitory effects on the expression of MDR1/P-glycoprotein and β-catenin, similar to miR-27a. Taken together, our findings suggest that miR-27a could function as a novel regulator to reverse MDR in hepatocellular carcinoma cells by inhibiting the FZD7/β-catenin pathway. [Copyright &y& Elsevier]

Abstract: The electrosorption performance of activated carbon (AC) electrodes with various potentials and concentrations was studied. It was found that electrosorption capacity and rate increased with the increasing of the bias potentials and solution concentrations. Such trend was found to be correlated to the increase of the electrostatic force and the decrease of the electrolyte resistance. The investigation of the electrosorption isotherm showed that the experimental data can be well represented by Langmuir isotherm. And the kinetics analysis indicated that the electrosorption of NaCl onto AC electrodes followed pseudo-first-order kinetics model. [Copyright &y& Elsevier]

In the commented paper, the authors considered a two-dimensional airfoil with nonlinear stiffness and damping, and derived the amplitude modulation equation (AME) of the system limit cycle oscillation around its bifurcation. Based on the variation equation of the AME, they conducted the stability analysis. Unfortunately, the obtained variation equation associated with the AME is incorrect. [ABSTRACT FROM AUTHOR]

Abstract: Dynamic magnetic resonance imaging (MRI) acquires a sequence of images for the visualization of the temporal variation of tissue or organs. Keyhole methods such as Fourier keyhole (FK) and keyhole SVD (KSVD) are the most popular methods for image reconstruction in dynamic MRI. This paper provides a class of adaptive keyhole methods, called adaptive FK (AFK) and adaptive KSVD (AKSVD), for dynamic MRI reconstruction. The proposed methods are based on the conventional Fourier encoding and SVD encoding schemes. Instead of the conventional keyhole methods’ duplication of un-acquired data from the reference images, the proposed methods use a temporal model to depict the inter-frame dynamic changes and to estimate the un-acquired data in each successive frame. Because the model is online identified from the acquired data, the proposed methods do not require the pre-imaging process, the navigator signals, and any prior knowledge of the imaged objects. Furthermore, the new methods use the conventional keyhole encoding schemes without the bias to any particular object characters, and the temporal model for updating information is in the general form of AR process without the preference to any particular motion types. Hence, the proposed methods are designed as a generic approach to dynamic MRI, other than for any specific class of objects. Studies on dynamic MRI data set show that the new methods can produce images with much lower reconstruction error than the conventional FK and KSVD. [Copyright &y& Elsevier]

Purpose: To evaluate the clinical utility of the application of a deep learning motion correction technique on 3D MPRAGE magnetic resonance images acquired in routine clinical practice.Methods: An encoder-decoder deep learning network inspired by InceptionResnet was trained on public datasets. The clinical utility of the trained network was evaluated retrospectively on 27 3D MPRAGE T1 weighted motion degraded MR images identified by radiologists during reporting. The assessment of image quality was performed by one board-certified radiologist and one senior radiology trainee for nine neuroanatomical regions of the brain using a five-point visual grading scale.Results: The deep learning motion correction technique resulted in reduced ghosting, ringing and blurring for all the brain regions investigated. The larger regions of interest such as ventricles improved the least (1.81 to 1.16, p-value: < 0.0001) while the smaller but complex regions such as the hippocampus improved most (3.0 to 1.67, p-value: < 0.0001). The Wilcox rank tests of image quality differences for the nine neuroanatomical regions were all statistically significant (p < 0.001). Overall, 60 % of the neuroanatomical regions were improved, 39 % were unchanged and 1 % were degraded. Out of the unchanged cases, 28 % were already scored at the highest image quality before motion correction. It was found that approximately 13 % of repeated scans could be avoided using the DL motion correction approach.Conclusion: The deep learning motion correction technique improved the overall visual perception of the 3D T1 weighted MPRAGE brain images. This would improve the clinical utility of otherwise motion degraded images and allow visualisation of normal anatomy and even subtle pathology. [ABSTRACT FROM AUTHOR]

In order to meet the high demand for joining ceramic/superalloy composite structure in extreme environments, a novel high-temperature resistant adhesion technique was developed for joining ZrO 2 and Inconel 625 by applying an aluminum phosphate emulsion/zirconium sol based adhesive. With increasing temperature, a series of reactions occurred in adhesive, and its high-temperature bonding was attributed to the formation of a composite structure containing various ceramics and intermetallics. The adhesive after RT curing could find direct applications in extreme environments, and provide bonding strength no less than 2.5 MPa in the temperature range of RT-1100 °C. The bonding strength was higher than 4 MPa in the temperature range of 800–1000 °C, which was further attributed to the formation of an effective CTE-gradient relationship among ZrO 2 , adhesive and Inconel 625, as well as the interfacial reactions between the two substrates. The work broadened the application of adhesion technique and brought new ideas for joining dissimilar engineering materials. [ABSTRACT FROM AUTHOR]

Abstract: MicroRNA-148a (miR-148a) which suppresses tumor growth by directly decreasing DNMT1 expression has been demonstrated as an important role for cancer therapy. The mechanisms for miR-148a in cancer will become potential future researches. [Copyright &y& Elsevier]

Hepatocellular carcinoma (HCC) is often diagnosed at advanced stages with no effective treatment options. Mechanistically, it is a complex biological process. Recently, the main cause of its incidence is changing from viral to non-viral. It has been shown that high cholesterol levels can cause the further transformation of non-alcoholic fatty liver disease (NAFLD) to HCC, but some investigations have found that serum cholesterol levels are negatively correlated with morbidity and mortality. Conflicting experimental results and epidemiological investigations illustrate the complex mechanisms of HCC. Cholesterol is essential for the survival of the body and tumors, although research on the function of cholesterol in tumors is evolving, the use of lowering cholesterol drugs in treating HCC remains limited. In this review, the cholesterol-involved mechanisms that cause the development of HCC or reduce the mortality and the latest progress in the use of cholesterol in the treatment of HCC and prospects for prevention and diagnosis have been summarized. • Cholesterol is an important component of the cell membranes, and plays an important role in HCC. • Cholesterol affects liver metabolism, gut microbiome homeostasis and immune function in HCC. • Regulation of cholesterol levels has great potential for the treatment of HCC, especially in immunotherapy. [ABSTRACT FROM AUTHOR]

This study aims to develop a non-intrusive reduced-order aerodynamic modeling framework for agile motion across a broad operating range in low-Reynolds-number incompressible flows. Since most previous reduced-order models are only valid in the neighborhood of the operating point, large amounts of high-fidelity input-output data have to be generated to cover the interested operating range, which requires expensive computational resources. To tackle such an issue, a signal interpolation approach by combining the discrete empirical interpolation method (DEIM) with Kriging technique is proposed to approximate the aerodynamic responses to a prescribed linear ramp-step maneuver with remarkable accuracy, instead of performing computational fluid dynamics (CFD) simulation at each operating point. The interpolated input-output data can be used to establish low-dimensional state-space aerodynamic models, by identifying the stability derivatives first and subsequently capturing the remaining transient dynamics via the eigensystem realization algorithm (ERA). To demonstrate the proposed framework, unsteady lift coefficients of a two-dimensional flat plate pitching about the leading edge are investigated over the range with Reynolds numbers 100∼500 and base angles of attack 0∼10 deg. Numerical results show good agreement between the reduced-order models and high-fidelity CFD simulations. [ABSTRACT FROM AUTHOR]

• Augmentation of deep learning with parallel magnetic resonance imaging results in accurate and stable image reconstruction. • Image quality evaluation on a large dataset showed consistent improvement. • Domain knowledge can reduce artificial features in the reconstructed MR images. • Augmentation of DL reconstruction with PMRI avoids modifications to the pulse sequences. A deep learning (DL) method for accelerated magnetic resonance (MR) imaging is presented that incorporates domain knowledge of parallel MR imaging to augment the DL networks for accurate and stable image reconstruction. The proposed DL method employs a novel loss function consisting of a combination of mean absolute error, structural similarity, and sobel edge loss. The DL model takes both original measurements and images reconstructed by the parallel imaging method as inputs to the network. The accuracy of the proposed method was evaluated using two anatomical regions and six MRI contrasts and was compared with state-of-the-art parallel imaging and deep learning methods. The proposed method significantly outperformed the other methods for all the six different contrasts in terms of structural similarity, peak signal to noise ratio, and normalized mean squared error. The out-of-sample performance of the proposed method was assessed for a truly "unseen" case in a volunteer scan. The method produced images without any artificial features, often occurring in the DL image reconstruction methods. A stability analysis was performed by adding perturbations to the input, which demonstrated that the proposed method is robust and stable with respect to small structural changes, and different undersampling ratios. Comprehensive validation on large datasets demonstrated that incorporation of domain knowledge sufficiently regularizes the DL based image reconstruction and produces accurate and stable image enhancement. [ABSTRACT FROM AUTHOR]

• For simultaneous PET-MRI systems, we propose a framework for joint image reconstruction from PET and accelerated-MRI data. • We propose a joint PET-MRI patch-based dictionary prior modeled as a Markov random field (MRF) for Bayesian reconstruction. • We use expectation-maximization (EM) to solve the Bayesian optimization problem. • We demonstrate results on carefully simulated BrainWeb-based data and in-vivo PET-MRI data. • Results from our framework outperform the state of the art, both qualitatively and quantitatively. For simultaneous positron-emission-tomography and magnetic-resonance-imaging (PET-MRI) systems, while early methods relied on independently reconstructing PET and MRI images, recent works have demonstrated improvement in image reconstructions of both PET and MRI using joint reconstruction methods. The current state-of-the-art joint reconstruction priors rely on fine-scale PET-MRI dependencies through the image gradients at corresponding spatial locations in the PET and MRI images. In the general context of image restoration, compared to gradient-based models, patch-based models (e.g., sparse dictionaries) have demonstrated better performance by modeling image texture better. Thus, we propose a novel joint PET-MRI patch-based dictionary prior that learns inter-modality higher-order dependencies together with intra-modality textural patterns in the images. We model the joint-dictionary prior as a Markov random field and propose a novel Bayesian framework for joint reconstruction of PET and accelerated-MRI images, using expectation maximization for inference. We evaluate all methods on simulated brain datasets as well as on in vivo datasets. We compare our joint dictionary prior with the recently proposed joint priors based on image gradients, as well as independently applied patch-based priors. Our method demonstrates qualitative and quantitative improvement over the state of the art in both PET and MRI reconstructions. [ABSTRACT FROM AUTHOR]

The formation of emerging nitrogenous disinfection by-products (N-DBPs) from the chlorination of aspartic acid (Asp) was investigated. The yield of dichloroacetonitrile (DCAN) was higher than other N-DBPs, such as dichloroacetamide(DCAcAm) and chloropicrin (TCNM) during the chlorination of Asp. The formation of DCAN, DCAcAm, and TCNM all showed a trend of first increasing and then decreasing during the chlorination of Asp with increasing contact time. The dosage of chlorine had an impact on the formation of DCAN, DCAcAm, and TCNM. The highest yields of DCAN and DCAcAm appeared when the Cl 2 /Asp molar ratio was about 20, the yield of TCNM increased with increasing the Cl 2 /Asp molar ratio from 5 to 30 and TCNM was not produced when the ratio was less than 5. Cyanogen chloride (CNCl) was detected when the Cl 2 /Asp molar ratio was lower than 5. N-DBPs formation was influenced by pH. DCAN formation increased with increasing pH from 5 to 6 and then decreased with increasing pH from 6 to 9, but DCAcAm and TCNM increased with increasing pH from 5 to 8 and then decreased. Higher temperatures reduced the formation of DCAN and DCAcAm, but increased TCNM formation. DCAN and DCAcAm formation decreased, and relatively stable TCNM formation increased, with increasing free chlorine contact time during chloramination. N -nitrosodimethylamine (NDMA) was produced during chloramination of Asp and increased with prolonged chloramination contact time. The presence of bromide ions enhanced the yields of haloacetonitriles and shifted N-DBPs to more brominated species. [ABSTRACT FROM AUTHOR]