Dimethylamine Copper(I) Halide Single Crystals: Structure, Physical Properties, and Scintillation Performance.

Hybrid copper(I) halides have garnered a significant amount of attention as potential substitutes in luminescence and scintillation applications. Herein, we report the discovery and crystal growth of new zero-dimensional compounds, (C2H8N)3Cu2I5 and (C2H8N)4Cu2Br6. The bromide and iodide have a triclinic structure with space group P1̅ and an orthorhombic structure with space group Pnma, respectively. (C2H8N)3Cu2I5 exhibits cyan emission peaking at 504 nm with a photoluminescence quantum yield (PLQY) of 34.79%, while (C2H8N)4Cu2Br6 shows yellowish-green emission peaking at 537 nm with a PLQY of 38.45%. The temperature-dependent photoluminescence data of both compounds were fitted to theoretical models, revealing that nonradiative intermediate states significantly affect thermal quenching and antiquenching. Electron-phonon interactions, the origin of emission line width broadening and peak shifting, were also investigated via fittings. The scintillation properties of (C2H8N)3Cu2I5 were evaluated, and an X-ray imaging device was successfully fabricated using (C2H8N)3Cu2I5. This work demonstrates the potentiality of copper halides in lighting and X-ray imaging applications.

Reduction of ringing artifacts induced by diaphragm drifting in free-breathing dynamic pulmonary MRI using 3D koosh-ball acquisition.

PURPOSE: To reduce the ringing artifacts of the motion-resolved images in free-breathing dynamic pulmonary MRI. METHODS: A golden-step based interleaving (GSI) technique was proposed to reduce ringing artifacts induced by diaphragm drifting. The pulmonary MRI data were acquired using a superior-inferior navigated 3D radial UTE sequence in an interleaved manner during free breathing. Successive interleaves were acquired in an incoherent fashion along the polar direction. Four-dimensional images were reconstructed from the motion-resolved k-space data obtained by retrospectively binning. The reconstruction algorithms included standard nonuniform fast Fourier transform (NUFFT), Voronoi-density-compensated NUFFT, extra-dimensional UTE, and motion-state weighted motion-compensation reconstruction. The proposed interleaving technique was compared with a conventional sequential interleaving (SeqI) technique on a phantom and eight subjects. RESULTS: The quantified ringing artifacts level in the motion-resolved image is positively correlated with the quantified nonuniformity level of the corresponding k-space. The nonuniformity levels of the end-expiratory and end-inspiratory k-space binned from GSI data (0.34 ± 0.07, 0.33 ± 0.05) are significantly lower with statistical significance (p < 0.05) than that binned from SeqI data (0.44 ± 0.11, 0.42 ± 0.12). Ringing artifacts are substantially reduced in the dynamic images of eight subjects acquired using the proposed technique in comparison with that acquired using the conventional SeqI technique. CONCLUSION: Ringing artifacts in the motion-resolved images induced by diaphragm drifting can be reduced using the proposed GSI technique for free-breathing dynamic pulmonary MRI. This technique has the potential to reduce ringing artifacts in free-breathing liver and kidney MRI based on full-echo interleaved 3D radial acquisition.

Supervised Factor Analysis Transfer: Calibration transfer with noise modeling and response variable integration.

Multivariate calibration models often encounter challenges in extrapolating beyond the calibration instruments due to variations in hardware configurations, signal processing algorithms, or environmental conditions. Calibration transfer techniques have been developed to mitigate this issue. In this study, we introduce a novel methodology known as Supervised Factor Analysis Transfer (SFAT) aimed at achieving robust and interpretable calibration transfer. SFAT operates from a probabilistic framework and integrates response variables into its transfer process to effectively align data from the target instrument to that of the source instrument. Within the SFAT model, the data from the source instrument, the target instrument, and the response variables are collectively projected onto a shared set of latent variables. These latent variables serve as the conduit for information transfer between the three distinct domains, thereby facilitating effective spectra transfer. Moreover, SFAT explicitly models the noise variances associated with each variable, thereby minimizing the transfer of non-informative noise. Furthermore, we provide empirical evidence showcasing the efficacy of SFAT across three real-world datasets, demonstrating its superior performance in calibration transfer scenarios.

Whole brain multiparametric mapping in two minutes using a dual-flip-angle stack-of-stars blipped multi-gradient-echo acquisition.

A new MRI technique is presented for three-dimensional fast simultaneous whole brain mapping of myelin water fraction (MWF), T1, proton density (PD), R2*, magnetic susceptibility (QSM), and B1 transmit field (B1+). Phantom and human (N = 9) datasets were acquired using a dual-flip-angle blipped multi-gradient-echo (DFA-mGRE) sequence with a stack-of-stars (SOS) trajectory. Images were reconstructed using a subspace-based algorithm with a locally low-rank constraint. A novel joint-sparsity-constrained multicomponent T2*-T1 spectrum estimation (JMSE) algorithm is proposed to correct for the T1 saturation effect and B1+/B1- inhomogeneities in the quantification of MWF. A tissue-prior-based B1+ estimation algorithm was adapted for B1 correction in the mapping of T1 and PD. In the phantom study, measurements obtained at an acceleration factor (R) of 12 using prospectively under-sampled SOS showed good consistency (R2 > 0.997) with Cartesian reference for R2*/T1app/M0app. In the in vivo study, results of retrospectively under-sampled SOS with R = 6, 12, 18, showed good quality (structure similarity index measure > 0.95) compared with those of fully-sampled SOS. Besides, results of prospectively under-sampled SOS with R = 12 showed good consistency (intraclass correlation coefficient > 0.91) with Cartesian reference for T1/PD/B1+/MWF/QSM/R2*, and good reproducibility (coefficient of variation < 7.0 %) in the test-retest analysis for T1/PD/B1+/MWF/R2*. This study has demonstrated the feasibility of simultaneous whole brain multiparametric mapping with a two-minute scan using the DFA-mGRE SOS sequence, which may overcome a major obstacle for neurological applications of multiparametric MRI.

Scan-specific Unsupervised Highly Accelerated Non-Cartesian CEST Imaging using Implicit Neural Representation and Explicit Sparse Prior.

OBJECTIVE: Chemical exchange saturation transfer (CEST) is a promising magnetic resonance imaging (MRI) technique. CEST imaging usually requires a long scan time, and reducing acquisition time is highly desirable for clinical applications. METHODS: A novel scan-specific unsupervised deep learning algorithm is proposed to accelerate steady-state pulsed CEST imaging with golden-angle stack-of-stars trajectory using hybrid-feature hash encoding implicit neural representation. Additionally, imaging quality is further improved by using the explicit prior knowledge of low rank and weighted joint sparsity in the spatial and Z-spectral domain of CEST data. RESULTS: In the retrospective acceleration experiment, the proposed method outperforms other state-of-the-art algorithms (TDDIP, LRTES, kt-SLR, NeRP, CRNN, and PBCS) for the in vivo human brain dataset under various acceleration rates. In the prospective acceleration experiment, the proposed algorithm can still obtain results close to the fully-sampled images. CONCLUSION AND SIGNIFICANCE: The hybrid-feature hash encoding implicit neural representation combined with explicit sparse prior (INRESP) can efficiently accelerate CEST imaging. The proposed algorithm achieves reduced error and improved image quality compared to several state-of-the-art algorithms at relatively high acceleration factors. The superior performance and the training database-free characteristic make the proposed algorithm promising for accelerating CEST imaging in various applications.

Accelerated four-dimensional free-breathing whole-liver water-fat magnetic resonance imaging with deep dictionary learning and chemical shift modeling.

Background: Multi-echo chemical-shift-encoded magnetic resonance imaging (MRI) has been widely used for fat quantification and fat suppression in clinical liver examinations. Clinical liver water-fat imaging typically requires breath-hold acquisitions, with the free-breathing acquisition method being more desirable for patient comfort. However, the acquisition for free-breathing imaging could take up to several minutes. The purpose of this study is to accelerate four-dimensional free-breathing whole-liver water-fat MRI by jointly using high-dimensional deep dictionary learning and model-guided (MG) reconstruction. Methods: A high-dimensional model-guided deep dictionary learning (HMDDL) algorithm is proposed for the acceleration. The HMDDL combines the powers of the high-dimensional dictionary learning neural network (hdDLNN) and the chemical shift model. The neural network utilizes the prior information of the dynamic multi-echo data in spatial respiratory motion, and echo dimensions to exploit the features of images. The chemical shift model is used to guide the reconstruction of field maps, R2∗ maps, water images, and fat images. Data acquired from ten healthy subjects and ten subjects with clinically diagnosed nonalcoholic fatty liver disease (NAFLD) were selected for training. Data acquired from one healthy subject and two NAFLD subjects were selected for validation. Data acquired from five healthy subjects and five NAFLD subjects were selected for testing. A three-dimensional (3D) blipped golden-angle stack-of-stars multi-gradient-echo pulse sequence was designed to accelerate the data acquisition. The retrospectively undersampled data were used for training, and the prospectively undersampled data were used for testing. The performance of the HMDDL was evaluated in comparison with the compressed sensing-based water-fat separation (CS-WF) algorithm and a parallel non-Cartesian recurrent neural network (PNCRNN) algorithm. Results: Four-dimensional water-fat images with ten motion states for whole-liver are demonstrated at several R values. In comparison with the CS-WF and PNCRNN, the HMDDL improved the mean peak signal-to-noise ratio (PSNR) of images by 9.93 and 2.20 dB, respectively, and improved the mean structure similarity (SSIM) of images by 0.058 and 0.009, respectively, at R=10. The paired t-test shows that there was no significant difference between HMDDL and ground truth for proton-density fat fraction (PDFF) and R2∗ values at R up to 10. Conclusions: The proposed HMDDL enables features of water images and fat images from the highly undersampled multi-echo data along spatial, respiratory motion, and echo dimensions, to improve the performance of accelerated four-dimensional (4D) free-breathing water-fat imaging.

Learning-Assisted Fast Determination of Regularization Parameter in Constrained Image Reconstruction.

OBJECTIVE: To leverage machine learning (ML) for fast selection of optimal regularization parameter in constrained image reconstruction. METHODS: Constrained image reconstruction is often formulated as a regularization problem and selecting a good regularization parameter value is an essential step. We solved this problem using an ML-based approach by leveraging the finding that for a specific constrained reconstruction problem defined for a fixed class of image functions, the optimal regularization parameter value is weakly subject-dependent and the dependence can be captured using few experimental data. The proposed method has four key steps: a) solution of a given constrained reconstruction problem for a few (say, 3) pre-selected regularization parameter values, b) extraction of multiple approximated quality metrics from the initial reconstructions, c) predicting the true quality metrics values from the approximated values using pre-trained neural networks, and d) determination of the optimal regularization parameter by fusing the predicted quality metrics. RESULTS: The effectiveness of the proposed method was demonstrated in two constrained reconstruction problems. Compared with L-curve-based method, the proposed method determined the regularization parameters much faster and produced substantially improved reconstructions. Our method also outperformed state-of-the-art learning-based methods when trained with limited experimental data. CONCLUSION: This paper demonstrates the feasibility and improved reconstruction quality by using machine learning to determine the regularization parameter in constrained reconstruction. SIGNIFICANCE: The proposed method substantially reduces the computational burden of the traditional methods (e.g., L-curve) or relaxes the requirement of large training data by modern learning-based methods, thus enhancing the practical utility of constrained reconstruction.

Profiling of aberrant sialylated N-glycans in hepatocellular carcinoma by liquid chromatography mass spectrometry.

BACKGROUND: Aberrant sialylation is closely associated with the tumorigenesis, progression, and metastasis, and may be of importance for disease diagnosis. However, the analysis of altered expression of sialylated glycans (SGs) in blood is particularly challenging due to the low content and poor ionization efficiency of sialylated glycans in mass spectrometry. METHODS: An analytical strategy based on enrichment of SGs, liquid chromatography-high resolution mass spectrometric detection, and automatic glycan annotation was developed to profile the sialylated N-glycome in serum. The enrichment of sialylated glycans was accomplished using cationic cotton via electrostatic and hydrogen interaction. Using partial least squares-discriminant analysis (PLS-DA), the approach was applied for nontarget screening and profiling of aberrant sialylated N-glycans in hepatocellular carcinoma (HCC). RESULTS: 55 SGs were identified in human serum, and three important SGs (SG35, SG45, and SG46) were screened to have good diagnostic specificity for HCC. Their areas under the receiver operating characteristic (ROC) curve (AUC) were higher than α-fetoprotein (AFP)'s (AUC = 0.85), at 0.88, 0.87, and 0.91, respectively. When three SGs are combined, the diagnostic specificity for HCC may increase to 94 %. The fact that SGs biomarkers are sensitive to AFP-Negative HCC is very noteworthy. CONCLUSIONS: The method significantly advanced the search for sialylated glycan-based cancer biomarkers. In comparison to traditional indicators like AFP and imaging tools, SGs showed a higher diagnostic sensitivity for HCC.

OPEs-ID: A software for non-targeted screening of organophosphate esters based on liquid chromatography-high-resolution mass spectrometry.

Organophosphate esters (OPEs) are widely used as flame retardants and plasticizers, presenting a potential threat to the environment and human health. To date, no automatic software exists for the nontargeted screening of OPEs. In this study, OPEs-ID, a user-friendly software, was developed for the identification of OPEs using liquid chromatography-high-resolution mass spectrometry. The main workflow of OPEs-ID included fragments-dependent precursor ion screening, elemental composition determination, extracted ion chromatograms (EIC) comparison, and molecular structure identification via MetFrag strategy. A mixture of 17 OPE standards was identified with an identification rate of 100% by OPEs-ID. OPEs-ID demonstrated a rate of 94.1% for correctly ranking within the top 1 candidate in a local database (41.2% in PubChem) for the 17 OPE standards, which remarkably improved the identification when compared to conventional in silico fragmentation algorithms. Using a pooled airborne fine particle sample (PM2.5), OPEs-ID could automatically retrieve 22 valid molecules with structure candidates. The detection frequencies of 9 newly identified OPEs were between 13% and 100% in the 32 PM2.5 samples. Their semi-quantification concentrations were comparable to those of some traditional OPEs. Overall, OPEs-ID offers a powerful tool to significantly enrich our understanding of the OPEs present in the environment.

Subspace Model-Assisted Deep Learning for Improved Image Reconstruction.

Image reconstruction from limited and/or sparse data is known to be an ill-posed problem and a priori information/constraints have played an important role in solving the problem. Early constrained image reconstruction methods utilize image priors based on general image properties such as sparsity, low-rank structures, spatial support bound, etc. Recent deep learning-based reconstruction methods promise to produce even higher quality reconstructions by utilizing more specific image priors learned from training data. However, learning high-dimensional image priors requires huge amounts of training data that are currently not available in medical imaging applications. As a result, deep learning-based reconstructions often suffer from two known practical issues: a) sensitivity to data perturbations (e.g., changes in data sampling scheme), and b) limited generalization capability (e.g., biased reconstruction of lesions). This paper proposes a new method to address these issues. The proposed method synergistically integrates model-based and data-driven learning in three key components. The first component uses the linear vector space framework to capture global dependence of image features; the second exploits a deep network to learn the mapping from a linear vector space to a nonlinear manifold; the third is an unrolling-based deep network that captures local residual features with the aid of a sparsity model. The proposed method has been evaluated with magnetic resonance imaging data, demonstrating improved reconstruction in the presence of data perturbation and/or novel image features. The method may enhance the practical utility of deep learning-based image reconstruction.

SERS determination of sodium saccharin content on the tipping paper of cigarettes using AgNP substrates prepared with a USB-power supply device.

A novel magnetic agitating heater powered by a USB port has been developed and applied to synthesize silver colloid substrate for surface-enhanced Raman scattering (SERS) detection of sodium saccharin content on the tipping paper of cigarettes. The heater device allows the convenient synthesis of the Ag colloid, and the reaction can be completed on-site in 15 min under mild conditions. The on-site synthesis of SERS substrate effectively avoided the need for storage and concerns regarding the poor stability and short lifespan of colloid substrates. The results demonstrated that the substrate obtained with the device could achieve SERS detection of Rhodamine 6G (R6G) at as low as 10-9 mol L-1 while maintaining a stable intensity with a relative standard deviation (RSD) of 5.52% (n = 5). Using the prepared substrate at the optimal conditions, the limit of detection of sodium saccharin (SS) was 1 mg L-1. By introducing an internal standard KSCN, a linear relationship was observed between the relative intensity at 708 cm-1 and the concentration of the SS in a range of 20-100 mg L-1 (R2 = 0.98). With the developed method, SS was directly extracted from the cigarette paper by immersing it in water, and the extracted solution was subsequently detected. The quantitative spike-recoveries were in the range of 95.5-116.7%, with RSD between 2.3-12.6%. The whole detection procedure including the on-site substrate preparation, took only about 30 min. This work opens new avenues for colloidal synthesis, and the detection method of SS on the cigarette paper also holds great promise in food safety applications.

Fluoranthene determination based on a rapid and sensitive syringe extraction and solid-phase fluorescence technique.

A rapid and sensitive strategy was proposed for the detection of fluoranthene (FL), which is a polycyclic aromatic hydrocarbon (PAH), in water samples. In this work, syringe solid-phase extraction (SPE) combined with solid-phase fluorescence spectrometry was used to determine FL in PAHs polluted environmental samples. The fluorescence signals were directly monitored on the membrane surface after FL was enriched by syringe SPE. Under the optimal conditions, the proposed method showed a linear relationship in the concentration range 2-50 µg/L with a correlation coefficient (R2 ) of 0.998, and the limit of detection was 0.143 µg/L. The recoveries varied from 93.47% to 109.81% in the actual samples, with the relative standard deviations (n = 3) ranging from 2.06% to 6.32%. According to the results, the established method can be applied in the field of rapid detection as it is fast, simple, portable, and highly sensitive, and has strong anti-interference.

One-Dimensional Hybrid Copper(I) Iodide Single Crystal with Renewable Scintillation Properties.

Low-dimensional hybrid copper(I) halides attract considerable attention in the field of light emissions. In this work, we obtained the centimeter-sized single crystal of 1,3-propanediamine copper(I) iodide (PDACuI3) with a solvent evaporation method. The single crystal X-ray diffraction of PDACuI3 reveals that the [CuI4] tetrahedra form the corner-connected chains separated by PDAs, forming a one-dimensional structure with an orthorhombic space group of Pbca. The band gap is determined to be 4.03 eV, and the room temperature photoluminescence (PL) quantum yield is determined to be 26.5%. The thermal quenching and negative thermal quenching of emission are observed via temperature-dependent PL spectra, and our study shows that the intermediate nonradiative state below the self-trapped exciton state may get involved in these temperature-dependent behaviors. The X-ray scintillation performance of PDACuI3 single crystals is also evaluated, and the relative light output renewed to 94.3% of the fresh one after a low-temperature annealing. On the basis of our results, PDACuI3 single crystals provide nontoxicity and renewable scintillation performance, thus showing potential application in the area of low-cost radiation detectors.

Attaining competency and proficiency in minimally invasive mitral valve repair: a learning curve assessment using cumulative sum analysis.

OBJECTIVE: To evaluate the learning curve of minimally invasive mitral valvuloplasty (MVP). BACKGROUND: Minimally invasive MVP is characterized by minimal trauma, minimal bleeding, and short postoperative recovery time. The learning curve of any new procedure needs to be evaluated for learning and replication. However, minimally invasive mitral valve technique is a wide-ranging concept, no further analysis of the outcomes and learning curve of minimally invasive Mitral valvuloplasty has been performed. METHODS: One hundred and fifty consecutive patients who underwent minimally invasive MVP alone without concurrent surgery were evaluated. Using cardiopulmonary bypass (CPB) time and aortic clamping (AC) time as evaluation variables, we visualized the learning curve for minimally invasive MVP using cumulative sum analysis. We also analyzed important postoperative variables such as postoperative drainage, duration of mechanical ventilation, ICU stay and postoperative hospital stay. RESULTS: The slope of the fitted curve was negative after 75 procedures, and the learning curve could be crossed after the completion of the 75th procedure when AC and CPB time were used as evaluation variables. And as the number of surgical cases increased, CPB, AC, postoperative drainage, duration of mechanical ventilation, ICU stay and postoperative hospital stay all showed different degrees of decrease. The incidence of postoperative adverse events is similar to conventional Mitral valvuloplasty. CONCLUSION: Compared to conventional MVP, minimally invasive MVP provides the same satisfactory surgical results and stabilization can be achieved gradually after completion of the 75th procedure.

Parallel non-Cartesian spatial-temporal dictionary learning neural networks (stDLNN) for accelerating 4D-MRI.

Dynamic magnetic resonance imaging (MRI) acquisitions are relatively slow due to physical and physiological limitations. The spatial-temporal dictionary learning (DL) approach accelerates dynamic MRI by learning spatial-temporal correlations, but the regularization parameters need to be manually adjusted, the performance at high acceleration rate is limited, and the reconstruction can be time-consuming. Deep learning techniques have shown good performance in accelerating MRI due to the powerful representational capabilities of neural networks. In this work, we propose a parallel non-Cartesian spatial-temporal dictionary learning neural networks (stDLNN) framework that combines dictionary learning with deep learning algorithms and utilizes the spatial-temporal prior information of dynamic MRI data to achieve better reconstruction quality and efficiency. The coefficient estimation modules (CEM) are designed in the framework to adaptively adjust the regularization coefficients. Experimental results show that combining dictionary learning with deep neural networks and using spatial-temporal dictionaries can obviously improve the image quality and computational efficiency compared with the state-of-the-art non-Cartesian imaging methods for accelerating the 4D-MRI especially at high acceleration rate.

Low-Rank Tensor Subspace Decomposition With Weighted Group Sparsity for the Acceleration of Non-Cartesian Dynamic MRI.

OBJECTIVE: Dynamic MR imaging often requires long scan time, and acceleration of data acquisition is highly desirable in clinical applications. METHODS: We proposed a Low-rank Tensor subspace decomposition with Weighted Group Sparsity (LTWGS) algorithm for non-Cartesian dynamic MRI. The proposed algorithm introduces the weighted group sparse constraints together with the subspace decomposition technique into the framework of low-rank tensor and sparse decomposition to better utilize the sparsity in the data. RESULTS: LTWGS increases the PSNR values by 1.97 dB, 2.03 dB, and 2.83 dB compared with PROST (patch-based reconstruction), SRTPCA (smooth robust tensor principal component analysis), and LRTES (low-rank tensor with "explicit subspace") in the dynamic abdominal imaging at an acceleration rate R = 25. LTWGS increases the PSNR values by 2.42 dB and 3.57 dB compared with PROST and LRTES in DCE liver imaging at R = 25. LTWGS increases the PSNR values by 1.40 dB and 1.96 dB compared with PROST and SRTPCA in cardiac cine imaging at R = 25. CONCLUSION AND SIGNIFICANCE: Jointly using group sparsity and sparsity can obtain better results than that using group sparsity alone, and weighted regularization can achieve better results than that without weighted regularization. The proposed algorithm results in reduced reconstruction error and improved image structural similarity in comparison with several state-of-the-art methods at relatively high acceleration factors. The proposed algorithm has the potential in various dynamic MRI application scenarios.

Bioinformatics analysis and experimental verification of the prognostic and biological significance mediated by fatty acid metabolism related genes for hepatocellular carcinoma.

Background: Liver cancer is among the leading causes of death related to cancer around the world. The most frequent type of human liver cancer is hepatocellular carcinoma (HCC). Fatty acid (FA) metabolism is an emerging hallmark that plays a promoting role in numerous malignancies. This study aimed to discover a FA metabolism-related risk signature and formulate a better model for HCC patients' prognosis prediction. Methods: We collected mRNA expression data and clinical parameters of patients with HCC using the TCGA databases, and the differential FA metabolism-related genes were explored. To create a risk prognostic model, we carried out the consensus clustering as well as univariate and multivariate Cox regression analyses. 16 genes were used to establish a prognostic model, which was then validated in the ICGC dataset. The accuracy of the model was performed using receiver operating characteristic (ROC) analyses, decision curve analysis (DCA) and nomogram. The immune cell infiltration level of risk genes was evaluated with single-sample GSEA (ssGSEA) algorithm. To reflect the response to immunotherapy, immunophenoscore (IPS) was obtained from TCGA-LIHC. Then, the expression of the candidate risk genes (p < 0.05) was validated by qRT-PCR, Western blotting and single-cell transcriptomics. Cellular function assays were performed to revealed the biological function of HAVCR1. Results: According to the TCGA-LIHC cohort analysis, the majority of the FA metabolism-related genes were expressed differentially in the HCC and normal tissues. The prognosis of patients with high-risk scores was observed to be worse. Multivariate COX regression analysis confirmed that the model can be employed as an independent prognosis factor for HCC patients. Furthermore, ssGSEA analysis revealed a link between the model and the levels of immune cell infiltration. Our model scoring mechanism also provides a high predictive value in HCC patients receiving anti-PDL1 immunotherapy. One of the FA metabolism-related genes, HAVCR1, displays a significant differential expression between normal and HCC cell lines. Hepatocellular carcinoma cells (Huh7, and HepG2) proliferation, motility, and invasion were all remarkably inhibited by HAVCR1 siRNA. Conclusion: Our study identified a novel FA metabolism-related prognostic model, revealing a better potential treatment and prevention strategy for HCC.

Meta-QTL analysis explores the key genes, especially hormone related genes, involved in the regulation of grain water content and grain dehydration rate in maize.

BACKGROUND: Low grain water content (GWC) at harvest of maize (Zea mays L.) is essential for mechanical harvesting, transportation and storage. Grain drying rate (GDR) is a key determinant of GWC. Many quantitative trait locus (QTLs) related to GDR and GWC have been reported, however, the confidence interval (CI) of these QTLs are too large and few QTLs has been fine-mapped or even been cloned. Meta-QTL (MQTL) analysis is an effective method to integrate QTLs information in independent populations, which helps to understand the genetic structure of quantitative traits. RESULTS: In this study, MQTL analysis was performed using 282 QTLs from 25 experiments related GDR and GWC. Totally, 11 and 34 MQTLs were found to be associated with GDR and GWC, respectively. The average CI of GDR and GWC MQTLs was 24.44 and 22.13 cM which reduced the 57 and 65% compared to the average QTL interval for initial GDR and GWC QTL, respectively. Finally, 1494 and 5011 candidate genes related to GDR and GWC were identified in MQTL intervals, respectively. Among these genes, there are 48 genes related to hormone metabolism. CONCLUSIONS: Our studies combined traditional QTL analyses, genome-wide association study and RNA-seq to analysis major locus for regulating GWC in maize.

Accelerating multi-echo chemical shift encoded water-fat MRI using model-guided deep learning.

PURPOSE: To accelerate chemical shift encoded (CSE) water-fat imaging by applying a model-guided deep learning water-fat separation (MGDL-WF) framework to the undersampled k-space data. METHODS: A model-guided deep learning water-fat separation framework is proposed for the acceleration using Cartesian/radial undersampling data. The proposed MGDL-WF combines the power of CSE water-fat imaging model and data-driven deep learning by jointly using a multi-peak fat model and a modified residual U-net network. The model is used to guide the image reconstruction, and the network is used to capture the artifacts induced by the undersampling. A data consistency layer is used in MGDL-WF to ensure the output images to be consistent with the k-space measurements. A Gauss-Newton iteration algorithm is adapted for the gradient updating of the networks. RESULTS: Compared with the compressed sensing water-fat separation (CS-WF) algorithm/2-step procedure algorithm, the MGDL-WF increased peak signal-to-noise ratio (PSNR) by 5.31/5.23, 6.11/4.54, and 4.75 dB/1.88 dB with Cartesian sampling, and by 4.13/6.53, 2.90/4.68, and 1.68 dB/3.48 dB with radial sampling, at acceleration rates (R) of 4, 6, and 8, respectively. By using MGDL-WF, radial sampling increased the PSNR by 2.07 dB at R = 8, compared with Cartesian sampling. CONCLUSIONS: The proposed MGDL-WF enables exploiting features of the water images and fat images from the undersampled multi-echo data, leading to improved performance in the accelerated CSE water-fat imaging. By using MGDL-WF, radial sampling can further improve the image quality with comparable scan time in comparison with Cartesian sampling.

Temperature-Dependent Luminescence and Anisotropic Optical Properties of Centimeter-Sized One-Dimensional Perovskite Trimethylammonium Lead Iodide Single Crystals.

Low-dimensional hybrid halide perovskite materials with self-trapped exciton (STE) emissions and anisotropic properties are highly attractive for their great potential in many applications. However, to date, reports on large one-dimensional (1D) perovskite single crystals have been limited. Here, centimeter-sized 1D single crystals of trimethylammonium lead iodide (TMAPbI3) with typical STE emission are synthesized by an antisolvent vapor-assisted crystallization method. Thermal quenching and antiquenching with a high relative sensitivity of photoluminescence (PL) are observed and studied via temperature-dependent photoluminescence spectroscopy. Further analysis indicates that the temperature-dependent PL behaviors are influenced by the self-trapping of the free exciton and the migrations between self-trapped excitons and intermediate nonradiative states. The TMAPbI3 single crystal also exhibits a linearly polarized emission and a large birefringence that is higher than those of commercial birefringent crystals. This 1D perovskite with high structural anisotropy has promise for applications in versatile optical- and luminescence-related fields.