Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network.

Purpose: We aim to reduce image noise in high-resolution (HR) virtual monoenergetic images (VMIs) from photon-counting detector (PCD) CT scans by developing a prior knowledge-aware iterative denoising neural network (PKAID-Net) that efficiently exploits the unique noise characteristics of VMIs at different energy (keV) levels. Approach: PKAID-Net offers two major features: first, it utilizes a lower-noise VMI (e.g., 70 keV) as a prior input; second, it iteratively constructs a refined training dataset to improve the neural network's denoising performance. In each iteration, the denoised image from the previous module serves as an updated target image, which is included in the dataset for the subsequent training iteration. Our study includes 10 patient coronary CT angiography exams acquired on a clinical dual-source PCD-CT (NAEOTOM Alpha, Siemens Healthineers). The HR VMIs were reconstructed at 50, 70, and 100 keV, using a sharp vascular kernel (Bv68) and thin (0.6 mm) slice thickness (0.3 mm increment). PKAID-Net's performance was evaluated in terms of image noise, spatial detail preservation, and quantitative accuracy. Results: PKAID-Net achieved a noise reduction of 96% compared to filtered back projection and 65% relative to iterative reconstruction, all while preserving spatial and spectral fidelity and maintaining a natural noise texture. The iterative refinement of PCD-CT data during the training process substantially enhanced the robustness of deep learning-based denoising compared to the original method, which resulted in some spatial detail loss. Conclusions: The PKAID-Net provides substantial noise reduction while maintaining spatial and spectral fidelity of the HR VMIs from PCD-CT.

Nanostructured High Entropy Alloys as Structural and Functional Materials.

Since their introduction in 2004, high entropy alloys (HEAs) have attracted significant attention due to their exceptional mechanical and functional properties. Advances in our understanding of atomic-scale ordering and phase formation in HEAs have facilitated the development of fabrication techniques for synthesizing nanostructured HEAs. These materials hold immense potential for applications in various fields including automobile industries, aerospace engineering, microelectronics, and clean energy, where they serve as either structural or functional materials. In this comprehensive Review, we conduct an in-depth analysis of the mechanical and functional properties of nanostructured HEAs, with a particular emphasis on the roles of different nanostructures in modulating these properties. To begin, we explore the intrinsic and extrinsic factors that influence the formation and stability of nanostructures in HEAs. Subsequently, we delve into an examination of the mechanical and electrocatalytic properties exhibited by bulk or three-dimensional (3D) nanostructured HEAs, as well as nanosized HEAs in the form of zero-dimensional (0D) nanoparticles, one-dimensional (1D) nanowires, or two-dimensional (2D) nanosheets. Finally, we present an outlook on the current research landscape, highlighting the challenges and opportunities associated with nanostructure design and the understanding of structure-property relationships in nanostructured HEAs.

Structural Elucidation of Glycosaminoglycans in the Tissue of Flounder and Isolation of Chondroitin Sulfate C.

Glycosaminoglycans (GAGs) are valuable bioactive polysaccharides with promising biomedical and pharmaceutical applications. In this study, we analyzed GAGs using HPLC-MS/MS from the bone (B), muscle (M), skin (S), and viscera (V) of Scophthalmus maximus (SM), Paralichthysi (P), Limanda ferruginea (LF), Cleisthenes herzensteini (G), Platichthys bicoloratus (PB), Pleuronichthys cornutus (PC), and Cleisthenes herzensteini (CH). Unsaturated disaccharide products were obtained by enzymatic hydrolysis of the GAGs and subjected to compositional analysis of chondroitin sulfate (CS), heparin sulfate (HS), and hyaluronic acid (HA), including the sulfation degree of CS and HS, as well as the content of each GAG. The contents of GAGs in the tissues and the sulfation degree differed significantly among the fish. The bone of S. maximus contained more than 12 µg of CS per mg of dry tissue. Although the fish typically contained high levels of CSA (CS-4S), some fish bone tissue exhibited elevated levels of CSC (CS-6S). The HS content was found to range from 10-150 ug/g, primarily distributed in viscera, with a predominant non-sulfated structure (HS-0S). The structure of HA is well-defined without sulfation modification. These analytical results are independent of biological classification. We provide a high-throughput rapid detection method for tissue samples using HPLC-MS/MS to rapidly screen ideal sources of GAG. On this basis, four kinds of CS were prepared and purified from flounder bone, and their molecular weight was determined to be 23-28 kDa by HPGPC-MALLS, and the disaccharide component unit was dominated by CS-6S, which is a potential substitute for CSC derived from shark cartilage.

Effect of Histamine H2 Receptor Antagonists on All-Cause Mortality in Critically Ill Patients With Essential Hypertension: A Retrospective Cohort Study.

Previous studies found that histamine H2 receptor antagonists (H2RAs) had blood pressure lowering and cardioprotective effects, but the impact of H2RAs on the survival outcomes of critically ill patients with essential hypertension is still unclear. The aim of this study was to investigate the association of H2RAs exposure with all-cause mortality in patients with essential hypertension based on Medical Information Mart for Intensive Care III database. A total of 17,739 patients were included, involving 8482 H2RAs users and 9257 non-H2RAs users. Propensity score matching (PSM) was performed to improve balance between 2 groups that were exposed to H2RAs or not. Kaplan-Meier survival curves were used to compare the cumulative survival rates and multivariable Cox regression models were performed to evaluate the association between H2RAs exposure and all-cause mortality. After 1:1 PSM, 4416 pairs of patients were enrolled. The results revealed potentially significant association between H2RAs exposure and decreased 30-day, 90-day, and 1-year mortalities in multivariate analyses (HR = 0.783, 95% CI: 0.696-0.882 for 30-day; HR = 0.860, 95% CI: 0.778-0.950 for 90-day; and HR = 0.883, 95% CI: 0.811-0.961 for 1-year mortality, respectively). Covariate effect analyses showed that the use of H2RAs was more beneficial in essential hypertension patients with age ≥ 60, BMI ≥ 25 kg/m2, coronary arteriosclerosis, stroke, and acute kidney failure, respectively. In conclusion, H2RAs exposure was related to lower mortalities in critically ill patients with essential hypertension, which provided novel potential strategy for the use of H2RAs in essential hypertension patients.

Heavy-Atom Effect Regulating Room-Temperature Phosphorescence of Organic-Inorganic Zn-Based Halides for White-Light Emission.

Organic-inorganic metal halides (OIMHs) with room-temperature phosphorescence (RTP) properties have aroused great research enthusiasm as outstanding broadband white-light emitters. Current studies on OIMHs with white-light emission were achieved via self-trapped excitons (STEs), but the unclear mechanism of STE formation is not favorable for the design of materials. In this work, zero-dimensional OIMHs composed of organic 3,4,5-trimethoxybenzylamine (TBA) and zine halide were synthesized, which enhanced the ratio of the RTP emission to the fluorescence emission from the TBA ligand. The experimental and mechanistic analyses demonstrate that the manageable RTP is mainly caused by the heavy-atom effect. In particular, by adjusting the incorporation ratio of halogen, an obvious white-light emission with a chromaticity coordinate value of (0.31, 0.33) can be achieved. This work developed a method for regulating the RTP of OIMHs with the heavy-atom effect to realize white-light emission, providing a new idea for the design of white-light emission materials.

Improving Stenosis Assessment in Energy Integrating Detector CT via Learned Monoenergetic Imaging Capability.

Coronary CT angiography (cCTA) is a fast non-invasive imaging exam for coronary artery disease (CAD) but struggles with dense calcifications and stents due to blooming artifacts, potentially causing stenosis overestimation. Virtual monoenergetic images (VMIs) at higher keV (e.g., 100 keV) from photon counting detector (PCD) CT have shown promise in reducing blooming artifacts and improving lumen visibility through its simultaneous high-resolution and multi-energy imaging capability. However, most cCTA exams are performed with single-energy CT (SECT) using conventional energy-integrating detectors (EID). Generating VMIs through EID-CT requires advanced multi-energy CT (MECT) scanners and potentially sacrifices temporal resolution. Given these limitations, MECT cCTA exams are not commonly performed on EID-CT and VMIs are not routinely generated. To tackle this, we aim to enhance the multi-energy imaging capability of EID-CT through the utilization of a convolutional neural network to LEarn MONoenergetic imAging from VMIs at Different Energies (LEMONADE). The neural network was trained using ten patient cCTA exams acquired on a clinical PCD-CT (NAEOTOM Alpha, Siemens Healthineers), with 70 keV VMIs as input (which is nominally equivalent to the SECT from EID-CT scanned at 120 kV) and 100 keV VMIs as the target. Subsequently, we evaluated the performance of EID-CT equipped with LEMONADE on both phantom and patient cases (n=10) for stenosis assessment. Results indicated that LEMONADE accurately quantified stenosis in three phantoms, aligning closely with ground truth and demonstrating stenosis percentage area reductions of 13%, 8%, and 9%. In patient cases, it led to a 12.9% reduction in average diameter luminal stenosis when compared to the original SECT without LEMONADE. These outcomes highlight LEMONADE's capacity to enable multi-energy CT imaging, mitigate blooming artifacts, and improve stenosis assessment for the widely available EID-CT. This has a high potential impact as most cCTA exams are performed on EID-CT.

Evaluation of data uncertainty for deep-learning-based CT noise reduction using ensemble patient data and a virtual imaging trial framework.

Deep learning-based image reconstruction and noise reduction (DLIR) methods have been increasingly deployed in clinical CT. Accurate assessment of their data uncertainty properties is essential to understand the stability of DLIR in response to noise. In this work, we aim to evaluate the data uncertainty of a DLIR method using real patient data and a virtual imaging trial framework and compare it with filtered-backprojection (FBP) and iterative reconstruction (IR). The ensemble of noise realizations was generated by using a realistic projection domain noise insertion technique. The impact of varying dose levels and denoising strengths were investigated for a ResNet-based deep convolutional neural network (DCNN) model trained using patient images. On the uncertainty maps, DCNN shows more detailed structures than IR although its bias map has less structural dependency, which implies that DCNN is more sensitive to small changes in the input. Both visual examples and histogram analysis demonstrated that hotspots of uncertainty in DCNN may be associated with a higher chance of distortion from the truth than IR, but it may also correspond to a better detection performance for some of the small structures.

Learned high-resolution cardiac CT imaging from ultra-high-resolution PCD-CT.

Coronary computed tomography angiography (cCTA) is a widely used non-invasive diagnostic exam for patients with coronary artery disease (CAD). However, most clinical CT scanners are limited in spatial resolution from use of energy-integrating detectors (EIDs). Radiological evaluation of CAD is challenging, as coronary arteries are small (3-4 mm diameter) and calcifications within them are highly attenuating, leading to blooming artifacts. As such, this is a task well suited for high spatial resolution. Recently, photon-counting-detector (PCD) CT became commercially available, allowing for ultra-high resolution (UHR) data acquisition. However, PCD-CTs are costly, restricting widespread accessibility. To address this problem, we propose a super resolution convolutional neural network (CNN): ILUMENATE (Improved LUMEN visualization through Artificial super-resoluTion imagEs), creating a high resolution (HR) image simulating UHR PCD-CT. The network was trained and validated using patches extracted from 8 patients with a modified U-Net architecture. Training input and labels consisted of UHR PCD-CT images reconstructed with a smooth kernel degrading resolution (LR input) and sharp kernel (HR label). The network learned the resolution difference and was tested on 5 unseen LR patients. We evaluated network performance quantitatively and qualitatively through visual inspection, line profiles to assess spatial resolution improvements, ROIs for CT number stability and noise assessment, structural similarity index (SSIM), and percent diameter luminal stenosis. Overall, ILUMENATE improved images quantitatively and qualitatively, creating sharper edges more closely resembling reconstructed HR reference images, maintained stable CT numbers with less than 4% difference, reduced noise by 28%, maintained structural similarity (average SSIM = 0.70), and reduced percent diameter stenosis with respect to input images. ILUMENATE demonstrates potential impact for CAD patient management, improving the quality of LR CT images bringing them closer to UHR PCD-CT images.

Immune-enhancing activity of compound polysaccharide on the inactivated influenza vaccine.

Traditional Chinese medicine polysaccharides have numerous biological activities with broad applications in the biomedical industries. However, a clear understanding of the pharmacological activities of compound polysaccharides with multi-component structures remain challenging. This study aimed to investigate the immune boosting effect of compound polysaccharides on the influenza vaccine and assess the preliminary structure-activity relationship. The compound polysaccharide (CP) was isolated from the combined Chinese herbs lentinan, pachymaran and tremellan, and purified by gradient ethanol precipitation to obtain its subcomponents of CP-20, CP-40, CP-60, and CP-80 with decreasing molecular weights. These polysaccharides were mainly composed of glucans with different linkage patterns, including α-(1 â†’ 3)-glucan, α-(1 â†’ 4)-glucan and ß-(1 â†’ 6)-glucan. A significant improvement was observed in the survival of mice vaccinated with inactivated (IAV) vaccine and the isolated polysaccharides as adjuvants. A reduction in the pulmonary virus titer and weight loss were also observed. Moreover, CP-40 and CP-60, as well as the original CP, significantly enhanced the serum anti-IAV antibody titers and interleukin IL-2, IL-5, and IL-6 concentrations. These preliminary results indicate the immune boosting effect of the compound polysaccharides is highly relevant to the specific structural properties of the subcomponent, and CP-40 is worthy of further exploration as a glycan adjuvant for the IAV vaccine.

Predictive value of CD86 for the occurrence of sepsis (Sepsis-3) in patients with infection.

This prospective observational study explored the predictive value of CD86 in the early diagnosis of sepsis in the emergency department. The primary endpoint was the factors associated with a diagnosis of sepsis. The secondary endpoint was the factors associated with mortality among patients with sepsis. It enrolled inpatients with infection or high clinical suspicion of infection in the emergency department of a tertiary Hospital between September 2019 and June 2021. The patients were divided into the sepsis and non-sepsis groups according to the Sepsis-3 standard. The non-sepsis group included 56 patients, and the sepsis group included 65 patients (19 of whom ultimately died). The multivariable analysis showed that CD86% (odds ratio [OR] = 1.22, 95% confidence interval [CI]: 1.04-1.44, P = 0.015), platelet count (OR = 0.99, 95%CI: 0.986-0.997, P = 0.001), interleukin-10 (OR = 1.01, 95%CI: 1.004-1.025, P = 0.009), and procalcitonin (OR = 1.17, 95%CI: 1.01-1.37, P = 0.043) were independent risk factors for sepsis, while human leukocyte antigen (HLA%) (OR = 0.96, 05%CI: 0.935-0.995, P = 0.022), respiratory rate (OR = 1.16, 95%CI: 1.03-1.30, P = 0.014), and platelet count (OR = 1.01, 95%CI: 1.002-1.016, P = 0.016) were independent risk factors for death in patients with sepsis. The model for sepsis (CD86%, platelets, interleukin-10, and procalcitonin) and the model for death (HLA%, respiratory rate, and platelets) had an area under the curve (AUC) of 0.870 and 0.843, respectively. CD86% in the first 24 h after admission for acute infection was independently associated with the occurrence of sepsis in the emergency department.

Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution.

BACKGROUND: Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. PURPOSE: In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. METHODS: The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. RESULTS: The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP > DCNN-Weak > IR > DCNN-Medium > DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. CONCLUSIONS: A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.

The Ras GTPase-activating protein UvGap1 orchestrates conidiogenesis and pathogenesis in the rice false smut fungus Ustilaginoidea virens.

Ras GTPase-activating proteins (Ras GAPs) act as negative regulators for Ras proteins and are involved in various signalling processes that influence cellular functions. Here, the function of four Ras GAPs, UvGap1 to UvGap4, was identified and analysed in Ustilaginoidea virens, the causal agent of rice false smut disease. Disruption of UvGAP1 or UvGAP2 resulted in reduced mycelial growth and an increased percentage of larger or dumbbell-shaped conidia. Notably, the mutant ΔUvgap1 completely lost its pathogenicity. Compared to the wild-type strain, the mutants ΔUvgap1, ΔUvgap2 and ΔUvgap3 exhibited reduced tolerance to H2 O2 oxidative stress. In particular, the ΔUvgap1 mutant was barely able to grow on the H2 O2 plate, and UvGAP1 was found to influence the expression level of genes involved in reactive oxygen species synthesis and scavenging. The intracellular cAMP level in the ΔUvgap1 mutant was elevated, as UvGap1 plays an important role in maintaining the intracellular cAMP level by affecting the expression of phosphodiesterases, which are linked to cAMP degradation in U. virens. In a yeast two-hybrid assay, UvRas1 and UvRasGef (Ras guanyl nucleotide exchange factor) physically interacted with UvGap1. UvRas2 was identified as an interacting partner of UvGap1 through a bimolecular fluorescence complementation assay and affinity capture-mass spectrometry analysis. Taken together, these findings suggest that the UvGAP1-mediated Ras pathway is essential for the development and pathogenicity of U. virens.

Functionalized Linear Conjugated Polymer/TiO2 Heterojunctions for Significantly Enhancing Photocatalytic H2 Evolution.

Conjugated polymers (CPs) have attracted much attention in recent years due to their structural abundance and tunable energy bands. Compared with CP-based materials, the inorganic semiconductor TiO2 has the advantages of low cost, non-toxicity and high photocatalytic hydrogen production (PHP) performance. However, studies on polymeric-inorganic heterojunctions, composed of D-A type CPs and TiO2, for boosting the PHP efficiency are still rare. Herein, an elucidation that the photocatalytic hydrogen evolution activity can actually be improved by forming polymeric-inorganic heterojunctions TFl@TiO2, TS@TiO2 and TSO2@TiO2, facilely synthesized through efficient in situ direct C-H arylation polymerization, is given. The compatible energy levels between virgin TiO2 and polymeric semiconductors enable the resulting functionalized CP@TiO2 heterojunctions to exhibit a considerable photocatalytic hydrogen evolution rate (HER). Especially, the HER of TSO2@TiO2 heterojunction reaches up to 11,220 µmol g-1 h-1, approximately 5.47 and 1260 times higher than that of pristine TSO2 and TiO2 photocatalysts. The intrinsic merits of a donor-acceptor conjugated polymer and the interfacial interaction between CP and TiO2 account for the excellent PHP activity, facilitating the separation of photo-generated excitons. Considering the outstanding PHP behavior, our work discloses that the coupling of inorganic semiconductors and suitable D-A conjugated CPs would play significant roles in the photocatalysis community.

DNA G-Quadruplex Is a Transcriptional Control Device That Regulates Memory.

The conformational state of DNA fine-tunes the transcriptional rate and abundance of RNA. Here, we report that G-quadruplex DNA (G4-DNA) accumulates in neurons, in an experience-dependent manner, and that this is required for the transient silencing and activation of genes that are critically involved in learning and memory in male C57/BL6 mice. In addition, site-specific resolution of G4-DNA by dCas9-mediated deposition of the helicase DHX36 impairs fear extinction memory. Dynamic DNA structure states therefore represent a key molecular mechanism underlying memory consolidation.One-Sentence Summary: G4-DNA is a molecular switch that enables the temporal regulation of the gene expression underlying the formation of fear extinction memory.

Mathematical modeling of optimal coagulant dosage for tofu preparation using MgCl2.

To explore the association between the optimal coagulant for tofu and the components of soybeans,30 different kinds of soybeans were selected, and tested for their optimal coagulant MgCl2 content. The optimal amount of coagulant was taken as the dependent variable, and the soybean Composition were taken as independent variables for the correlation analysis. The results showed that there was a positive correlation between the optimal coagulant content and the content of histidine, 7S ß-conglycinin, B1aB1bB2B3B4 of 11 s glycincin, and α'-subunit of 7S ß-conglycinin, negative correlation with lysine. The regression formula is y = -1.186 + 3.457*B1aB1bB2B3B4 + 2.304*7S + 0.351*histidine - 0.084*lysine + 4.696*α', and the model is validated to be within 10 % of the error value and has a high degree of confidence. This study provides theoretical support for realizing the green production of traditional soybean products.

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects.

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Reversibly immortalization establishes a hair follicle stem cell line with hair follicle reconstruction ability.

Hair follicle stem cells (HFSCs) play critical roles in the periodic regeneration of hair follicles. HFSCs are also a good model for stem cell biology research. However, no stable mouse HFSC cell line has been reported, which restricts the research and application of HFSCs. We isolated HFSCs from mouse hair follicles and immortalized them by inducing a reversible SV40 large T antigen. Through monoclonal screening, we identified a reversibly immortalized cell line, immortalized HFSC (iHFSC2). RNA sequencing, fluorescence-activated cell sorting, western blotting and immunofluorescence experiments revealed that the expression patterns of iHFSC2 and HFSC were similar at the protein and mRNA levels. After that, iHFSC2s were passaged and morphologically monitored for up to 40 times to detect their long-term culture potential. The long-term cultured iHFSC2 could regenerate hair follicles with complete hair follicle structure and HFSCs in the bulge area. This work successfully established an HFSC cell line with the ability of hair follicle reconstruction.

Motion artifact correction in cardiac CT using cross-phase temporospatial information and synergistic attention gate and spatial transformer sub-networks.

Objectives.To improve quality of coronary CT angiography (CCTA) images using a generalizable motion-correction algorithm.Approach. A neural network with attention gate and spatial transformer (ATOM) was developed to correct coronary motion. Phantom and patient CCTA images (39 males, 32 females, age range 19-92, scan date 02/2020 to 10/2021) retrospectively collected from dual-source CT were used to create training, development, and testing sets corresponding to 140- and 75 ms temporal resolution, with 75 ms images as labels. To test generalizability, ATOM was deployed for locally adaptive motion-correction in both 140- and 75 ms patient images. Objective metrics were used to assess motion-corrupted and corrected phantom and patient images, including structural-similarity-index (SSIM), dice-similarity-coefficient (DSC), peak-signal-noise-ratio (PSNR), and normalized root-mean-square-error (NRMSE). In objective quality assessment, ATOM was compared with several baseline networks, including U-net, U-net plus attention gate, U-net plus spatial transformer, VDSR, and ResNet. Two cardiac radiologists independently interpreted motion-corrupted and -corrected images at 75 and 140 ms in a blinded fashion and ranked diagnostic image quality (worst to best: 1-4, no ties).Main results. ATOM improved quality metrics (p< 0.05) before/after correction: in phantom, SSIM 0.87/0.95, DSC 0.85/0.93, PSNR 19.4/22.5, NRMSE 0.38/0.27; in patient images, SSIM 0.82/0.88, DSC 0.88/0.90, PSNR 30.0/32.0, NRMSE 0.16/0.12. ATOM provided more consistent improvement of objective image quality, compared to the presented baseline networks. The motion-corrected images received better ranks than un-corrected at the same temporal resolution (p< 0.05): 140 ms images 1.65/2.25, and 75 ms images 3.1/3.2. The motion-corrected 75 ms images received the best rank in 65% of testing cases. A fair-to-good inter-reader agreement was observed (Kappa score 0.58).Significance. ATOM reduces motion artifacts, improving visualization of coronary arteries. This algorithm can be used to virtually improve temporal resolution in both single- and dual-source CT.

Switching from Thermally Activated Delayed Fluorescence in Single Crystals for Low-Threshold Laser to Room-temperature Phosphorescence in Amorphous-Film for Highly Efficient OLEDs.

Metal-organic phosphorescent complexes containing Ir or Pt are work horse in organic light-emitting diode (OLED) technology, which can harvest both singlet and triplet excitons in electroluminescence (EL) owing to strong heavy-atom effect. Recently, organic room-temperature phosphorescence (ORTP) have achieved high photoluminescence quantum yield (PLQY) in rigid crystalline state, which, however, is unsuitable for OLED fabrication, therefore leading to an EL efficiency far low behind those of metal-organic phosphorescent complexes. Here, we reported a luminescence mechanism switch from thermally activated delayed fluorescence (TADF) in single crystal microwires to ORTP in amorphous thin-films, based on a tert-butylcarbazole difluoroboron ß-diketonate derivative of DtCzBF2. Tightly packed and well-faceted single-crystal microwires exhibit aggregation induced emission (AIE), enabling TADF microlasers at 473 nm with an optical gain coefficient as high as 852 cm-1 . In contrast, loosely packed dimers of DtCzBF2 formed in guest-host amorphous thin-films decrease the oscillator strength of fluorescence transition but stabilize triplets for ORTP with a PLQY up to 61 %, leading to solution-processed OLEDs with EQE approaching 20 %. This study opens possibilities of low-cost ORTP emitters for high performance OLEDs and future low-threshold electrically injected organic semiconductor lasers (OSLs).