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Abstract Two-dimensional semiconductors have shown great potential for the development of advanced intelligent optoelectronic systems. Among them, two-dimensional perovskite oxides with compelling optoelectronic performance have been thriving in high-performance photodetection. However, harsh synthesis and defect chemistry severely limit their overall performance and further large-scale heterogeneous integration. Here, we report the wafer-scale integration of highly oriented nanosheets by introducing a charge-assisted oriented assembly film-formation process and confirm its universality and scalability. The shallow-trap dominance induced by structural optimization endows the device with a distinguished performance balance, including high photosensitivity close to that of single nanosheet units and fast response speed. An integrated ultra-flexible 256-pixel device demonstrates the versatility of material-to-substrate integration and conformal imaging functionality. Moreover, the device achieves efficient recognition of multidirectional motion trajectories with an accuracy of over 99.8%. Our work provides prescient insights into the large-area fabrication and utilization of 2D perovskite oxides in advanced optoelectronics.

Abstract Objectives To investigate the implications of frailty as a predictive factor for outcomes among patients with oral and maxillofacial space infection. Methods A retrospective cohort study was conducted to analyze 348 medical records, gathering data on several key aspects. These included the etiology of infection, the location of inflamed areas, the treatment administered, and the ultimate treatment outcomes. Additionally, the study collected information on the Symptom Severity (SS) score, frailty score, age, gender, the presence of systemic diseases, alcohol consumption, and smoking history. Results A total of 155 patients were classified as frailty, while 193 patients were classified as non-frailty. We found a significantly different in age, BMI, hospitalization expenses, length of hospital stay, SS, fibrinogen and admission to ICU between the frail group and the non- frail group. Conclusions Frailty serves as a valuable predictor of outcomes among patients with oral and maxillofacial space infections. By identifying high-risk patients, frailty can be employed as a clinical tool to guide perioperative care, ultimately optimizing patient outcomes. Notably, frail patients often require more ICU treatment.

Abstract Background Preserved Ratio Impaired Spirometry (PRISm) is considered to be a precursor of chronic obstructive pulmonary disease. Radiomics nomogram can effectively identify the PRISm subjects from non-COPD subjects, especially when during large-scale CT lung cancer screening. Methods Totally 1481 participants (864, 370 and 247 in training, internal validation, and external validation cohorts, respectively) were included. Whole lung on thin-section computed tomography (CT) was segmented with a fully automated segmentation algorithm. PyRadiomics was adopted for extracting radiomics features. Clinical features were also obtained. Moreover, Spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking and least absolute shrinkage and selection operator (LASSO) classifier were adopted to analyze whether radiomics features could be used to build radiomics signatures. A nomogram that incorporated clinical features and radiomics signature was constructed through multivariable logistic regression. Last, calibration, discrimination and clinical usefulness were analyzed using validation cohorts. Results The radiomics signature, which included 14 stable features, was related to PRISm of training and validation cohorts (p

Abstract Strict requirement of a coherent spectrum in coherent diffractive imaging (CDI) architectures poses a significant obstacle to achieving efficient photon utilization across the full spectrum. To date, nearly all broadband computational imaging experiments have relied on accurate spectroscopic measurements, as broad spectra are incompatible with conventional CDI systems. This paper presents an advanced approach to broaden the scope of CDI to ultra-broadband illumination with unknown probe spectrum, effectively addresses the key challenges encountered by existing state-of-the-art broadband diffractive imaging frameworks. This advancement eliminates the necessity for prior knowledge of probe spectrum and relaxes constraints on non-dispersive samples, resulting in a significant extension in spectral bandwidth, achieving a nearly fourfold improvement in bandlimit compared to the existing benchmark. Our method not only monochromatizes a broadband diffraction pattern from unknown illumination spectrum, but also determines the compressive sampled profile of spectrum of the diffracted radiation. This superiority is experimentally validated using both CDI and ptychography techniques on an ultra-broadband supercontinuum with relative bandwidth exceeding 40%, revealing a significantly enhanced coherence and improved reconstruction with high fidelity under ultra-broadband illumination.

The matrix protein 2 (M2) is a preferred target for developing a universal vaccine against the influenza A virus (IAV). This study aimed to develop a method for assessing antibody-dependent cell-mediated cytotoxicity (ADCC) associated with M2-based immunization in mice. We first established a stable cell line derived from mouse lymphoma cells (YAC-1) expressing M2 of H3N2. This cell line, designated as YAC-1-M2, was generated using a second-generation lentiviral tricistronic plasmid system to transduce the M2 gene into YAC-1 cells. The ADCC effect induced by polyclonal antibodies targeting matrix protein 2 ectodomain (M2e) was demonstrated by YAC-1-M2 cell lysis by natural killer cells (NK) derived from mice, in the presence of anti-M2 antibodies obtained from mice immunized with an mRNA vaccine based on M2e. This ADCC effect was found to be stronger compared to the effect induced by monoclonal antibodies (14C2) against M2. Moreover, the ADCC effect was enhanced as the effector-to-target ratio of NK to YAC-1-M2 cells increased. In conclusion, we established a novel method to detect ADCC of M2 of IAV, which paves the way for the development of an M2-based universal vaccine against IAV and an in-depth analysis of its mechanism of broad-spectrum immune protection in mice.

Abstract The intriguing biomineralization process in nature endows the mineralized biological materials with intricate microarchitected structures in a facile and orderly way, which provides an inspiration for processing ceramics. Here, we propose a simple and efficient manufacturing process to fabricate cellular ceramics in programmed cell-based 3D configurations, inspired by the biomineralization process of the diatom frustule. Our approach separates the ingredient synthesis from architecture building, enabling the programmable manufacturing of cellular ceramics with various cell sizes, geometries, densities, metastructures, and constituent elements. Our approach exploits surface tension to capture precursor solutions in the architected cellular lattices, allowing us to control the liquid geometry and manufacture cellular ceramics with high precision. We investigate the geometry parameters for the architected lattices assembled by unit cells and unit columns, both theoretically and experimentally, to guide the 3D fluid interface creation in arranged configurations. We manufacture a series of globally cellular and locally compact piezoceramics, obtaining an enhanced piezoelectric constant and a designed piezoelectric anisotropy. This bioinspired, surface tension-assisted approach has the potential to revolutionize the design and processing of multifarious ceramic materials for structural and functional applications in energy, electronics and biomedicine.

Purpose: To establish a radiomics nomogram based on MRI radiomics features combined with clinical characteristics for distinguishing pleomorphic adenoma (PA) from warthin tumor (WT). Methods: 294 patients with PA (n = 159) and WT (n = 135) confirmed by histopathology were included in this study between July 2017 and June 2023. Clinical factors including clinical data and MRI features were analyzed to establish clinical model. 10 MRI radiomics features were extracted and selected from T1WI and FS-T2WI, used to establish radiomics model and calculate radiomics scores (Rad-scores). Clinical factors and Rad-scores were combined to serve as crucial parameters for combined model. Through Receiver operator characteristics (ROC) curve and decision curve analysis (DCA), the discriminative values of the three models were qualified and compared, the best-performing combined model was visualized in the form of a radiomics nomogram. Results: The combined model demonstrated excellent discriminative performance for PA and WT in the training set (AUC=0.998) and testing set (AUC=0.993) and performed better compared with the clinical model and radiomics model in the training set (AUC=0.996, 0.952) and testing model (AUC=0.954, 0.849). The DCA showed that the combined model provided more overall clinical usefulness in distinguishing parotid PA from WT than another two models. Conclusion: An analytical radiomics nomogram based on MRI radiomics features, incorporating clinical factors, can effectively distinguish between PA and WT.

ObjectivesThe purpose of this study was to develop and validate a new feature fusion algorithm to improve the classification performance of benign and malignant ground-glass nodules (GGNs) based on deep learning.MethodsWe retrospectively collected 385 cases of GGNs confirmed by surgical pathology from three hospitals. We utilized 239 GGNs from Hospital 1 as the training and internal validation set, and 115 and 31 GGNs from Hospital 2 and Hospital 3, respectively, as external test sets 1 and 2. Among these GGNs, 172 were benign and 203 were malignant. First, we evaluated clinical and morphological features of GGNs at baseline chest CT and simultaneously extracted whole-lung radiomics features. Then, deep convolutional neural networks (CNNs) and backpropagation neural networks (BPNNs) were applied to extract deep features from whole-lung CT images, clinical, morphological features, and whole-lung radiomics features separately. Finally, we integrated these four types of deep features using an attention mechanism. Multiple metrics were employed to evaluate the predictive performance of the model.ResultsThe deep learning model integrating clinical, morphological, radiomics and whole lung CT image features with attention mechanism (CMRI-AM) achieved the best performance, with area under the curve (AUC) values of 0.941 (95% CI: 0.898-0.972), 0.861 (95% CI: 0.823-0.882), and 0.906 (95% CI: 0.878-0.932) on the internal validation set, external test set 1, and external test set 2, respectively. The AUC differences between the CMRI-AM model and other feature combination models were statistically significant in all three groups (all p

Abstract Background Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. Conclusions The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.

A series of triazolopyridine-based dual JAK/HDAC inhibitors were rationally designed and synthesised by merging different pharmacophores into one molecule. All triazolopyridine derivatives exhibited potent inhibitory activities against both targets and the best compound 4-(((5-(benzo[d][1, 3]dioxol-5-yl)-[1, 2, 4]triazolo[1, 5-a]pyridin-2-yl)amino)methyl)-N-hydroxybenzamide (19) was dug out. 19 was proved to be a pan-HDAC and JAK1/2 dual inhibitor and displayed high cytotoxicity against two cancer cell lines MDA-MB-231 and RPMI-8226 with IC50 values in submicromolar range. Docking simulation revealed that 19 fitted well into the active sites of HDAC and JAK proteins. Moreover, 19 exhibited better metabolic stability in vitro than SAHA. Our study demonstrated that compound 19 was a promising candidate for further preclinical studies.

Purpose: It is vital to develop noninvasive approaches with high accuracy to discriminate the preserved ratio impaired spirometry (PRISm) group from the chronic obstructive pulmonary disease (COPD) groups. Radiomics has emerged as an image analysis technique. This study aims to develop and confirm the new radiomics-based noninvasive approach to discriminate these two groups. Methods: Totally 1066 subjects from 4 centers were included in this retrospective research, and classified into training, internal validation or external validation sets. The chest computed tomography (CT) images were segmented by the fully automated deep learning segmentation algorithm (Unet231) for radiomics feature extraction. We established the radiomics signature (Rad-score) using the least absolute shrinkage and selection operator algorithm, then conducted ten-fold cross-validation using the training set. Last, we constructed a radiomics signature by incorporating independent risk factors using the multivariate logistic regression model. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA). Results: The Rad-score, including 15 radiomic features in whole-lung region, which was suitable for diffuse lung diseases, was demonstrated to be effective for discriminating between PRISm and COPD. Its diagnostic accuracy was improved through integrating Rad-score with a clinical model, and the area under the ROC (AUC) were 0.82(95 %CI 0.79–0.86), 0.77(95 %CI 0.72–0.83) and 0.841(95 %CI 0.78–0.91) for training, internal validation and external validation sets, respectively. As revealed by analysis, radiomics nomogram showed good fit and superior clinical utility. Conclusions: The present work constructed the new radiomics-based nomogram and verified its reliability for discriminating between PRISm and COPD.

Rationale and objectives: To compare the performance of SS, FOCUS SS, MUSE, and FOCUS MUSE DWI for pulmonary lesions to obtain a better technique for pulmonary DWI imaging. Materials and methods: 44 patients with pulmonary lesions were recruited to perform pulmonary DWI using SS, FOCUS SS, MUSE, and FOCUS MUSE sequences. Then, two radiologists with 12 and 10 years of chest MRI experiences assessed the overall image quality while another two radiologists both with 3 years of experiences evaluated the SNR, DR, and ADC of pulmonary lesions. Using interclass correlation coefficient (ICC) and kappa statistics to assess consistency of readers, Friedman test and Dunn-Bonferroni post hoc were used to calculate the difference between sequences. Mann-Whitney test and ROC curve were used to distinguish malignant from benign lesions. Results: All the assessed variables of the four sequences presented good to excellent intra-/inter-observer consistency. Compared with SS, FOCUS SS and MUSE, FOCUS MUSE demonstrated better image quality, including significantly higher 5-point Likert scale score (P 0.05) while MUSE presented with significantly higher SNR over them (P

Japanese encephalitis (JE) vaccination is the most effective way to prevent JE. Plaque reduction neutralization test (PRNT) as the standard method for potency testing for inactivated JE vaccine could not provide the exact potency value. Envelope (E) protein of JE virus induces the body to create neutralizing antibodies. There is a potential for using the determination of E protein to assess the immunogenicity and efficacy of JE vaccine. In this study, an automatic time-resolved fluoroimmunoassay for detection of E protein in JE vaccine was established as a simple and rapid in vitro potency assay to complement PRNT, including the expression and paired screening of monoclonal antibodies, the establishment of assay method and performance verification. A pair of anti-E protein neutralizing antibodies (L022 and L034) were screened to construct the sandwich detection pattern. After pre-treating the vaccine sample, the entire analysis was performed using a fully automated machine, which had a little detection time and eliminated manual error. The results of the validation experiment met the requirements for quality control. The linear range was from 0.78125 U/mL to 25 U/mL, the sensitivity was 0.01 U/mL, the intra-assay coefficient of variation was less than 5 %, and the inter-assay coefficient of variation was less than 10 %. The recovery from the dilution was between 90 % and 110 %. This present TRFIA shown good stability and effectiveness in quality control for samples related to JE vaccine production. The outcomes demonstrated that the present TRFIA could be an alternative in vitro potency assay in quality control for inactivated JE vaccine.

Abstract Miniaturizing spectrometers for compact and cost-effective mobile platforms is a major challenge in current spectroscopy research, where conventional spectrometers are impractical due to their bulky footprint. Existing miniaturized designs primarily rely on precalibrated response functions of nanophotonic structures to encode spectral information captured in a snapshot by detector arrays. Accurate spectrum reconstruction is achieved through computational techniques, but this requires precise component design, high-precision fabrication, and calibration. We propose an ultra-simplified computational spectrometer that employs a one-to-broadband diffraction decomposition strategy facilitated by a numerical regularized transform that depends only on the spectrum of the diffracted radiation. The key feature of our design is the use of a simple, arbitrarily shaped pinhole as the partial disperser, eliminating the need for complex encoding designs and full spectrum calibration. Our spectrometer achieves a reconstructed spectral peak location accuracy of better than 1 nm over a 200 nm bandwidth and excellent resolution for peaks separated by 3 nm in a bimodal spectrum, all within a compact footprint of under half an inch. Notably, our approach also reveals a breakthrough in broadband coherent diffractive imaging without requiring any prior knowledge of the broadband illumination spectrum, assumptions of non-dispersive specimens, or correction for detector quantum efficiency.

Abstract Hydrogen-isotope storage materials are essential for the controlled nuclear fusion. However, the currently used smelting-ZrCo alloy suffers from rapid degradation of performance due to severe disproportionation. Here, we reveal a defect-derived disproportionation mechanism and report a nano-single-crystal strategy to solve ZrCo’s problems. Single-crystal nano-ZrCo is synthesized by a wet-chemistry method and exhibits excellent comprehensive hydrogen-isotope storage performances, including ultrafast uptake/release kinetics, high anti-disproportionation ability, and stable cycling, far superior to conventional smelting-ZrCo. Especially, a further incorporation of Ti into nano-ZrCo can almost suppress the disproportionation reaction. Moreover, a mathematical relationship between dehydrogenation temperature and ZrCo particle size is established. Additionally, a microwave method capable of nondestructively detecting the hydrogen storage state of ZrCo is developed. The proposed disproportionation mechanism and anti-disproportionation strategy will be instructive for other materials with similar problems.

Pattern selection is crucial for optimizing the calibration process of optical proximity correction (OPC) models in computational lithography. However, it remains a challenge to achieve a balance between representative coverage and computational efficiency. This work presents a comprehensive evaluation of the feature vectors’ (FVs’) effectiveness in pattern selection for OPC model calibration, leveraging key performance indicators (KPIs) based on Kullback–Leibler divergence and distance ranking. Through the construction of autoencoder-based FVs and fast Fourier transform (FFT)-based FVs, we compare their efficacy in capturing critical pattern features. Validation experimental results indicate that autoencoder-based FVs, particularly augmented with the lithography domain knowledge, outperform FFT-based counterparts in identifying anomalies and enhancing lithography model performance. These results also underscore the importance of adaptive pattern representation methods in calibrating the OPC model with evolving complexities.

Abstract Metal–organic frameworks (MOFs) are promising candidates for room‐temperature hydrogen storage materials after modification, thanks to their ability to chemisorb hydrogen. However, the hydrogen adsorption strength of these modified MOFs remains insufficient to meet the capacity and safety requirements of hydrogen storage systems. To address this challenge, a highly defective framework material known as de‐MgMOF is prepared by gently annealing Mg‐MOF‐74. This material retains some of the crystal properties of the original Mg‐MOF‐74 and exhibits exceptional hydrogen storage capacity at above‐ambient temperatures. The MgO5 knots around linker vacancies in de‐MgMOF can adsorb a significant amount of dissociated and nondissociated hydrogen, with adsorption enthalpies ranging from −22.7 to −43.6 kJ mol−1, indicating a strong chemisorption interaction. By leveraging a spillover catalyst of Pt, the material achieves a reversible hydrogen storage capacity of 2.55 wt.% at 160 °C and 81 bar. Additionally, this material offers rapid hydrogen uptake/release, stable cycling, and convenient storage capabilities. A comprehensive techno‐economic analysis demonstrates that this material outperforms many other hydrogen storage materials at the system level for on‐board applications.

Strain engineering has been extensively explored to modulate the various intrinsic properties of flexible inorganic semiconductor films. However, experimental characterization of tensile and compressive strain-induced modulation of optoelectronic properties and their differences has not been easily implemented in flexible inorganic semiconductor films. Herein, the strain-dependent structural, optical, and optoelectronic properties of flexible ZnO films under pre-tensile and pre-compressive strains are systemically investigated by a Mueller matrix ellipsometry-based quantitative characterization method combined with x-ray diffraction and first-principle calculation. With extended prestress-driven deposition processing under bi-direction bending modes, pre-tensile and pre-compressive strains with symmetric magnitudes can be achieved in flexible ZnO films, which allows precise observation of the strain-driven asymmetric modulation of optoelectronic properties. When the applied prestrain varies approximately equally from 0% (baseline) to −0.99% (compression) and 1.07% (tensility), respectively, the relative changes for the c-axis lattice constant are 0.0133 and 0.0104 Å, respectively. Meanwhile, the dependence factors of the bandgap energy on the pre-compression and pre-tensile strains were determined as −0.0099 and −0.0156 eV/%, respectively, and the complex refractive index also presents an asymmetric varying trend. With the help of the strain–stress analysis and the first-principle calculation, the intriguing asymmetric strain-optical modulation effect could be attributed to the biaxial strain mechanism and the difference in the deformation potential between the two prestrain modes. These systematic investigation consequences are thus promising as a basis for the booming applications of the flexible inorganic semiconductor ensemble.

Purpose: Spinal multiple myeloma (MM) and metastases are two common cancer types with similar imaging characteristics, for which differential diagnosis is needed to ensure precision therapy. The aim of this study is to establish radiomics models for effective differentiation between them. Methods: Enrolled in this study were 263 patients from two medical institutions, including 127 with spinal MM and 136 with spinal metastases. Of them, 210 patients from institution I were used as the internal training cohort and 53 patients from Institution II were used as the external validation cohort. Contrast-enhanced T1-weighted imaging (CET1) and T2-weighted imaging (T2WI) sequences were collected and reviewed. Based on the 1037 radiomics features extracted from both CET1 and T2WI images, Logistic Regression (LR), AdaBoost (AB), Support Vector Machines (SVM), Random Forest (RF), and multiple kernel learning based SVM (MKL-SVM) were constructed. Hyper-parameters were tuned by five-fold cross-validation. The diagnostic efficiency among different radiomics models was compared by accuracy (ACC), sensitivity (SEN), specificity (SPE), area under the ROC curve (AUC), YI, positive predictive value (PPV), negative predictive value (NPY), and F1-score. Results: Based on single-sequence, the RF model outperformed all other models. All models based on T2WI images performed better than those based on CET1. The efficiency of all models was boosted by incorporating CET1 and T2WI sequences, and the MKL-SVM model achieved the best performance with ACC, AUC, and F1-score of 0.862, 0.870, and 0.874, respectively. Conclusions: The radiomics models constructed based on MRI achieved satisfactory diagnostic performance for differentiation of spinal MM and metastases, demonstrating broad application prospects for individualized diagnosis and treatment.

By applying ultrasonic treatment to ZL205A aluminum alloy melt containing different Cd contents and then using a tensile test, a scanning electron microscope experiment, and a metallographic experiment, the influence of ultrasonic treatment to ZL205A aluminum alloy melt on the properties of aluminum–copper alloy with different Cd contents in the liquid phase high-temperature region was studied. The effect of ultrasonic treatment on the solidification structure of ZL205A aluminum alloy was investigated. The results showed that there was no significant difference in the form of Cd and the solid solution rate of the aluminum alloy with different Cd contents in ultrasonic-assisted casting. However, the effect of ultrasonic waves on the high-temperature liquid phase of the melt of ZL205A aluminum alloy can effectively reduce the porosity defects in the solidification structure of the casting, refine the solidification structure, and make the structure more uniform. The ultrasonic cavitation effect can promote the formation and uniform distribution of Al–3Ti and improve the nucleation rate, and the effect can continue after heat treatment. This can solve the problem that increasing Cd content in ZL205A greatly reduces the toughness of castings while increasing the strength. The sample with a Cd content of 0.20% and with ultrasonic treatment has the highest strength, the average tensile strength is 475.5 MPa, and the yield strength is 381.75 MPa. However, compared with the conventional casting samples with the same Cd content, the decrease in elongation was significantly reduced.

Abstract Background In recent years, the role of altered cellular metabolism in tumor progression has attracted widespread attention. Related metabolic enzymes have also been considered as potential cancer therapeutic targets. Serine hydroxymethyltransferase 2 (SHMT2) has been reported to be upregulated in several cancers and associated with poor prognosis. However, there are few studies of SHMT2 in esophageal cancer (EC), and the related functions and mechanisms also need to be further explored. Methods In this study, we first analyzed SHMT2 expression in EC by online database and clinical samples. Then, the biological functions of SHMT2 in EC were investigated by cell and animal experiments. The intracellular m6A methylation modification levels were also evaluated by MeRIP. Linked genes and mechanisms of SHMT2 were analyzed by bioinformatics and rescue experiments. Results We found that SHMT2 expression was abnormally upregulated in EC and associated with poor prognosis. Functionally, SHMT2 silencing suppressed c-myc expression in an m6A-dependent manner, thereby blocking the proliferation, migration, invasion and immune escape abilities of EC cells. Mechanistically, SHMT2 encouraged the accumulation of methyl donor SAM through a one-carbon metabolic network, thereby regulating the m6A modification and stability of c-myc mRNA in a METTL3/FTO/ALKBH5/IGF2BP2-dependent way. In vivo animal experiments also demonstrated that SHMT2 mediated MYC expression by m6A-methylation modification, thus boosting EC tumorigenesis. Conclusion In conclusion, our data illustrated that SHMT2 regulated malignant progression and immune escape of EC cell through c-myc m6A modification. These revealed mechanisms related to SHMT2 in EC and maybe offer promise for the development of new therapeutic approaches.

PURPOSETo investigate the value of clinical characteristics and radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC).METHODSA total of 336 patients with NSCLC from our hospital were randomly divided into two groups, i.e., the training cohort (n = 236) and the internal validation cohort (n = 100) (7:3 ratio). Furthermore, 69 patients from two other hospitals were collected as the external validation cohort. Eight clinical patient characteristics were recorded, and 20 tumor radiological features were quantitatively measured and qualitatively analyzed. In the training cohort, the differences in clinical characteristics and radiological features were compared using univariate and multivariate analysis. A nomogram was created, and the predictive efficacy of the model was evaluated in the validation cohorts. The receiver operating characteristic curve and area under the curve (AUC) value were used to evaluate the discriminative ability of the model. In addition, the Hosmer–Lemeshow test and calibration curve were used to evaluate the goodness-of-fit of the model, and the decision curve was used to analyze the model’s clinical application value.RESULTSThe best predictors included gender, the carcinoembryonic antigen (CEA), consolidation-to-tumor ratio (CTR), density type, and distal ribbon sign. Among these, the tumor density type [odds ratio (OR): 6.738] and distal ribbon sign (OR: 5.141) were independent risk factors for predicting the STAS status. Moreover, three different STAS prediction models were constructed, i.e., a clinical, radiological, and combined model. The clinical model comprised gender and the CEA, the radiological model included the CTR, density type, and distal ribbon sign, and the combined model comprised the above two models. A DeLong test results revealed that the combined model was superior to the clinical model in all three cohorts and superior to the radiological model in the external validation cohort; the cohort AUC values were 0.874, 0.822, and 0.810, respectively. The results also showed that the combined model had the highest diagnostic efficacy among the models. The Hosmer–Lemeshow test showed that the combined model showed a good fit in all three cohorts, and the calibration curve showed that the predicted probability value of the combined model was in good agreement with the actual STAS status. Finally, the decision curve showed that the combined model had a better clinical application value than the clinical and radiological models.CONCLUSIONThe nomogram created in this study, based on clinical characteristics and radiological features, has a high diagnostic efficiency for predicting the STAS status in patients with clinical stage IA NSCLC and may support the creation of personalized treatment strategies before surgery.

Abstract Nanoparticles, films, and patterns are three critical piezoelectric elements with widespread applications in sensing, actuations, catalysis and energy harvesting. High productivity and large-area fabrication of these functional elements is still a significant challenge, let alone the control of their structures and feature sizes on various substrates. Here, we report a fast and versatile electrostatic disc microprinting, enabled by triggering the instability of liquid-air interface of inks. The printing process allows for fabricating lead zirconate titanate free-standing nanoparticles, films, and micro-patterns. The as-fabricated lead zirconate titanate films exhibit a high piezoelectric strain constant of 560 pm V−1, one to two times higher than the state-of-the-art. The multiplexed tip jetting mode and the large layer-by-layer depositing area can translate into depositing speeds up to 109 μm3 s−1, one order of magnitude faster than current techniques. Printing diversified functional materials, ranging from suspensions of dielectric ceramic and metal nanoparticles, to insulating polymers, to solutions of biological molecules, demonstrates the great potential of the electrostatic disc microprinting in electronics, biotechnology and beyond.

PURPOSETo assess the quantification accuracy of pulmonary nodules using virtual monoenergetic images (VMIs) derived from spectral-detector computed tomography (CT) under an ultra-low-dose scan protocol.METHODSA chest phantom consisting of 12 pulmonary nodules was scanned using spectral-detector CT at 100 kVp/10 mAs, 100 kVp/20 mAs, 120 kVp/10 mAs, and 120 kVp/30 mAs. Each scanning protocol was repeated three times. Each CT scan was reconstructed utilizing filtered back projection, hybrid iterative reconstruction, iterative model reconstruction (IMR), and VMIs of 40–100 keV. The signal-to-noise ratio and air noise of images, absolute differences, and absolute percentage measurement errors (APEs) of the diameter, density, and volume of the four scan protocols and ten reconstruction images were compared.RESULTSWith each fixed reconstruction image, the four scanning protocols exhibited no significant differences in APEs for diameter and density (all P > 0.05). Of the four scan protocols and ten reconstruction images, APEs for nodule volume had no significant differences (all P > 0.05). At 100 kVp/10 mAs, APEs for density using IMR were the lowest (APE-mean: 6.69), but no significant difference was detected between VMIs at 50 keV (APE-mean: 11.69) and IMR (P = 0.666). In the subgroup analysis, at 100 kVp/10 mAs, there were no significant differences between VMIs at 50 keV and IMR in diameter and density (all P > 0.05). The radiation dose at 100 kVp/10 mAs was reduced by 77.8% compared with that at 120 kVp/30 mAs.CONCLUSIONCompared with IMR, reconstruction at 100 kVp/10 mAs and 50 keV provides a more accurate quantification of pulmonary nodules, and the radiation dose is reduced by 77.8% compared with that at 120 kVp/30 mAs, demonstrating great potential for ultra-low-dose spectral-detector CT.

Seed vigor significantly affects peanut breeding and agricultural yield by influencing seed germination and seedling growth and development. Traditional vigor testing methods are inadequate for modern high-throughput assays. Although hyperspectral technology shows potential for monitoring various crop traits, its application in predicting peanut seed vigor is still limited. This study developed and validated a method that combines hyperspectral technology with genome-wide association studies (GWAS) to achieve high-throughput detection of seed vigor and identify related functional genes. Hyperspectral phenotyping data and physiological indices from different peanut seed populations were used as input data to construct models using machine learning regression algorithms to accurately monitor changes in vigor. Model-predicted phenotypic data from 191 peanut varieties were used in GWAS, gene-based association studies, and haplotype analyses to screen for functional genes. Real-time fluorescence quantitative PCR (qPCR) was used to analyze the expression of functional genes in three high-vigor and three low-vigor germplasms. The results indicated that the random forest and support vector machine models provided effective phenotypic data. We identified Arahy.VMLN7L and Arahy.7XWF6F, with Arahy.VMLN7L negatively regulating seed vigor and Arahy.7XWF6F positively regulating it, suggesting distinct regulatory mechanisms. This study confirms that GWAS based on hyperspectral phenotyping reveals genetic relationships in seed vigor levels, offering novel insights and directions for future peanut breeding, accelerating genetic improvements, and boosting agricultural yields. This approach can be extended to monitor and explore germplasms and other key variables in various crops.

GaN on Si plays an important role in the integration and promotion of GaN-based wide-gap materials with Si-based integrated circuits (IC) technology. A series of GaN film materials were grown on Si (111) substrate using a unique plasma assistant molecular beam epitaxy (PA-MBE) technology and investigated using multiple characterization techniques of Nomarski microscopy (NM), high-resolution X-ray diffraction (HR-XRD), variable angular spectroscopic ellipsometry (VASE), Raman scattering, photoluminescence (PL), and synchrotron radiation (SR) near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. NM confirmed crack-free wurtzite (w-) GaN thin films in a large range of 180–1500 nm. XRD identified the w- single crystalline structure for these GaN films with the orientation along the c-axis in the normal growth direction. An optimized 700 °C growth temperature, plus other corresponding parameters, was obtained for the PA-MBE growth of GaN on Si, exhibiting strong PL emission, narrow/strong Raman phonon modes, XRD w-GaN peaks, and high crystalline perfection. VASE studies identified this set of MBE-grown GaN/Si as having very low Urbach energy of about 18 meV. UV (325 nm)-excited Raman spectra of GaN/Si samples exhibited the GaN E2(low) and E2(high) phonon modes clearly without Raman features from the Si substrate, overcoming the difficulties from visible (532 nm) Raman measurements with strong Si Raman features overwhelming the GaN signals. The combined UV excitation Raman–PL spectra revealed multiple LO phonons spread over the GaN fundamental band edge emission PL band due to the outgoing resonance effect. Calculation of the UV Raman spectra determined the carrier concentrations with excellent values. Angular-dependent NEXAFS on Ga K-edge revealed the significant anisotropy of the conduction band of w-GaN and identified the NEXAFS resonances corresponding to different final states in the hexagonal GaN films on Si. Comparative GaN material properties are investigated in depth.

Abstract Objectives Although postoperative radiotherapy (PORT) could reduce the incidence of local recurrence in patients with IIIA-N2 non-small cell lung cancer (NSCLC), the role of PORT on survival in patients with surgically treated stage IIIA-N2 NSCLC remains controversial. Therefore, this study was designed to evaluate the effect of PORT on survival for patients with surgically treated stage IIIA-N2 NSCLC. Materials and methods This study population was chosen from the Surveillance, Epidemiology, and End Results database. The Cox proportional hazards regression analysis was used to determine significant contributors to overall survival (OS) and cancer special survival (CSS) outcomes. To balance baseline characteristics between the non-PORT group and PORT group, propensity score matching (PSM) with 1:1 propensity nearest-neighbor match by 0.001 matching tolerance was conducted by R software. Furthermore, a Kaplan–Meier curve was used to visualize the OS and CSS between the PORT group and non-PORT group survival probability. Results Of all evaluated cases, 4511 with IIIA-N2 NSCLC were eligible for inclusion, of which 1920 were enrolled into the PORT group. On univariate analysis and multivariate analysis, sex, age, year of diagnosis, race, histologic type, T stage, PORT, use of chemotherapy, and positive regional nodes were significantly associated with OS and CSS in IIIA-N2 NSCLC (P

Abstract Piezoelectric biomaterials have attracted great attention owing to the recent recognition of the impact of piezoelectricity on biological systems and their potential applications in implantable sensors, actuators, and energy harvesters. However, their practical use is hindered by the weak piezoelectric effect caused by the random polarization of biomaterials and the challenges of large-scale alignment of domains. Here, we present an active self-assembly strategy to tailor piezoelectric biomaterial thin films. The nanoconfinement-induced homogeneous nucleation overcomes the interfacial dependency and allows the electric field applied in-situ to align crystal grains across the entire film. The β-glycine films exhibit an enhanced piezoelectric strain coefficient of 11.2 pm V−1 and an exceptional piezoelectric voltage coefficient of 252 × 10−3 Vm N−1. Of particular significance is that the nanoconfinement effect greatly improves the thermostability before melting (192 °C). This finding offers a generally applicable strategy for constructing high-performance large-sized piezoelectric bio-organic materials for biological and medical microdevices.

Highlights Freestanding lead-free piezoceramic thin films can be derived via water peeling. The d 33 value of the freestanding BCZT thin film reaches 209 pm V−1. The freestanding piezoceramic films obtain versatile application scenarios such as energy harvest and virus detection.

Anthropogenetic environmental deterioration and climate change caused by energy production and consumption pose a significant threat to the future of humanity. Renewable, environmentally friendly, and cost-effective energy sources are becoming increasingly important for addressing future energy demands. Mechanical power is the most common type of external energy that can be converted into useful electric power. Because of its strong electromechanical coupling ability, the piezoelectric mechanism is a far more successful technique for converting mechanics energy to electrical energy when compared to electrostatic, electromagnetic, and triboelectric transduction systems. Currently, the scientific community has maintained a strong interest in piezoelectric micro-power generators because of their great potential for powering a sensor unit in the distributed network nodes. A national network usually has a large mass of sensor units distributed in each city, and a self-powered sensor network is eagerly required. This paper presents a comprehensive review of the development of piezoelectric micro-power generators. The fundamentals of piezoelectric energy conversion, including operational modes and working mechanisms, are introduced. Current research progress in piezoelectric materials including zinc oxide, ceramics, single crystals, organics, composite, bio-inspired and foam materials are reviewed. Piezoelectric energy harvesting at the nano- and microscales, and its applications in a variety of fields such as wind, liquid flow, body movement, implantable and sensing devices are discussed. Finally, the future development of multi-field coupled, hybrid piezoelectric micropower generators and their potential applications are discussed.

PURPOSEPreoperative prediction of visceral pleural invasion (VPI) is important because it enables thoracic surgeons to choose appropriate surgical plans. This study aimed to develop and validate a multivariate logistic regression model incorporating the maximum standardized uptake value (SUVmax) and valuable computed tomography (CT) signs for the non-invasive prediction of VPI status in subpleural clinical stage IA lung adenocarcinoma patients before surgery.METHODSA total of 140 patients with subpleural clinical stage IA peripheral lung adenocarcinoma were recruited and divided into a training set (n = 98) and a validation set (n = 42), according to the positron emission tomography/CT examination temporal sequence, with a 7:3 ratio. Next, VPI-positive and VPI-negative groups were formed based on the pathological results. In the training set, the clinical information, the SUVmax, the relationship between the tumor and the pleura, and the CT features were analyzed using univariate analysis. The variables with significant differences were included in the multivariate analysis to construct a prediction model. A nomogram based on multivariate analysis was developed, and its predictive performance was verified in the validation set.RESULTSThe size of the solid component, the consolidation-to-tumor ratio, the solid component pleural contact length, the SUVmax, the density type, the pleural indentation, the spiculation, and the vascular convergence sign demonstrated significant differences between VPI-positive (n = 40) and VPI-negative (n = 58) cases on univariate analysis in the training set. A multivariate logistic regression model incorporated the SUVmax [odds ratio (OR): 1.753, P = 0.002], the solid component pleural contact length (OR: 1.101, P = 0.034), the pleural indentation (OR: 5.075, P = 0.041), and the vascular convergence sign (OR: 13.324, P = 0.025) as the best combination of predictors, which were all independent risk factors for VPI in the training group. The nomogram indicated promising discrimination, with an area under the curve value of 0.892 [95% confidence interval (CI), 0.813–0.946] in the training set and 0.885 (95% CI, 0.748–0.962) in the validation set. The calibration curve demonstrated that its predicted probabilities were in acceptable agreement with the actual probability. The decision curve analysis illustrated that the current nomogram would add more net benefit.CONCLUSIONThe nomogram integrating the SUVmax and the CT features could non-invasively predict VPI status before surgery in subpleural clinical stage IA lung adenocarcinoma patients.

Abstract Capillary‐enabled water energy harvesters (WEHs) are capable of generating direct‐current electricity continuously. However, active‐metal electrodes can introduce metal–air batteries in these WEHs. Given the nearly identical device structures and output characteristics of these two technologies, it is essential to distinguish between them. Herein, we present a systematic study of the water‐activated metal–air battery (WMB) through theoretical analyses and experimental verifications. We conclude the general formation rules of the WMB from a material and device‐structure perspective. Furthermore, we provide a comparative summary of various WEHs and WMBs for easy identification. We aim to improve the comprehension of metal–air batteries in the field of WEHs and assist in distinguishing between these technologies.

Protected grape cultivation develops rapidly because of huge economic benefits. However, adverse environmental conditions (insufficient sunlight, high temperature, etc.) in protected cultivation led to low-quality berries. This study aimed to evaluate the effects of berry thinning on the quality attributes of two table grape cultivars (Baoguang and Cuiguang) under linkage greenhouse conditions. Three treatments (L, light berry thinning; M, moderate berry thinning; H, heavy berry thinning) were compared with the control (C, no berry thinning). Berry thinning increased berry weight, total soluble solids (TSS), fructose, glucose, the ratio of TSS to titratable acidity (TA), anthocyanin contents, berry firmness, and mineral contents (Ca, Fe, Na, Mg). Conversely, TA and organic acid profiles were decreased by berry thinning. Cultivars showed significant effects on most of the berry quality parameters. The interaction of cultivars by berry-thinning treatments affected sugar and acid components, anthocyanin contents, and mineral elements.

In recent years, with the development of artificial intelligence, especially deep learning technology, researches on automatic detection of cerebrovascular diseases on medical images have made tremendous progress and these models are gradually entering into clinical practice. However, because of the complexity and flexibility of the deep learning algorithms, these researches have great variability on model building, validation process, performance description and results interpretation. The lack of a reliable, consistent, standardized design protocol has, to a certain extent, affected the progress of clinical translation and technology development of computer aided detection systems. After reviewing a large number of literatures and extensive discussion with domestic experts, this position paper put forward recommendations of standardized design on the key steps of deep learning-based automatic image detection models for cerebrovascular diseases. With further research and application expansion, this position paper would continue to be updated and gradually extended to evaluate the generalizability and clinical application efficacy of such tools.

ObjectiveTo develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics.MethodsThis retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 239 GGNs from Hospital 1 as the training and internal validation set; 115 and 31 GGNs from Hospital 2 and Hospital 3 as the external test sets 1 and 2, respectively. An additional 32 stable GGNs from Hospital 3 with more than five years of follow-up were used as the external test set 3. We evaluated clinical and morphological features of GGNs at baseline chest CT and extracted the whole-lung radiomics features simultaneously. Besides, baseline whole-lung CT image features are further assisted and extracted using the convolutional neural network. We used the back-propagation neural network to construct five prediction models based on different collocations of the features used for training. The area under the receiver operator characteristic curve (AUC) was used to compare the prediction performance among the five models. The Delong test was used to compare the differences in AUC between models pairwise.ResultsThe model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed best among the five models, and achieved the highest AUC in the internal validation set, external test set 1, and external test set 2, which were 0.886 (95% CI: 0.841-0.921), 0.830 (95%CI: 0.749-0.893) and 0.879 (95%CI: 0.712-0.968), respectively. In the above three sets, the differences in AUC between the CMRI model and other models were significant (all P < 0.05). Moreover, the accuracy of the CMRI model in the external test set 3 was 96.88%.ConclusionThe baseline whole-lung CT features were feasible to predict the benign and malignant of GGNs, which is helpful for more refined management of GGNs.

In integrated circuit (IC) manufacturing, fast, nondestructive, and precise detection of defects in patterned wafers, realized by bright-field microscopy, is one of the critical factors for ensuring the final performance and yields of chips. With the critical dimensions of IC nanostructures continuing to shrink, directly imaging or classifying deep-subwavelength defects by bright-field microscopy is challenging due to the well-known diffraction barrier, the weak scattering effect, and the faint correlation between the scattering cross-section and the defect morphology. Herein, we propose an optical far-field inspection method based on the form-birefringence scattering imaging of the defective nanostructure, which can identify and classify various defects without requiring optical super-resolution. The technique is built upon the principle of breaking the optical form birefringence of the original periodic nanostructures by the defect perturbation under the anisotropic illumination modes, such as the orthogonally polarized plane waves, then combined with the high-order difference of far-field images. We validated the feasibility and effectiveness of the proposed method in detecting deep subwavelength defects through rigid vector imaging modeling and optical detection experiments of various defective nanostructures based on polarization microscopy. On this basis, an intelligent classification algorithm for typical patterned defects based on a dual-channel AlexNet neural network has been proposed, stabilizing the classification accuracy of λ /16-sized defects with highly similar features at more than 90%. The strong classification capability of the two-channel network on typical patterned defects can be attributed to the high-order difference image and its transverse gradient being used as the network’s input, which highlights the polarization modulation difference between different patterned defects more significantly than conventional bright-field microscopy results. This work will provide a new but easy-to-operate method for detecting and classifying deep-subwavelength defects in patterned wafers or photomasks, which thus endows current online inspection equipment with more missions in advanced IC manufacturing.

The relative sunspot number is one of the major parameters for the study of long-term solar activity. The automatic calculation of the relative sunspot number is more stable and accurate as compared to manual methods. In this paper, we propose an algorithm that can detect sunspots, and divide them into groups to automatically calculate the relative sunspot number. Mathematical morphology was adopted to detect sunspots then group them. The data set used were the continuum images from SDO/HMI. The process was carried out on the overall HMI data available on the timespan from 2022 January to 2023 May with a time cadence of one day. The experimental results indicated that the method achieved high accuracy of 85.3%. It was well fitted with the international relative sunspot number provided by Solar Influences Data Analysis Center (CC = 0.91). We calculated the conversion factor K value of SDO/HMI for calculating the relative sunspots number ( K = 1.03).

Multi-level programmable photonic integrated circuits (PICs) and optical metasurfaces have gained widespread attention in many fields, such as neuromorphic photonics, optical communications, and quantum information. In this paper, we propose pixelated programmable Si _3 N _4 PICs with record-high 20-level intermediate states at 785 nm wavelength. Such flexibility in phase or amplitude modulation is achieved by a programmable Sb _2 S _3 matrix, the footprint of whose elements can be as small as 1.2 μ m, limited only by the optical diffraction limit of an in-house developed pulsed laser writing system. We believe our work lays the foundation for laser-writing ultra-high-level (20 levels and even more) programmable photonic systems and metasurfaces based on phase change materials, which could catalyze diverse applications such as programmable neuromorphic photonics, biosensing, optical computing, photonic quantum computing, and reconfigurable metasurfaces.

Optical anisotropy offers a heightened degree of flexibility in shaping optical properties and designing cutting-edge devices. Quasi-one-dimensional Ta2NiS5, with giant optical anisotropy, has been used in the development of new lasers and sensors. In this research endeavor, we successfully acquired the complete dielectric tensor of Ta2NiS5, utilizing the advanced technique of Mueller matrix spectroscopic ellipsometry, enabling a rigorous quantitative assessment of its optical anisotropy. The results indicate that Ta2NiS5 demonstrates giant birefringence and dichroism, with Δnmax = 1.54 and Δkmax = 1.80. This pursuit also delves into the fundamental underpinnings of this optical anisotropy, drawing upon a fusion of first-principles calculations and critical points analysis. The anisotropy of Ta2NiS5 arises from differences in optical transitions in different directions and is shown to be due to van Hove singularities without exciton effects. Its giant optical anisotropy is expected to be useful in the design of novel optical devices, and the revelation of the physical mechanism facilitates the modulation of its optical properties.

Peanut is an important oilseed crop around the world which provides vegetable oil, protein and vitamins for humans. Major latex-like proteins (MLPs) play important roles in plant growth and development, as well as responses to biotic and abiotic stresses. However, their biological function in peanut is still unclear. In this study, a genome-wide identification of MLP genes in cultivated peanut and two diploid ancestor species was analyzed to determine their molecular evolutionary characteristics and the expression profile under drought and waterlogging stress conditions. Firstly, a total of 135 MLP genes were identified from the genome of tetraploid peanut (Arachis hypogaea) and two diploid species Arachis. duranensis and Arachis. ipaensis. Then, phylogenetic analysis revealed that MLP proteins were divided into five different evolutionary groups. These genes were distributed unevenly at the ends of chromosomes 3, 5, 7, 8, 9 and 10 in three Arachis species. The evolution of MLP gene family in peanut was conserved and led by tandem and segmental duplication. The prediction analysis of cis-acting elements showed that the promoter region of peanut MLP genes contained different proportions of transcription factors, plant hormones-responsive elements and so on. The expression pattern analysis showed that they were differentially expressed under waterlogging and drought stress. These results of this study provide a foundation for further research on the function of the important MLP genes in peanut.

The development of a combination of chemo/photothermal therapy could overcome the limitations of single-modality therapy and enhance therapeutic efficacy. In this study, a pH/thermal dual-responsive multifunctional drug delivery system with dual-drug loading and enhanced chemo/photothermal therapy is developed based on polydopamine-coated mesoporous silica-gold nanorods (PDA-AuNRs@MSN). Nanoscale mesoporous silica-gold nanorods encapsulating doxorubicin (DOX) are designed as a core and then modified by polydopamine. The PDA shell not only conjugates with another anticancer bortezomib (Btz) to form pH-sensitive bond through boronic acid and catechol but also acts as a gatekeeper to control the release of doxorubicin and enhance the photothermal effect. Such a nanocarrier not only acts as a contrast agent for PA imaging but also serves as a therapeutic agent for enhanced chemo/photothermal therapy. The DOX and Btz could be released in an on-demand mode under near-infrared light irradiation and acid environment. The tumor size and location could be observed via PA imaging after intravenous injection into 4T1-bearing mice. Compared with AuNRs@MSN, PDA-AuNRs@MSN exhibit an increased near-infrared (NIR) absorption at 808 nm and an enhanced photothermal effect. The integrated D/B-PDA-AuNRs@MSN nanoparticles show higher cell apoptosis and enhanced tumor treatment efficacy in vitro and in vivo in comparison with single chemotherapy or photothermal therapy. Combined together, D/B-PDA-AuNRs@MSN show pH/thermal-responsive controlled-release and synergistic chemo/photothermal therapy for tumor.

Low temperature is one of the environmental factors that restrict the growth and geographical distribution of peach (Prunus persica L. Batsch). To explore the molecular mechanisms of peach brunches in response to cold, we analyzed the metabolomics and transcriptomics of ‘Donghe No.1’ (cold-tolerant, CT) and ‘21st Century’ (cold-sensitive, CS) treated by different temperatures (-5 to -30°C) for 12 h. Some cold-responsive metabolites (e.g., saccharides, phenolic acids and flavones) were identified with upregulation only in CT. Further, we identified 1991 cold tolerance associated genes in these samples and they were significantly enriched in the pathways of ‘galactose metabolism’, ‘phenylpropanoid biosynthesis’ and ‘flavonoids biosynthesis’. Weighted gene correlation network analysis showed that soluble sugar, flavone, and lignin biosynthetic associated genes might play a key role in the cold tolerance of peach. In addition, several key genes (e.g., COMT, CCR, CAD, PER and F3’H) were substantially expressed more in CT than CS under cold stress, indicating that they might be major factors during the adaptation of peach to low temperature. This study will not only improve our understanding towards the molecular mechanisms of peach trees under cold stress but also contribute to the screening and breeding program of peach in the future.

It is challenging to break the activity–stability trade-off in Fe–N–C fuel cell catalysts. Here, the authors show that interactions between iron atoms and clusters accelerate reaction kinetics and suppress demetalation to improve fuel cell stability.

Background: The effectiveness of lung cancer screening with low-dose computed tomography (LDCT) has been established. The current study evaluates the cost-effectiveness of lung cancer screening with LDCT in a general population in China. Methods: A previously validated micro-simulation model was used to simulate a cohort of men and women on a lifetime horizon in the presence and absence of LDCT screening. The modeling data were collected from numerous national and international sources. Simulated screening scenarios included different combinations of screening intervals and start and stop ages. Additional costs (valued in Chinese Yuan, CNY; 1 USD = 6.8976 CNY, 1 EUR = 7.8755 CNY in 2020), life-years gained (LYG) and mortality reduction due to screening were also determined. The costs and life-years were discounted by 3%. All results were scaled to 1,000 individuals. The average cost-effectiveness ratio (ACER) was calculated. A willingness-to-pay threshold of CNY 217.3k / LYG was considered. A healthcare system perspective was adopted. Results: Compared to no screening, lung cancer screening by LDCT in a general Chinese population yielded 21.0 – 36.7 LYG in men and 9.2 – 16.6 LYG in women across the scenarios. For men, biennial LDCT screening yielded an ACER of CNY 171.4k – 306.3k / LYG relative to no screening. Biennial screening performed between 55 and 75 years of age was optimal at the defined threshold; it resulted in CNY 174.6k / LYG and a lung cancer mortality reduction of 9.1%, and this scenario had a 75% probability of being cost-effective. For women, the ACER ranged from CNY 364.2k to 1193.3k / LYG. Conclusions: In China, lung cancer screening with LDCT in the general population including never smokers could be cost-effective for men with 75% probability, but not for women. The optimal strategy for men would be performing biennial screening between 55 and 75 years of age.

Leveraging their unique phase modulation characteristics, birefringent waveplates have been widely used in various optical systems. With the development of material science and manufacturing techniques, the polarization properties of waveplates have become increasingly complex and diverse. Among these properties, the field-of-view effect of the waveplate caused due to manufacturing defects or improper installation procedures is extremely difficult to calibrate and seriously affects the precision and accuracy of the relevant optical systems. In this paper, a calibration method that can compensate for the field-of-view effect of waveplates installed in the instrument is proposed. Moreover, to approve the fidelity of the proposed calibration method, a series of film thickness measurement experiments are carried out. The results show that under different installation conditions of the waveplates, the precision and accuracy of the film thickness measured with the proposed method significantly improved. This method can be expected to reduce the assembly difficulty of such optical systems, while also improving their accuracy and stability.

In order to meet the requirements of dynamic monitoring from a bird’s eye view for typical rapidly changing processes such as mechanical rotation and photoresist exposure reaction, we propose a vertical high-speed Mueller matrix ellipsometer that consists of a polarization state generator (PSG) based on the time-domain polarization modulation and a polarization state analyzer (PSA) based on division-of-amplitude polarization demodulation. The PSG is realized using two cascaded photoelastic modulators, while the PSA is realized using a six-channel Stokes polarimeter. On this basis, the polarization effect introduced by switching the optical-path layout of the instrument from the horizontal transmission to the vertical transmission is fully considered, which is caused by changing the incidence plane. An in situ calibration method based on the correct definition of the polarization modulation and demodulation reference plane has been proposed, enabling the precise calibration of the instrument by combining it with a time-domain light intensity fitting algorithm. The measurement experiments of SiO2 films and an air medium prove the accuracy and feasibility of the proposed calibration method. After the precise calibration, the instrument can exhibit excellent measurement performance in the range of incident angles from 45° to 90°, in which the measurement time resolution is maintained at the order of 10 μs, the measurement accuracy of Mueller matrix elements is better than 0.007, and the measurement precision is better than 0.005.

Fabricating geometrically complex piezoceramics in compact sintered bodies has been difficult to achieve. Here the authors demonstrate a gravity-driven sintering strategy where high-temperature viscous behavior of piezoceramic allows for forming of complex shaped sintered bodies.

Abstract Hydrogen storage technology is one of the main challenges that restrict hydrogen utilization. Although there has been great progress in the research of hydrogen storage materials, seldom can they fulfill the criteria for onboard applications. Here, we focus on some innovative materials with complicated nanostructures and distinctive interactions with hydrogen, which may provide promising prospects. Recent advances in nano‐sized hydrides, catalytic adsorbents, multilayered materials, amorphous metal hydrides, and porous materials with open metal sites are discussed. We analyze the current problems of these hydrogen storage materials and provide suggestions for future development.

PurposeMultiple myeloma (MM) and metastasis originated are the two common malignancy diseases in the spine. They usually show similar imaging patterns and are highly demanded to differentiate for precision diagnosis and treatment planning. The objective of this study is therefore to construct a novel deep-learning-based method for effective differentiation of two diseases, with the comparative study of traditional radiomics analysis.MethodsWe retrospectively enrolled a total of 217 patients with 269 lesions, who were diagnosed with spinal MM (79 cases, 81 lesions) or spinal metastases originated from lung cancer (138 cases, 188 lesions) confirmed by postoperative pathology. Magnetic resonance imaging (MRI) sequences of all patients were collected and reviewed. A novel deep learning model of the Multi-view Attention-Guided Network (MAGN) was constructed based on contrast-enhanced T1WI (CET1) sequences. The constructed model extracts features from three views (sagittal, coronal and axial) and fused them for a more comprehensive differentiation analysis, and the attention guidance strategy is adopted for improving the classification performance, and increasing the interpretability of the method. The diagnostic efficiency among MAGN, radiomics model and the radiologist assessment were compared by the area under the receiver operating characteristic curve (AUC).ResultsAblation studies were conducted to demonstrate the validity of multi-view fusion and attention guidance strategies: It has shown that the diagnostic model using multi-view fusion achieved higher diagnostic performance [ACC (0.79), AUC (0.77) and F1-score (0.67)] than those using single-view (sagittal, axial and coronal) images. Besides, MAGN incorporating attention guidance strategy further boosted performance as the ACC, AUC and F1-scores reached 0.81, 0.78 and 0.71, respectively. In addition, the MAGN outperforms the radiomics methods and radiologist assessment. The highest ACC, AUC and F1-score for the latter two methods were 0.71, 0.76 & 0.54, and 0.69, 0.71, & 0.65, respectively.ConclusionsThe proposed MAGN can achieve satisfactory performance in differentiating spinal MM between metastases originating from lung cancer, which also outperforms the radiomics method and radiologist assessment.