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Deep learning has catalyzed a transformative shift in material discovery, offering a key advantage over traditional experimental and theoretical methods by significantly reducing associated costs. Models adept at predicting properties from chemical compositions alone do not require structural information. However, this cost-efficient approach compromises model precision, particularly in Chemical Composition-based Property Prediction Models (CPMs), which are notably less accurate than Structure-based Property Prediction Models (SPMs). Addressing this challenge, our study introduces a novel Teacher-Student (TS) strategy, where a pretrained SPM serves as an instructive 'teacher' to enhance the CPM's precision. This TS strategy successfully harmonizes low-cost exploration with high accuracy, achieving a significant 47.1% reduction in relative error in scenarios involving 100 data entries. We also evaluate the effectiveness of the proposed strategy by employing perovskites as a case study. This method represents a significant advancement in the exploration and identification of valuable materials, leveraging CPM's potential while overcoming its precision limitations.

Flammability and poor toughness of unmodified PLA limit its applications in various fields. Though ammonium polyphosphate (APP) is a green and effective flame retardant, it has poor compatibility with the matrix, leading to a decrease in mechanical properties. Stereo-complexation greatly improves the strength and heat resistance of traditional PLA. However, the effect of flame retardants on the formation of stereo-complexed crystals and the impact of stereo-complexation on flame retardancy have not been studied previously. In this research, PDLA chains were first in-situ reacted with APP particles for improved interfacial compatibility. By utilizing the characteristic of PLA enantiomers that can form stereo-complexed crystals, near-complete stereo-complexed PLA fibers with flame retardancy were produced via clean and continuous melt spinning. The compatibility between PDLA-g-APP and PLLA matrix was studied by SEM, rheological analyses and DSC. Strength and flexibility of the fibers were simultaneously enhanced compared to traditional PLA due to the synergistic effect of interfacial compatibility and stereo-complexation. Compared to traditional PLA, the peak heat release rate and total heat release in microcalorimetry test were reduced by 33 % and 22 %, respectively. The flame-retardant fibers achieved a V-0 rating in the UL-94 test, and an increase in LOI value from 19.4 % to 28.2 %., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Pancreatic ductal adenocarcinoma (PDA) mortality is primarily attributed to metastasis and chemotherapy resistance. In this research, the long non-coding RNA MACC1-AS1 was studied, playing a significant role in regulating lipid oxidation processes. This regulation could further lead to the inhibition of ferroptosis induced by chemotherapeutic drugs, making it a contributing factor to gemcitabine resistance in PDA. In both gemcitabine-resistant PDA patients and mouse models, the elevated expression level of MACC1-AS1 in the tumors was noted. Additionally, overexpression of MACC1-AS1 in pancreatic cancer cells was found to enhance tolerance to gemcitabine and suppress ferroptosis. Proteomic analysis of drug-resistant pancreatic cells revealed that overexpressed MACC1-AS1 inhibited the ubiquitination degradation of residues in the protein kinase STK33 by MDM4. Furthermore, its accumulation in the cytoplasm activated STK33, further activating the ferroptosis-suppressing proteins GPX4, thereby counteracting gemcitabine-induced cellular oxidative damage. These findings suggested that the long non-coding RNA MACC1-AS1 could play a significant role in the ability of pancreatic cancer cells to evade iron-mediated ferroptosis induced by gemcitabine. This discovery holds promise for developing clinical therapeutic strategies to combat chemotherapy resistance in pancreatic cancer., (© 2024. The Author(s).)

Liquid crystal elastomers (LCEs) are a kind of polymer network that combines the entropic elasticity of polymer networks and the mesogenic unit by means of mild cross-linking. LCEs have been extensively investigated in various fields, including artificial muscles, actuators, and microrobots. However, LCEs are characterized by the poor mechanical properties of the light polymers themselves. In this study, we propose to prepare a carbon nanotube/liquid crystal elastomer (CNT/LCE) composite yarn by electrospinning technology and a two-step cross-linking strategy. The CNT/LCE composite yarn exhibits a reversible shrinkage ratio of nearly 70%, a tensile strength of 16.45 MPa, and a relatively sensitive response speed of ∼3 s, enabling a fast response by photothermal actuation. The research disclosed in this article may provide new insights for the development of artificial muscles and next-generation smart robots.

Ptychography, a widely used computational imaging method, generates images by processing coherent interference patterns scattered from an object of interest. In order to capture scenes with large field-of-view (FoV) and high spatial resolution simultaneously in a single shot, we propose a temporal-compressive structured-light Ptychography system. A novel three-step reconstruction algorithm composed of multi-frame spectra reconstruction, phase retrieval, and multi-frame image stitching is developed, where we employ the emerging Transformer-based network in the first step. Experimental results demonstrate that our system can expand the FoV by 20× without losing spatial resolution. Our results offer huge potential for enabling lensless imaging of molecules with large FoV as well as high spatial-temporal resolutions. We also notice that due to the loss of low-intensity information caused by the compressed sensing process, our method so far is only applicable to binary targets.

We consider capturing high-speed color video under different illumination conditions using a video snapshot compressive imaging system (video SCI). An adaptive progressive coding method is proposed, and we conduct an integrated design of the imaging system in terms of optics, mechanics, and control. Compared to previous video SCI systems, this adaptive progressive coding method mitigates the image stability issues in various illumination conditions, ensuring high-quality imaging while greatly improving the light throughput of the system. Based on the analysis of both simulation and real experimental results, we found that this imaging system can achieve color video shooting under an illumination range of 2 lux to 60 lux.

Background: Radical resection is a curative treatment for patients with hepatocellular carcinoma (HCC), but the incidence of recurrence remains high. We aimed to explore the performance of predicting HCC recurrence by longitudinal surveillance of the protein induced by vitamin K absence (PIVKA-II), alpha- fetoprotein (AFP), and lectin-reactive AFP (AFP-L3) during postoperative follow-up., Methods: Patients who underwent radical resection for HCC at the Ningbo Medical Centre Lihuili Hospital between January 2015 and December 2020 were included. All enrolled patients regularly monitor PIVKA-II, AFP, AFP-L3 every 3 months during postoperative follow-up. The surveillance performance of PIVKA-II, AFP, AFP-L3 during follow-up for the prediction of HCC recurrence was compared in patients. The generalized estimation equation (GEE) was used to analyze the trends of the tumor biomarkers and interactions with time. Area under the receiver operator characteristic (AUROC) curves, the optimal cut-off value, the sensitivity and specificity were calculated to evaluate the performance of the three biomarkers. The recurrence-free survival (RFS) and overall survival (OS) of patients with any of the elevated biomarkers was analyzed by Kaplan-Meier curves and the log-rank test. Multivariate logistic regression models were used to analyze potential risk factors for recurrence., Results: The GEE analysis indicated that PIVKA-II, AFP, AFP-L3 in the recurrence patients were higher than the no recurrence patients during follow-up, PIVKA-II and AFP showed increasing trends from 6 months before recurrence. In predicting recurrence, the AUROCs for PIVKA-II, AFP, AFP-L3 and their combination were 0.885, 0.754, 0.781 and 0.885 respectively, the optimal cut-off value for PIVKA-II, AFP, AFP-L3 was 29.5 mAU/ml, 10.7 ng/L, 1.5% respectively. The sensitivity in predicting recurrence for PIVKA-II, AFP, AFP-L3 and combination were 75.0, 54.7, 57.8 and 79.7% respectively. The RFS and the OS of patients with any of the biomarkers elevated during the follow-up was significantly shorter than that without elevated biomarkers ( P  < 0.001). Multivariate analysis showed that any of the biomarkers elevated was the independent risk factor of recurrence., Conclusion: Longitudinal surveillance of PIVKA-II, AFP and AFP-L3 can effectively predict recurrence of HCC after operation., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

In order to capture the spatial-spectral (x,y,λ) information of the scene, various techniques have been proposed. Different from the widely used scanning-based methods, spectral snapshot compressive imaging (SCI) utilizes the idea of compressive sensing to compressively capture the 3D spatial-spectral data-cube in a single-shot 2D measurement and thus it is efficient, enjoying the advantages of high-speed and low bandwidth. However, the reconstruction process , i.e., to retrieve the 3D cube from the 2D measurement, is an ill-posed problem and it is challenging to reconstruct high quality images. Previous works usually use 2D convolutions and preliminary attention to address this challenge. However, these networks and attention do not exactly extract spectral features. On the other hand, 3D convolutions can extract more features in a 3D cube, but increase computational cost significantly. To balance this trade-off, in this paper, we propose a hybrid multi-dimensional attention U-Net (HMDAU-Net) to reconstruct hyperspectral images from the 2D measurement in an end-to-end manner. HMDAU-Net integrates 3D and 2D convolutions in an encoder-decoder structure to fully utilize the abundant spectral information of hyperspectral images with a trade-off between performance and computational cost. Furthermore, attention gates are employed to highlight salient features and suppress the noise carried by the skip connections. Our proposed HMDAU-Net achieves superior performance over previous state-of-the-art reconstruction algorithms.

Background: This study was to evaluate the predictors of xerostomia and Grade 3 xerostomia in locoregionally advanced nasopharyngeal carcinoma (NPC) patients receiving radical radiotherapy and establish prediction models for xerostomia and Grade 3 xerostomia based on the predictors., Methods: Totally, 365 patients with locoregionally advanced NPC who underwent radical radiotherapy were randomly divided into the training set (n = 255) and the testing set (n = 110) at a ratio of 7:3. All variables were included in the least absolute shrinkage and selection operator regression to screen out the potential predictors for xerostomia as well as the Grade 3 xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy. The random forest (RF), a decision tree classifier (DTC), and extreme-gradient boosting (XGB) models were constructed. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC) and accuracy were analyzed to evaluate the predictive performance of the models., Results: In the RF model for predicting xerostomia, the sensitivity was 1.000 (95%CI 1.000-1.000), the PPV was 0.990 (95%CI 0.975-1.000), the NPV was 1.000 (95%CI 1.000-1.000), the AUC was 0.999 (95%CI 0.997-1.000) and the accuracy was 0.992 (95%CI 0.981-1.000) in the training set. The sensitivity was 0.933 (95%CI 0.880-0.985), the PPV was 0.933 (95%CI 0.880-0.985), and the AUC was 0.915 (95%CI 0.860-0.970) in the testing set. Hypertension, age, total radiotherapy dose, dose at 50% of the left parotid volume, mean dose to right parotid gland, mean dose to oral cavity, and course of induction chemotherapy were important variables associated with the risk of xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy. The AUC of DTC model for predicting xerostomia was 0.769 (95%CI 0.666-0.872) in the testing set. The AUC of the XGB model for predicting xerostomia was 0.834 (0.753-0.916) in the testing set. The RF model showed the good predictive ability with the AUC of 0.986 (95%CI 0.972-1.000) in the training set, and 0.766 (95%CI 0.626-0.905) in the testing set for identifying patients who at high risk of Grade 3 xerostomia in those with high risk of xerostomia., Conclusions: An RF model for predicting xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy and an RF model for predicting Grade 3 xerostomia in those with high risk of xerostomia showed good predictive ability., (© 2022. The Author(s).)

The development of ultrafast detectors for electron microscopy (EM) opens a new door to exploring dynamics of nanomaterials; however, it raises grand challenges for big data processing and storage. Here, we combine deep learning and temporal compressive sensing (TCS) to propose a novel EM big data compression strategy. Specifically, TCS is employed to compress sequential EM images into a single compressed measurement; an end-to-end deep learning network is leveraged to reconstruct the original images. Owing to the significantly improved compression efficiency and built-in denoising capability of the deep learning framework over conventional JPEG compression, compressed videos with a compression ratio of up to 30 can be reconstructed with high fidelity. Using this approach, considerable encoding power, memory, and transmission bandwidth can be saved, allowing it to be deployed to existing detectors. We anticipate the proposed technique will have far-reaching applications in edge computing for EM and other imaging techniques., Competing Interests: The authors declare no competing interests. The third author, Xin Yuan, is an employee of Nokia., (© 2021 The Authors.)

Background & Aims: Hepatopulmonary syndrome (HPS) is characterized by pulmonary vasodilation and arterial blood oxygen desaturation in patients with chronic liver disease. Generally, common bile duct ligation (CBDL) rats are a suitable experimental model for studying hepatopulmonary syndrome. Our previous study demonstrated that endotoxin surges markedly, followed by bacterial translocation and the loss of liver immune function in all the stages of CBDL, thereby contributing to the pathogenesis of HPS. However, the mechanisms behind the increase of the endotoxin and how to alleviate it have not yet been elucidated. Pulmonary injury induced by increased bilirubin, endotoxin, and inflammatory mediators occurs in the early and later stages of CBDL. This study assessed the effects of Tea polyphenols (TP) and Levofloxacin on endotoxin reduction and suppression of lung injury in HPS rats in the long and short term, respectively., Methods: Morphological change of pulmonary injury, HPS relative index, endotoxin concentration, and the activation extent of Malondialdehyde (MDA) and Myeloperoxidase (MPO) were evaluated in CBDL rats with or without TP and Levofloxacin for three weeks or six weeks. The inflammation factors of serum, lung tissue, and BALF were then compared at the same condition for the two time periods. This was followed by adoption of the network pharmacology approach, which was mainly composed of active component gathering, target prediction, HPS gene collection, network analysis, and gene enrichment analysis. Finally, the mRNA and protein levels of the inflammatory factors were studied and relative signaling expression was assessed using RT-PCR and Western blot analysis., Results: The obtained results indicated that the pulmonary injury manifestation was perceived and endotoxin, MDA, and MPO activation were markedly increased in the early and later stages of CBDL. TP and Levofloxacin treatment alleviated endotoxin infection and inflammation factor expression three weeks and six weeks after CBDL. In addition, Levofloxacin displayed a short time anti-bacterial effect, while TP exerted a long period function. TP and Levofloxacin also reduced TNF-α, TGF-β, IL-1β, PDGF-BB, NO, ICAM-1, and ET-1 expression on the mRNA or protein expression. With regard to the pharmacological mechanism, the network analysis indicated that 12 targets might be the therapeutic targets of TP and Levofloxacin on HPS, namely ET-1, NOs3, VEGFa, CCl2, TNF, Ptgs2, Hmox1, Alb, Ace, Cav1, and Mmp9. The gene enrichment analysis implied that TP and Levofloxacin probably benefited patients with HPS by modulating pathways associated with the AGE-RAGE signaling pathway, the TNF signaling pathway, the HIF-1 signaling pathway, the VEGF signaling pathway, and the IL-17 signaling pathway, Rheumatoid arthritis, Fluid shear stress, and atherosclerosis. Finally, the TNF-α level was mainly diminished on the protein level following CBDL., Conclusions: TP and Levofloxacin could alleviate pulmonary injury for short and long period, respectively, while at the same time preventing endotoxin and the development of HPS in CBDL rats. These effects are possibly associated with the regulation of the Endotoxin -TNF-α pathways., (Copyright © 2021 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

Accurate rainfall observation data with high temporal and spatial resolution are essential for national disaster prevention and mitigation as well as climate response decisions. This paper introduces a field experiment using an E-band millimeter-wave link to obtain rainfall rate information in Nanjing city, which is situated in the east of China. The link is 3 km long and operates at 71 and 81 GHz. We first distinguish between the wet and the dry periods, and then determine the classification threshold for calculating attenuation baseline in real time. We correct the influence of the wet antenna attenuation and finally calculate the rainfall rate through the power law relationship between the rainfall rate and the rain-induced attenuation. The experimental results show that the correlation between the rainfall rate retrieved from the 71 GHz link and the rainfall rate measured by the raindrop spectrometer is up to 0.9. The correlation at 81 GHz is up to 0.91. The mean relative errors are all below 5%. By comparing with the rainfall rate measured by the laser raindrop spectrometer set up at the experimental site, we verified the reliability and accuracy of monitoring rainfall using the E-band millimeter-wave link.

Towards the goal of developing scalable, economical and effective antimicrobial textiles to reduce infection transmission, here we prepared color-variable photodynamic materials comprised of photosensitizer (PS)-loaded wool/acrylic (W/A) blends. Wool fibers in the W/A blended fabrics were loaded with the photosensitizer rose bengal (RB), and the acrylic fibers were dyed with a variety of traditional cationic dyes (cationic yellow, cationic blue and cationic red) to broaden their color range. Investigations on the colorimetric and photodynamic properties of a series of these materials were implemented through CIELab evaluation, as well as photooxidation and antibacterial studies. Generally, the photodynamic efficacy of these dual-dyed fabrics was impacted by both the choice, and how much of the traditional cationic dye was employed in the dyeing of the W/A fabrics. When compared with the PS-only singly-dyed material, RB-W/A, that showed a 99.97% (3.5 log units; p = 0.02) reduction of Staphylococcus aureus under visible light illumination (λ ≥ 420 nm, 60 min), the addition of cationic dyes led to a slight decrease in the photoinactivation ability of the dual-dyed fabrics, but was still able to achieve a 99.3% inactivation of S. aureus . Overall, our findings demonstrate the feasibility and potential applications of low cost and color variable RB-loaded W/A blended fabrics as effective self-disinfecting textiles against pathogen transmission.

As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition. Improving the accuracy of a number of data is crucial in 3D face recognition problems. Traditional machine learning methods can be used to recognize 3D faces, but the face recognition rate has declined rapidly with the increasing number of 3D images. As a result, classifying large amounts of 3D image data is time-consuming, expensive, and inefficient. The deep learning methods have become the focus of attention in the 3D face recognition research. In our experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks. The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best Top-1 accuracy is up to 98.26% and the Top-2 accuracy is 99.40%. The framework proposed costs less iterations than traditional methods. The analysis suggests that a large number of 3D face data by the proposed recognition framework could significantly improve recognition decisions in realistic 3D face scenarios., Competing Interests: The authors declare there are no competing interests., (©2019 Zheng et al.)

Accurate and reliable segmentation of liver tissue and liver tumor is essential for the follow-up of hepatic diagnosis. In this paper, we present a method for liver segmentation and a method for liver tumor segmentation. The two methods are grounded on a novel unified level set method (LSM), which incorporates both region information and edge information to evolve the contour. This level set framework is more resistant to edge leakage than the single-information driven LSMs for liver segmentation and surpasses many other models for liver tumor segmentation. Specifically, for liver segmentation, a hybrid image preprocessing scheme is used first to convert an input CT image into a binary image. Then with manual setting of a few seed points on the obtained binary image, the following region-growing is performed to extract a rough liver region with no leakage. The unified LSM is proposed at last to refine the segmentation result. For liver tumor segmentation, a local intensity clustering based LSM coupled with hidden Markov random field and expectation-maximization (HMRF-EM) algorithm is applied to construct an enhanced edge indicator for the unified LSM. With this development, expected segmentation results can be obtained via the unified LSM, even for complex tumors. The two methods were evaluated with various datasets containing a local hospital dataset, the public datasets SLIVER07, 3Dircadb, and MIDAS via five measures. The proposed liver segmentation method outperformed other previous semiautomatic methods on the SLIVER07 dataset and required less interaction. The proposed liver tumor segmentation method was also competitive with other state-of-the-art methods in both accuracy and efficiency on the 3Dircadb database. Our methods are evaluated to be accurate and efficient, which allows their adoptions in clinical practice.

Background: This study aimed to investigate staging changes for Chinese breast cancer patients assessed by the 7 th (anatomic) and 8 th (prognostic) editions of the AJCC staging manual, and to explore the predictive factors for these changes., Methods: Data of patients who received curative surgery for stage I-III breast cancer at Ningbo Medical Center Lihuili Eastern Hospital were retrospectively reviewed. The assessment of staging was according to the criteria of the 7 th and 8 th editions of the AJCC staging manual. Univariate and multivariate logistic regression analyses were performed to analyze the associations between staging changes and clinicopathological characteristics., Results: Staging changes were found in 59.37% of patients and were more likely to be seen in stage IIIA (96.10%) and IIA (85.94%), then IIB (70.33%), IB (68.75%), followed by IA (36.17%) and IIIC (30.08%). In univariate analysis, staging changes were associated with tumor location, clinical tumor size, clinical axillary lymph node status and Ki67 index. However, multivariate analysis found that staging changes were significantly associated with tumor size >2 cm (odds ratio [OR] = 3.263, 95% confidence interval [95% CI], 2.638-4.036), lymph node involvement (OR = 2.261, 95% CI, 1.830-2.794) and high Ki-67 index (OR = 1.661, 95% CI 1.343-2.054)., Conclusions: Our study demonstrated that there were marked staging changes when 2 different editions of the AJCC staging manual were used. Since prognostic biomarkers are available in routine clinical practice, the more recent staging manual should be followed to select better systemic therapy and give better outcomes for Chinese breast cancer patients.

Laparoscopic pancreaticoduodenectomy (LPD) is one of the most complex operations in general surgery. With the development and maturation of surgical technology, more and more of such surgeries have been reported each year. Five LPDs have been performed in our department in the past year. We have achieved very satisfying clinical results with very few complications. The average operation takes 6.5 hours, which is significantly shorter compared to prior operations. In addition, the average hospitalization time was significantly shortened. Here we present a case report on one of the LPDs.