Enhancing Electrocatalytic CO2-to-CO Conversion by Weakening CO Binding through Nitrogen Integration in the Metallic Fe Catalyst.

Metallic iron (Fe) typically demonstrates the unfavorable catalytic activity for the CO2 reduction reaction (CO2RR), mainly attributed to the excessively strong binding of CO products on Fe sites. Toward this end, we employed an effective approach involving electronic structure modulation through nitrogen (N) integration to enhance the performance of the CO2RR. Here, an efficient catalyst has been developed, composed of N-doped metallic iron (Fe) nanoparticles encapsulated in a porous N-doped carbon framework. Notably, this N-integrated Fe catalyst displays significantly enhanced performance in the electrocatalytic reduction of CO2, yielding the highest CO Faradaic efficiency of 97.5% with a current density of 6.68 mA cm-2 at -0.7 V versus the reversible hydrogen electrode. The theoretical calculations, combined with the in situ attenuated total reflection surface-enhanced infrared absorption spectroscopy study, reveal that N integration modulates the electron density around Fe, resulting in the weakening of the binding strength between the Fe active sites and *CO intermediates, consequently promoting the desorption of CO and the overall CO2RR process.

Homogeneous crystallization and buried interface passivation for perovskite tandem solar modules.

Scalable fabrication of all-perovskite tandem solar cells is challenging because the narrow-bandgap subcells made of mixed lead-tin (Pb-Sn) perovskite films suffer from nonuniform crystallization and inferior buried perovskite interfaces. We used a dopant from Good's list of biochemical buffers, aminoacetamide hydrochloride, to homogenize perovskite crystallization and used it to extend the processing window for blade-coating Pb-Sn perovskite films and to selectively passivate defects at the buried perovskite interface. The resulting all-perovskite tandem solar module exhibited a certified power conversion efficiency of 24.5% with an aperture area of 20.25 square centimeters.

Polyimide covalent organic frameworks bearing star-shaped electron-deficient polycyclic aromatic hydrocarbon building blocks: molecular innovations for energy conversion and storage.

Polyimide covalent organic frameworks (PI-COFs) are outstanding functional materials for electrochemical energy conversion and storage owing to their integrated advantages of the high electroactive feature of polyimides and the periodic porous structure of COFs. Nevertheless, only anhydride monomers with C2 symmetry are generally used, and limited selectivity of electron-deficient monomers has become a major obstacle in the development of materials. The introduction of polycyclic aromatic hydrocarbons (PAHs) is a very effective method to regulate the structure-activity relationship of PI-COFs due to their excellent stability and electrical properties. Over the past two years, various star-shaped electron-deficient PAH building blocks possessing different compositions and topologies have been successfully fabricated, greatly improving the monomer selectivity and electrochemical performances of PI-COFs. This paper systematically summarizes the recent highlights in PI-COFs based on these building blocks. Firstly, the preparation of anhydride (or phthalic acid) monomers and PI-COFs related to different star-shaped PAHs is presented. Secondly, the applications of these PI-COFs in energy conversion and storage and the corresponding factors influencing their performance are discussed in detail. Finally, the future development of this meaningful field is briefly proposed.

Pseudo-medical image-guided technology based on 'CBCT-only' mode in esophageal cancer radiotherapy.

Purpose To minimize the various errors introduced by image-guided radiotherapy (IGRT) in the application of esophageal cancer treatment, this study proposes a novel technique based on the 'CBCT-only' mode of pseudo-medical image guidance. Methods The framework of this technology consists of two pseudo-medical image synthesis models in the CBCT→CT and the CT→PET direction. The former utilizes a dual-domain parallel deep learning model called AWM-PNet, which incorporates attention waning mechanisms. This model effectively suppresses artifacts in CBCT images in both the sinogram and spatial domains while efficiently capturing important image features and contextual information. The latter leverages tumor location and shape information provided by clinical experts. It introduces a PRAM-GAN model based on a prior region aware mechanism to establish a non-linear mapping relationship between CT and PET image domains.  As a result, it enables the generation of pseudo-PET images that meet the clinical requirements for radiotherapy. Results The NRMSE and multi-scale SSIM (MS-SSIM) were utilized to evaluate the test set, and the results were presented as median values with lower quartile and upper quartile ranges. For the AWM-PNet model, the NRMSE and MS-SSIM values were 0.0218 (0.0143, 0.0255) and 0.9325 (0.9141, 0.9410), respectively. The PRAM-GAN model produced NRMSE and MS-SSIM values of 0.0404 (0.0356, 0.0476) and 0.9154 (0.8971, 0.9294), respectively. Statistical analysis revealed significant differences (p < 0.05) between these models and others. The numerical results of dose metrics, including D98 %, Dmean, and D2 %, validated the accuracy of HU values in the pseudo-CT images synthesized by the AWM-PNet. Furthermore, the Dice coefficient results confirmed statistically significant differences (p < 0.05) in GTV delineation between the pseudo-PET images synthesized using the PRAM-GAN model and other compared methods. Conclusion The AWM-PNet and PRAM-GAN models have the capability to generate accurate pseudo-CT and pseudo-PET images, respectively. The pseudo-image-guided technique based on the 'CBCT-only' mode shows promising prospects for application in esophageal cancer radiotherapy.

Identification of organic pollutants and heavy metals in natural rubber wastewater and evaluation its phytotoxicity and cytogenotoxicity.

The natural rubber industry consumes large volumes of water and annually releases wastewater with rich organic and inorganic loads. This wastewater is allowed for soil irrigation in developing countries. However, the pollutant composition in wastewater and its environmental effects remain unclear. Therefore, we aimed to assess the wastewater's physicochemical parameters, toxic organic pollutants, heavy metals, and phytotoxic and cytogenotoxic. The result revealed that values of comprehensive wastewater parameters were recorded as chemical oxygen demand (187432.1 mg/L), pH (4.23), total nitrogen (1157.1 mg/L), ammonia nitrogen (1113.0 mg/L), total phosphorus (1181.2 mg/L), Zn (593.3 mg/L), Cr (0.6127 mg/L), and Ni (0.2986 mg/L). The organic compounds detected by LC-MS were salbostatin, sirolimus, Gibberellin A34-catabolite, 1-(sn-glycero-3-phospho)-1D-myo-inositol, and methyldiphenylsilane. The toxicity of the identified toxic chemicals and heavy metals was confirmed by onion and mung bean phytotoxicity characterization tests. The wastewater affected the germination of mung bean seeds, reduced or inhibited the growth of onions, and induced various chromosomal aberrations in root apical meristems. Our study shows that the treatment of natural rubber wastewater needs to be improved, and the feasibility of irrigating soil with wastewater needs to be reconsidered.

Scalable Solution-Processed Hybrid Electron Transport Layers for Efficient All-Perovskite Tandem Solar Modules.

All-perovskite tandem solar cells offer the potential to surpass the Shockley-Queisser (SQ) limit efficiency of single-junction solar cells while maintaining the advantages of low-cost and high-productivity solution processing. However, scalable solution processing of electron transport layer (ETL) in p-i-n structured perovskite solar subcells remains challenging due to the rough perovskite film surface and energy level mismatch between ETL and perovskites. Here, scalable solution processing of hybrid fullerenes (HF) with blade-coating on both wide-bandgap (≈1.80 eV) and narrow-bandgap (≈1.25 eV) perovskite films in all-perovskite tandem solar modules is developed. The HF, comprising a mixture of fullerene (C60 ), phenyl C61 butyric acid methyl ester, and indene-C60 bisadduct, exhibits improved conductivity, superior energy level alignment with both wide- and narrow-bandgap perovskites, and reduced interfacial nonradiative recombination when compared to the conventional thermal-evaporated C60 . With scalable solution-processed HF as the ETLs, the all-perovskite tandem solar modules achieve a champion power conversion efficiency of 23.3% (aperture area = 20.25 cm2 ). This study paves the way to all-solution processing of low-cost and high-efficiency all-perovskite tandem solar modules in the future.

Efficient All-Perovskite Tandem Solar Cells with Low-Optical-Loss Carbazolyl Interconnecting Layers.

Combining wide-band gap (WBG) and narrow-band gap (NBG) perovskites with interconnecting layers (ICLs) to construct monolithic all-perovskite tandem solar cell is an effective way to achieve high power conversion efficiency (PCE). However, optical losses from ICLs need to be further reduced to leverage the full potential of all-perovskite tandem solar cells. Here, metal oxide nanocrystal layers anchored with carbazolyl hole-selective-molecules (CHs), which exhibit much lower optical loss, is employed to replace poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT : PSS) as the hole transporting layers (HTLs) in lead-tin (Pb-Sn) perovskite sub-cells and ICLs in all-perovskite tandem solar cells. Optically transparent indium tin oxide nanocrystals (ITO NCs) layers are employed to enhance anchoring of CHs, while a mixture of two CHs is adopted to tune the surface energy-levels of ITO NCs. The optimized mixed Pb-Sn NBG perovskite solar cells demonstrate a high PCE of 23.2 %, with a high short-circuit current density (Jsc ) of 33.5 mA cm-2 . A high PCE of 28.1 % is further obtained in all-perovskite tandem solar cells, with the highest Jsc of 16.7 mA cm-2 to date. Encapsulated tandem solar cells maintain 90 % of their reference point after 500 h of operation at the maximum power point (MPP) under 1-Sun illumination.

The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis.

BACKGROUND: Due to tumoral heterogeneity and the lack of robust biomarkers, the prediction of chemoradiotherapy response and prognosis in patients with esophageal cancer (EC) is challenging. The goal of this study was to assess the study quality and clinical value of machine learning and radiomic-based quantitative imaging studies for predicting the outcomes of EC patients after chemoradiotherapy. MATERIALS AND METHODS: PubMed, Embase, and Cochrane were searched for eligible articles. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS), Image Biomarkers Standardization Initiative (IBSI) Guideline, and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement, as well as the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A meta-analysis of the evidence focusing on predicting chemoradiotherapy response and outcome in EC patients was implemented. RESULTS: Forty-six studies were eligible for qualitative synthesis. The mean RQS score was 9.07, with an adherence rate of 42.52%. The adherence rates of the TRIPOD and IBSI were 61.70 and 43.17%, respectively. Ultimately, 24 studies were included in the meta-analysis, of which 16 studies had a pooled sensitivity, specificity, and area under the curve (AUC) of 0.83 (0.76-0.89), 0.83 (0.79-0.86), and 0.84 (0.81-0.87) in neoadjuvant chemoradiotherapy datasets, as well as 0.84 (0.75-0.93), 0.89 (0.83-0.93), and 0.93 (0.90-0.95) in definitive chemoradiotherapy datasets, respectively. Moreover, radiomics could distinguish patients from the low-risk and high-risk groups with different disease-free survival (DFS) (pooled hazard ratio: 3.43, 95% CI 2.39-4.92) and overall survival (pooled hazard ratio: 2.49, 95% CI 1.91-3.25). The results of subgroup and regression analyses showed that some of the heterogeneity was explained by the combination with clinical factors, sample size, and usage of the deep learning (DL) signature. CONCLUSIONS: Noninvasive radiomics offers promising potential for optimizing treatment decision-making in EC patients. However, it is necessary to make scientific advancements in EC radiomics regarding reproducibility, clinical usefulness analysis, and open science categories. Improved model reporting of study objectives, blind assessment, and image processing steps are required to help promote real clinical applications of radiomics in EC research.

Sediment-water distribution and potential sources of polyhalogenated carbazoles in a coastal river locating at a north metropolis, China.

The fate and transformation of PHCZs in the coastal river environment are not yet comprehensively understood. Paired river water and surface sediment were collected, and 12 PHCZs were analyzed to find out their potential sources and investigate the distribution of PHCZs between river water and sediment. The concentration of ∑PHCZs varied from 8.66 to 42.97 ng/g (mean 22.46 ng/g) in sediment and 17.91 to 81.82 ng/L (mean 39.07 ng/L) in river water. 18-B-36-CCZ was the dominant PHCZ congener in sediment, while 36-CCZ was in water. Meanwhile, the logKoc values for CZ and PHCZs were among the first calculated in the estuary and the mean logKoc varied from 4.12 for 1-B-36-CCZ to 5.63 for 3-CCZ. The logKoc values of CCZs were higher than those of BCZs, this may suggest that sediments have a higher capacity for accumulation and storage of CCZs than highly mobile environmental media.

Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.

Background: Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR). Methods: In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation. Results: Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19). Conclusions: A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.

Background: The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification. Methods: Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models. Results: Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images. Conclusions: The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.

Recent Advances in the Photoreactions Triggered by Porphyrin-Based Triplet-Triplet Annihilation Upconversion Systems: Molecular Innovations and Nanoarchitectonics.

Triplet-triplet annihilation upconversion (TTA-UC) is a very promising technology that could be used to convert low-energy photons to high-energy ones and has been proven to be of great value in various areas. Porphyrins have the characteristics of high molar absorbance, can form a complex with different metal ions and a high proportion of triplet states as well as tunable structures, and thus they are important sensitizers for TTA-UC. Porphyrin-based TTA-UC plays a pivotal role in the TTA-UC systems and has been widely used in many fields such as solar cells, sensing and circularly polarized luminescence. In recent years, applications of porphyrin-based TTA-UC systems for photoinduced reactions have emerged, but have been paid little attention. As a consequence, this review paid close attention to the recent advances in the photoreactions triggered by porphyrin-based TTA-UC systems. First of all, the photochemistry of porphyrin-based TTA-UC for chemical transformations, such as photoisomerization, photocatalytic synthesis, photopolymerization, photodegradation and photochemical/photoelectrochemical water splitting, was discussed in detail, which revealed the different mechanisms of TTA-UC and methods with which to carry out reasonable molecular innovations and nanoarchitectonics to solve the existing problems in practical application. Subsequently, photoreactions driven by porphyrin-based TTA-UC for biomedical applications were demonstrated. Finally, the future developments of porphyrin-based TTA-UC systems for photoreactions were briefly discussed.

Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs.

Background: Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs. Methods: Two bone suppression methods (Gusarev et al. and Rajaraman et al.) were implemented. The Gusarev and Rajaraman methods were trained on 217 pairs of normal and bone-suppressed chest radiographs from the X-ray Bone Shadow Suppression dataset (https://www.kaggle.com/hmchuong/xray-bone-shadow-supression). Two classifier methods with different network architectures were implemented. Binary classifier models were trained on the public RICORD-1c and RSNA Pneumonia Challenge datasets. An external test dataset was created retrospectively from a set of 320 COVID-19 positive patients from Queen Elizabeth Hospital (Hong Kong, China) and a set of 518 non-COVID-19 patients from Pamela Youde Nethersole Eastern Hospital (Hong Kong, China), and used to evaluate the effect of bone suppression on classifier performance. Classification performance, quantified by sensitivity, specificity, negative predictive value (NPV), accuracy and area under the receiver operating curve (AUC), for non-suppressed radiographs was compared to that for bone suppressed radiographs. Some of the pre-trained models used in this study are published at (https://github.com/danielnflam). Results: Bone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture [from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed)]. For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance. Conclusions: Rajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.

Recent Advances in Porphyrin-Based Systems for Electrochemical Oxygen Evolution Reaction.

Oxygen evolution reaction (OER) plays a pivotal role in the development of renewable energy methods, such as water-splitting devices and the use of Zn-air batteries. First-row transition metal complexes are promising catalyst candidates due to their excellent electrocatalytic performance, rich abundance, and cheap price. Metalloporphyrins are a class of representative high-efficiency complex catalysts owing to their structural and functional characteristics. However, OER based on porphyrin systems previously have been paid little attention in comparison to the well-described oxygen reduction reaction (ORR), hydrogen evolution reaction, and CO2 reduction reaction. Recently, porphyrin-based systems, including both small molecules and porous polymers for electrochemical OER, are emerging. Accordingly, this review summarizes the recent advances of porphyrin-based systems for electrochemical OER. Firstly, the electrochemical OER for water oxidation is discussed, which shows various methodologies to achieve catalysis from homogeneous to heterogeneous processes. Subsequently, the porphyrin-based catalytic systems for bifunctional oxygen electrocatalysis including both OER and ORR are demonstrated. Finally, the future development of porphyrin-based catalytic systems for electrochemical OER is briefly prospected.

Research on new treatment mode of radiotherapy based on pseudo-medical images.

BACKGROUND AND OBJECTIVE: Multi-modal medical images with multiple feature information are beneficial for radiotherapy. A new radiotherapy treatment mode based on triangle generative adversarial network (TGAN) model was proposed to synthesize pseudo-medical images between multi-modal datasets. METHODS: CBCT, MRI and CT images of 80 patients with nasopharyngeal carcinoma were selected. The TGAN model based on multi-scale discriminant network was used for data training between different image domains. The generator of the TGAN model refers to cGAN and CycleGAN, and only one generation network can establish the non-linear mapping relationship between multiple image domains. The discriminator used multi-scale discrimination network to guide the generator to synthesize pseudo-medical images that are similar to real images from both shallow and deep aspects. The accuracy of pseudo-medical images was verified in anatomy and dosimetry. RESULTS: In the three synthetic directions, namely, CBCT â†’ CT, CBCT â†’ MRI, and MRI â†’ CT, significant differences (p < 0.05) in the three-fold-cross validation results on PSNR and SSIM metrics between the pseudo-medical images obtained based on TGAN and the real images. In the testing stage, for TGAN, the MAE metric results in the three synthesis directions (CBCT â†’ CT, CBCT â†’ MRI, and MRI â†’ CT) were presented as mean (standard deviation), which were 68.67 (5.83), 83.14 (8.48), and 79.96 (7.59), and the NMI metric results were 0.8643 (0.0253), 0.8051 (0.0268), and 0.8146 (0.0267) respectively. In terms of dose verification, the differences in dose distribution between the pseudo-CT obtained by TGAN and the real CT were minimal. The H values of the measurement results of dose uncertainty in PGTV, PGTVnd, PTV1, and PTV2 were 42.510, 43.121, 17.054, and 7.795, respectively (P < 0.05). The differences were statistically significant. The gamma pass rate (2%/2 mm) of pseudo-CT obtained by the new model was 94.94% (0.73%), and the numerical results were better than those of the three other comparison models. CONCLUSIONS: The pseudo-medical images acquired based on TGAN were close to the real images in anatomy and dosimetry. The pseudo-medical images synthesized by the TGAN model have good application prospects in clinical adaptive radiotherapy.

Bioremediation of tetramethyl thiuram disulfide and resource utilization of natural rubber wastewater by WR-2 Bacillus-dominated microbial community.

Tetramethyl thiuram disulfide (TMTD), an emerging pollutant with ecotoxicity and accumulation in rubber wastewater, is directly discharged by factories into the surrounding soil to save costs, and this disrupts the nearby ecosystem. In this study, an efficient bioremediation microbial community (WR-2) dominated by Bacillus was acclimatized and isolated from soil contaminated by rubber wastewater. After passing through the metabolic process of WR-2, the ecotoxic TMTD decomposes within 14 days. In the pot experiment, WR-2 not only completed the bioremediation of contaminated soil but also significantly improved the crop growth conditions and the product quality. These results show that WR-2 has broad application prospects in the bioremediation of soil contaminated by rubber wastewater. It also provides a theoretical framework for the resource utilization of the effluent at the end of the initial rubber processing.

Synthesis of pseudo-CT images from pelvic MRI images based on an MD-CycleGAN model for radiotherapy.

Objective.A multi-discriminator-based cycle generative adversarial network (MD-CycleGAN) model is proposed to synthesize higher-quality pseudo-CT from MRI images.Approach.MRI and CT images obtained at the simulation stage with cervical cancer were selected to train the model. The generator adopted DenseNet as the main architecture. The local and global discriminators based on a convolutional neural network jointly discriminated the authenticity of the input image data. In the testing phase, the model was verified by a fourfold cross-validation method. In the prediction stage, the data were selected to evaluate the accuracy of the pseudo-CT in anatomy and dosimetry, and they were compared with the pseudo-CT synthesized by GAN with the generator based on the architectures of ResNet, sUNet, and FCN.Main results.There are significant differences (P < 0.05) in the fourfold cross-validation results on the peak signal-to-noise ratio and structural similarity index metrics between the pseudo-CT obtained based on MD-CycleGAN and the ground truth CT (CTgt). The pseudo-CT synthesized by MD-CycleGAN had closer anatomical information to the CTgtwith a root mean square error of 47.83 ± 2.92 HU, a normalized mutual information value of 0.9014 ± 0.0212, and a mean absolute error value of 46.79 ± 2.76 HU. The differences in dose distribution between the pseudo-CT obtained by MD-CycleGAN and the CTgtwere minimal. The mean absolute dose errors of Dosemax, Dosemin, and Dosemeanbased on the planning target volume were used to evaluate the dose uncertainty of the four pseudo-CT. The u-values of the Wilcoxon test were 55.407, 41.82, and 56.208, and the differences were statistically significant. The 2%/2 mm-based gamma pass rate (%) of the proposed method was 95.45 ± 1.91, and the comparison methods (ResNet_GAN, sUnet_GAN, and FCN_GAN) were 93.33 ± 1.20, 89.64 ± 1.63, and 87.31 ± 1.94, respectively.Significance.The pseudo-CT images obtained based on MD-CycleGAN have higher imaging quality and are closer to the CTgtin terms of anatomy and dosimetry than other GAN models.

Research on obtaining pseudo CT images based on stacked generative adversarial network.

BACKGROUND: To investigate the feasibility of using a stacked generative adversarial network (sGAN) to synthesize pseudo computed tomography (CT) images based on ultrasound (US) images. METHODS: The pre-radiotherapy US and CT images of 75 patients with cervical cancer were selected for the training set of pseudo-image synthesis. In the first stage, labeled US images were used as the first conditional GAN input to obtain low-resolution pseudo CT images, and in the second stage, a super-resolution reconstruction GAN was used. The pseudo CT image obtained in the first stage was used as an input, following which a high-resolution pseudo CT image with clear texture and accurate grayscale information was obtained. Five cross validation tests were performed to verify our model. The mean absolute error (MAE) was used to compare each pseudo CT with the same patient's real CT image. Also, another 10 cases of patients with cervical cancer, before radiotherapy, were selected for testing, and the pseudo CT image obtained using the neural style transfer (NSF) and CycleGAN methods were compared with that obtained using the sGAN method proposed in this study. Finally, the dosimetric accuracy of pseudo CT images was verified by phantom experiments. RESULTS: The MAE metric values between the pseudo CT obtained based on sGAN, and the real CT in five-fold cross validation are 66.82±1.59 HU, 66.36±1.85 HU, 67.26±2.37 HU, 66.34±1.75 HU, and 67.22±1.30 HU, respectively. The results of the metrics, namely, normalized mutual information (NMI), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR), between the pseudo CT images obtained using the sGAN method and the ground truth CT (CTgt) images were compared with those of the other two methods via the paired t-test, and the differences were statistically significant. The dice similarity coefficient (DSC) measurement results showed that the pseudo CT images obtained using the sGAN method were more similar to the CTgt images of organs at risk. The dosimetric phantom experiments also showed that the dose distribution between the pseudo CT images synthesized by the new method was similar to that of the CTgt images. CONCLUSIONS: Compared with NSF and CycleGAN methods, the sGAN method can obtain more accurate pseudo CT images, thereby providing a new method for image guidance in radiotherapy for cervical cancer.

Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy.

PURPOSE: To propose a synthesis method of pseudo-CT (CTCycleGAN) images based on an improved 3D cycle generative adversarial network (CycleGAN) to solve the limitations of cone-beam CT (CBCT), which cannot be directly applied to the correction of radiotherapy plans. METHODS: The improved U-Net with residual connection and attention gates was used as the generator, and the discriminator was a full convolutional neural network (FCN). The imaging quality of pseudo-CT images is improved by adding a 3D gradient loss function. Fivefold cross-validation was performed to validate our model. Each pseudo CT generated is compared against the real CT image (ground truth CT, CTgt) of the same patient based on mean absolute error (MAE) and structural similarity index (SSIM). The dice similarity coefficient (DSC) coefficient was used to evaluate the segmentation results of pseudo CT and real CT. 3D CycleGAN performance was compared to 2D CycleGAN based on normalized mutual information (NMI) and peak signal-to-noise ratio (PSNR) metrics between the pseudo-CT and CTgt images. The dosimetric accuracy of pseudo-CT images was evaluated by gamma analysis. RESULTS: The MAE metric values between the CTCycleGAN and the real CT in fivefold cross-validation are 52.03 ± 4.26HU, 50.69 ± 5.25HU, 52.48 ± 4.42HU, 51.27 ± 4.56HU, and 51.65 ± 3.97HU, respectively, and the SSIM values are 0.87 ± 0.02, 0.86 ± 0.03, 0.85 ± 0.02, 0.85 ± 0.03, and 0.87 ± 0.03 respectively. The DSC values of the segmentation of bladder, cervix, rectum, and bone between CTCycleGAN and real CT images are 91.58 ± 0.45, 88.14 ± 1.26, 87.23 ± 2.01, and 92.59 ± 0.33, respectively. Compared with 2D CycleGAN, the 3D CycleGAN based pseudo-CT image is closer to the real image, with NMI values of 0.90 ± 0.01 and PSNR values of 30.70 ± 0.78. The gamma pass rate of the dose distribution between CTCycleGAN and CTgt is 97.0% (2%/2 mm). CONCLUSION: The pseudo-CT images obtained based on the improved 3D CycleGAN have more accurate electronic density and anatomical structure.

Recent Progress in Light-Driven Molecular Shuttles.

Molecular shuttles are typical molecular machines that could be applied in various fields. The motion modes of wheel components in rotaxanes could be strategically modulated by external stimuli, such as pH, ions, solvent, light, and so on. Light is particularly attractive because it is harmless and can be operated in a remote mode and usually no byproducts are formed. Over the past decade, many examples of light-driven molecular shuttles are emerging. Accordingly, this review summarizes the recent research progress of light-driven molecular shuttles. First, the light-driven mechanisms of molecular motions with different functional groups are discussed in detail, which show how to drive photoresponsive or non-photoresponsive molecular shuttles. Subsequently, the practical applications of molecular shuttles in different fields, such as optical information storage, catalysis for organic reactions, drug delivery, and so on, are demonstrated. Finally, the future development of light-driven molecular shuttle is briefly prospected.