Enhancing the quality of fermented plant leaves: the role of metabolite signatures and associated fungi.

Fungi play a pivotal role in fermentation processes, influencing the breakdown and transformation of metabolites. However, studies focusing on the effects of fungal-metabolite correlations on leaf fermentation quality enhancement are limited. This study investigated specific metabolites and fungi associated with high- and low-quality fermented plant leaves. Their changes were monitored over fermentation periods of 0, 8, 16, and 24 days. The results indicated that organoheterocyclic compounds, lipids, lipid-like molecules, organic nitrogen compounds, phenylpropanoids, and polyketides were predominant in high-quality samples. The fungi Saccharomyces (14.8%) and Thermoascus (4.6%) were predominantly found in these samples. These markers exhibited significant changes during the 24-day fermentation period. The critical influence of fungal community equilibrium was demonstrated by interspecies interactions (e.g., between Saccharomyces and Eurotium). A co-occurrence network analysis identified Saccharomyces as the primary contributor to high-quality samples. These markers collectively enhance the quality and sensory characteristics of the final product.

Glutamatergic neurons in the paraventricular nucleus of the hypothalamus participate in the regulation of visceral pain induced by pancreatic cancer in mice.

Background: Visceral pain induced by pancreatic cancer seriously affects patients' quality of life, and there is no effective treatment, because the mechanism of its neural circuit is unknown. Therefore, the aim of this study is to explore the main neural circuit mechanism regulating visceral pain induced by pancreatic cancer in mice. Methods: The mouse model of pancreatic cancer visceral pain was established on C57BL/6N mice by pancreatic injection of mPAKPC-luc cells. Abdominal mechanical hyperalgesia and hunch score were performed to assess visceral pain; the pseudorabies virus (PRV) was used to identify the brain regions innervating the pancreas; the c-fos co-labeling method was used to ascertain the types of activated neurons; in vitro electrophysiological patch-clamp technique was used to record the electrophysiological activity of specific neurons; the calcium imaging technique was used to determine the calcium activity of specific neurons; specific neuron destruction and chemogenetics methods were used to explore whether specific neurons were involved in visceral pain induced by pancreatic cancer. Results: The PRV injected into the pancreas was detected in the paraventricular nucleus of the hypothalamus (PVN). Immunofluorescence staining showed that the majority of c-fos were co-labeled with glutamatergic neurons in the PVN. In vitro electrophysiological results showed that the firing frequency of glutamatergic neurons in the PVN was increased. The calcium imaging results showed that the calcium activity of glutamatergic neurons in the PVN was enhanced. Both specific destruction of glutamatergic neurons and chemogenetics inhibition of glutamatergic neurons in the PVN alleviated visceral pain induced by pancreatic cancer. Conclusions: Glutamatergic neurons in the PVN participate in the regulation of visceral pain induced by pancreatic cancer in mice, providing new insights for the discovery of effective targets for the treatment of pancreatic cancer visceral pain.

Functional synergy of a human-specific and an ape-specific metabolic regulator in human neocortex development.

Metabolism has recently emerged as a major target of genes implicated in the evolutionary expansion of human neocortex. One such gene is the human-specific gene ARHGAP11B. During human neocortex development, ARHGAP11B increases the abundance of basal radial glia, key progenitors for neocortex expansion, by stimulating glutaminolysis (glutamine-to-glutamate-to-alpha-ketoglutarate) in mitochondria. Here we show that the ape-specific protein GLUD2 (glutamate dehydrogenase 2), which also operates in mitochondria and converts glutamate-to-αKG, enhances ARHGAP11B's ability to increase basal radial glia abundance. ARHGAP11B + GLUD2 double-transgenic bRG show increased production of aspartate, a metabolite essential for cell proliferation, from glutamate via alpha-ketoglutarate and the TCA cycle. Hence, during human evolution, a human-specific gene exploited the existence of another gene that emerged during ape evolution, to increase, via concerted changes in metabolism, progenitor abundance and neocortex size.

Automated contouring, treatment planning, and quality assurance for VMAT craniospinal irradiation (VMAT-CSI).

Purpose: Create a comprehensive automated solution for pediatric and adult VMAT-CSI including contouring, planning, and plan check to reduce planning time and improve plan quality. Methods: Seventy-seven previously treated CSI patients (age, 2-67 years) were used for creation of an auto-contouring model to segment 25 organs at risk (OARs). The auto-contoured OARs were evaluated using the Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and a qualitative ranking by one physician and one physicist (scale: 1-acceptable, 2-minor edits, 3-major edits). The auto-planning script was developed using the Varian Eclipse Scripting API and tested with 20 patients previously treated with either low-dose VMAT-CSI (12 Gy) or high-dose VMAT-CSI (36 Gy + 18 Gy boost). Clinically relevant metrics, planning time, and blinded physician review were used to evaluate significance of differences between the auto and manual plans. Finally, the plan preparation for treatment and plan check processes were automated to improve efficiency and safety of VMAT-CSI. Results: The auto-contours achieved an average DSC of 0.71 ± 0.15, HD95 of 4.81 ± 4.68, and reviewers' ranking of 1.22 ± 0.39, indicating close to "acceptable-as-is" contours. Compared to the manual CSI plans, the auto-plans for both dose regimens achieved statistically significant reductions in body V50% and Dmean for parotids, submandibular, and thyroid glands. The variance in the dosimetric parameters decreased for the auto-plans as compared to the manual plans indicating better plan consistency. From the blinded review, the auto-plans were marked as equivalent or superior to the manual-plans 88.3% of the time. The required time for the auto-contouring and planning was consistently between 1-2 hours compared to an estimated 5-6 hours for manual contouring and planning. Conclusions: Reductions in contouring and planning time without sacrificing plan quality were obtained using the developed auto-planning process. The auto-planning scripts and documentation will be made freely available to other institutions and clinics.

AI-assisted mass spectrometry imaging with in situ image segmentation for subcellular metabolomics analysis.

Subcellular metabolomics analysis is crucial for understanding intracellular heterogeneity and accurate drug-cell interactions. Unfortunately, the ultra-small size and complex microenvironment inside the cell pose a great challenge to achieving this goal. To address this challenge, we propose an artificial intelligence-assisted subcellular mass spectrometry imaging (AI-SMSI) strategy with in situ image segmentation. Based on the nanometer-resolution MSI technique, the protonated guanine and threonine ions were respectively employed as the nucleus and cytoplasmic markers to complete image segmentation at the subcellular level, avoiding mutual interference of signals from various compartments in the cell. With advanced AI models, the metabolites within the different regions could be further integrated and profiled. Through this method, we decrypted the distinct action mechanism of isomeric drugs, doxorubicin (DOX) and epirubicin (EPI), only with a stereochemical inversion at C-4'. Within the cytoplasmic region, fifteen specific metabolites were discovered as biomarkers for distinguishing the drug action difference between DOX and EPI. Moreover, we identified that the downregulations of glutamate and aspartate in the malate-aspartate shuttle pathway may contribute to the higher paratoxicity of DOX. Our current AI-SMSI approach has promising applications for subcellular metabolomics analysis and thus opens new opportunities to further explore drug-cell specific interactions for the long-term pursuit of precision medicine.

An Adaptive Radon-Transform-Based Marker Detection and Localization Method for Displacement Measurements Using Unmanned Aerial Vehicles.

UAVs have been widely used in deformation monitoring because of their high availability and flexibility. However, the quality of UAV images is affected by changing attitude and surveying environments, resulting in a low monitoring accuracy. Cross-shaped markers are used to improve the accuracy of UAV monitoring due to their distinct center contrast and absence of eccentricity. However, existing methods cannot rapidly and precisely detect these markers in UAV images. To address these problems, this paper proposes an adaptive Radon-transform-based marker detection and localization method for UAV displacement measurements, focusing on two critical detection parameters, namely, the radius of marker information acquisition and the edge width of the cross-shaped scoring template. The experimental results show that the marker detection rate is 97.2% under different combinations of flight altitudes, radius ratios of marker information acquisition, and marker sizes. Furthermore, the root mean square error of detection and localization is 0.57 pixels, significantly surpassing the performance and accuracy of other methods. We also derive the critical detection radius and appropriate parameter combinations for different heights to further improve the practicality of the method.

Nanoparticles targeting mutant p53 overcome chemoresistance and tumor recurrence in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) shows high drug resistance and leads to low survival due to the high level of mutated Tumor Protein p53 (TP53). Cisplatin is a first-line treatment option for NSCLC, and the p53 mutation is a major factor in chemoresistance. We demonstrate that cisplatin chemotherapy increases the risk of TP53 mutations, further contributing to cisplatin resistance. Encouragingly, we find that the combination of cisplatin and fluvastatin can alleviate this problem. Therefore, we synthesize Fluplatin, a prodrug consisting of cisplatin and fluvastatin. Then, Fluplatin self-assembles and is further encapsulated with poly-(ethylene glycol)-phosphoethanolamine (PEG-PE), we obtain Fluplatin@PEG-PE nanoparticles (FP NPs). FP NPs can degrade mutant p53 (mutp53) and efficiently trigger endoplasmic reticulum stress (ERS). In this study, we show that FP NPs relieve the inhibition of cisplatin chemotherapy caused by mutp53, exhibiting highly effective tumor suppression and improving the poor NSCLC prognosis.

Electronic Documentation of Intraoperative Observation of Cystoscopic Procedures Using the cMDX Information System.

PURPOSE: Accurate documentation of lesions during transurethral resection of bladder tumors (TURBT) is essential for precise diagnosis, treatment planning, and follow-up care. However, optimizing schematic documentation techniques for bladder lesions has received limited attention. MATERIALS AND METHODS: This prospective observational study used a cMDX-based documentation system that facilitates graphical representation, a lesion-specific questionnaire, and heatmap analysis with a posterization effect. We designed a graphical scheme for bladder covering bladder landmarks to visualize anatomic features and to document the lesion location. The lesion-specific questionnaire was integrated for comprehensive lesion characterization. Finally, spatial analyses were applied to investigate the anatomic distribution patterns of bladder lesions. RESULTS: A total of 97 TURBT cases conducted between 2021 and 2023 were included, identifying 176 lesions. The lesions were distributed in different bladder areas with varying frequencies. The distribution pattern, sorted by frequency, was observed in the following areas: posterior, trigone, lateral right and anterior, and lateral left and dome. Suspicious levels were assigned to the lesions, mostly categorized either as indeterminate or moderate. Lesion size analysis revealed that most lesions fell between 5 and 29 mm. CONCLUSION: The study highlights the potential of schematic documentation techniques for informed decision making, quality assessment, primary research, and secondary data utilization of intraoperative data in the context of TURBT. Integrating cMDX and heatmap analysis provides valuable insights into lesion distribution and characteristics.

Quantifying particle concentration via AI-enhanced optical coherence tomography.

Efficient and robust quantification of the number of nanoparticles in solution is not only essential but also insufficient in nanotechnology and biomedical research. This paper proposes to use optical coherence tomography (OCT) to quantify the number of gold nanorods, which exemplify the nanoparticles with high light scattering signals. Additionally, we have developed an AI-enhanced OCT image processing to improve the accuracy and robustness of the quantification result.

The Protective Effects of Ecdysterone on Cognitive Impairment through Regulating Akt/GSK-3ß/Nrf2 Signaling Pathway and Oxidative Stress in Cognitive Mice Model and Aß-Induced Cell Neurotoxicity.

BACKGROUND: Severe neurological condition like Alzheimer's disease (AD) has a significantly negative impact on families and society, wherein there is no proven cure. As one of the principal active constituents of Achyranthes bidentata Blume, ecdysterone (ECR) has demonstrated antioxidant and cognitive dysfunction improvement effects. Nonetheless, the mechanism underlying the improvement of cognitive dysfunction by ECR remains unclear. This study sought to ascertain whether ECR may allebviate cognitive impairment by reducing oxidative stress via activation of the nuclear factor erythroid-2-related factor-2 (Nrf2) antioxidant system through Akt/GSK3ß pathway. METHODS: In terms of the experimental procedure, we determined the neuroprotective benefits of ECR in vivo via a cognitive impairment model of senescence-accelerated mouse prone 8 (SAMP8), we performed procedures such as behavioral testing, biochemical assaying, Nissl and TUNEL stainings, as well as flow cytometry, immunohistochemistry and western blotting. Furthermore, we investigated the underlying mechanistic action of ECR by activating PC12 cells with ß-amyloid peptide fragment 25-35 (Aß25-35). RESULTS: In vivo studies showed that ECR effectively improved cognitive impairment in SAMP8 via enhancement of learning and memory capabilities, but decreased oxidative stress, apoptosis and neuronal damage in the hippocampus. During the in vitro study, we observed that ECR dose-dependently reduced the oxidative stress and apoptosis that were induced in PC12 cells by Aß25-35. Additionally, the use of Akt inhibitors further established the potential of ECR to control Nrf2 through activation of the Akt/GSK3ß pathway and protect the PC12 cells from Aß25-35 induced damage. CONCLUSIONS: These findings offer proof that ECR reduces cognitive impairment by triggering the Nrf2 antioxidant system via the Akt/GSK3ß pathway and offer fresh information on ECR's potential as a promising therapeutic development candidate for AD.

Reactive Oxygen Species-Responsive Nanoparticles Toward Extracellular Matrix Normalization for Pancreatic Fibrosis Regression.

Pancreatic fibrosis (PF) is primarily characterized by aberrant production and degradation modes of extracellular matrix (ECM) components, resulting from the activation of pancreatic stellate cells (PSCs) and the pathological cross-linking of ECM mediated by lysyl oxidase (LOX) family members. The excessively deposited ECM increases matrix stiffness, and the over-accumulated reactive oxygen species (ROS) induces oxidative stress, which further stimulates the continuous activation of PSCs and advancing PF; challenging the strategy toward normalizing ECM homeostasis for the regression of PF. Herein, ROS-responsive and Vitamin A (VA) decorated micelles (named LR-SSVA) to reverse the imbalanced ECM homeostasis for ameliorating PF are designed and synthesized. Specifically, LR-SSVA selectively targets PSCs via VA, thereby effectively delivering siLOXL1 and resveratrol (RES) into the pancreas. The ROS-responsive released RES inhibits the overproduction of ECM by eliminating ROS and inactivating PSCs, meanwhile, the decreased expression of LOXL1 ameliorates the cross-linked collagen for easier degradation by collagenase which jointly normalizes ECM homeostasis and alleviates PF. This research shows that LR-SSVA is a safe and efficient ROS-response and PSC-targeted drug-delivery system for ECM normalization, which will propose an innovative and ideal platform for the reversal of PF.

Sulfur Migration Enhanced Proton-Coupled Electron Transfer for Efficient CO2 Desorption with Core-Shelled C@Mn3O4.

Transforming hazardous species into active sites by ingenious material design was a promising and positive strategy to improve catalytic reactions in industrial applications. To synergistically address the issue of sluggish CO2 desorption kinetics and SO2-poisoning solvent of amine scrubbing, we propose a novel method for preparing a high-performance core-shell C@Mn3O4 catalyst for heterogeneous sulfur migration and in situ reconstruction to active -SO3H groups, and thus inducing an enhanced proton-coupled electron transfer (PCET) effect for CO2 desorption. As anticipated, the rate of CO2 desorption increases significantly, by 255%, when SO2 is introduced. On a bench scale, dynamic CO2 capture experiments reveal that the catalytic regeneration heat duty of SO2-poisoned solvent experiences a 32% reduction compared to the blank case, while the durability of the catalyst is confirmed. Thus, the enhanced PCET of C@Mn3O4, facilitated by sulfur migration and simultaneous transformation, effectively improves the SO2 resistance and regeneration efficiency of amine solvents, providing a novel route for pursuing cost-effective CO2 capture with an amine solvent.

PolypMixNet: Enhancing semi-supervised polyp segmentation with polyp-aware augmentation.

BACKGROUND: AI-assisted polyp segmentation in colonoscopy plays a crucial role in enabling prompt diagnosis and treatment of colorectal cancer. However, the lack of sufficient annotated data poses a significant challenge for supervised learning approaches. Existing semi-supervised learning methods also suffer from performance degradation, mainly due to task-specific characteristics, such as class imbalance in polyp segmentation. PURPOSE: The purpose of this work is to develop an effective semi-supervised learning framework for accurate polyp segmentation in colonoscopy, addressing limited annotated data and class imbalance challenges. METHODS: We proposed PolypMixNet, a semi-supervised framework, for colorectal polyp segmentation, utilizing novel augmentation techniques and a Mean Teacher architecture to improve model performance. PolypMixNet introduces the polyp-aware mixup (PolypMix) algorithm and incorporates dual-level consistency regularization. PolypMix addresses the class imbalance in colonoscopy datasets and enhances the diversity of training data. By performing a polyp-aware mixup on unlabeled samples, it generates mixed images with polyp context along with their artificial labels. A polyp-directed soft pseudo-labeling (PDSPL) mechanism was proposed to generate high-quality pseudo labels and eliminate the dilution of lesion features caused by mixup operations. To ensure consistency in the training phase, we introduce the PolypMix prediction consistency (PMPC) loss and PolypMix attention consistency (PMAC) loss, enforcing consistency at both image and feature levels. Code is available at https://github.com/YChienHung/PolypMix. RESULTS: PolypMixNet was evaluated on four public colonoscopy datasets, achieving 88.97% Dice and 88.85% mIoU on the benchmark dataset of Kvasir-SEG. In scenarios where the labeled training data is limited to 15%, PolypMixNet outperforms the state-of-the-art semi-supervised approaches with a 2.88-point improvement in Dice. It also shows the ability to reach performance comparable to the fully supervised counterpart. Additionally, we conducted extensive ablation studies to validate the effectiveness of each module and highlight the superiority of our proposed approach. CONCLUSION: PolypMixNet effectively addresses the challenges posed by limited annotated data and unbalanced class distributions in polyp segmentation. By leveraging unlabeled data and incorporating novel augmentation and consistency regularization techniques, our method achieves state-of-the-art performance. We believe that the insights and contributions presented in this work will pave the way for further advancements in semi-supervised polyp segmentation and inspire future research in the medical imaging domain.

Donor engineering of a benzothiadiazole-based D-A-D-type molecular semiconductor for perovskite solar cells: a theoretical study.

Due to the easy formation of compact molecular packing arrangements and the favorable photophysical and electrochemical properties, donor-acceptor-donor (D-A-D)-type small molecule hole-transporting materials (HTMs) have been widely synthesized and researched to improve the efficiency and stability of perovskite solar cells (PSCs). The main approach in recent experiments has been to seek good acceptors, whereas the influence of the electron-donating units has been less reported. In this work, six new benzothiadiazole-based D-A-D-type HTMs are tailored by employing the ethyl-substituted phenoxazine (POZ), phenothiazine (PTZ) and carbazole (CZ) as the donors. To obtain an elementary understanding of new HTMs, the electronic, optical, hole-transporting and interfacial properties are simulated with quantum chemistry methods. The results indicate that all tailored HTMs exhibit suitable energy alignment compared with the band structures of the perovskite, and the continuous highest occupied molecular orbital (HOMO) levels will be helpful for interfacial energy regulation. In comparison with the YN1, the maximum absorption wavelengths of the newly designed HTMs are red-shifted due to the decreased excitation energies from the ground-state to the first singlet excited-state. Importantly, the hole mobilities of all designed HTMs are distinctly higher than the referenced YN1, which is contributed by the better planarity of the molecular skeleton and the easier orbital overlapping between adjacent molecules. The interfacial simulations manifest that the FAPbI3/SM37 system displays a more stable adsorption configuration and greater charge redistributions at the interface compared to YN1, which further promotes the separation of photogenerated electron-hole pairs. Moreover, larger Stokes shifts and better solubility are also acquired for the new HTMs. In summary, our calculations not only propose several potential highly efficient HTMs, but also provide useful insights at the atomic level for the experimental synthesis of new D-A-D-type HTMs.

Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. Existing methods, including advanced deep learning techniques, struggle to reliably impute gene expressions due to a lack of mechanisms that explicitly consider the underlying biological knowledge of the system. In reality, it has long been recognized that gene-gene interactions may serve as reflective indicators of underlying biology processes, presenting discriminative signatures of the cells. A genomic data analysis framework that is capable of leveraging the underlying gene-gene interactions is thus highly desirable and could allow for more reliable identification of distinctive patterns of the genomic data through extraction and integration of intricate biological characteristics of the genomic data. Here we tackle the problem in two steps to exploit the gene-gene interactions of the system. We first reposition the genes into a 2D grid such that their spatial configuration reflects their interactive relationships. To alleviate the need for labeled ground truth gene expression datasets, a self-supervised 2D convolutional neural network is employed to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods.

Deciphering the Feature Representation of Deep Neural Networks for High-Performance AI.

AI driven by deep learning is transforming many aspects of science and technology. The enormous success of deep learning stems from its unique capability of extracting essential features from Big Data for decision-making. However, the feature extraction and hidden representations in deep neural networks (DNNs) remain inexplicable, primarily because of lack of technical tools to comprehend and interrogate the feature space data. The main hurdle here is that the feature data are often noisy in nature, complex in structure, and huge in size and dimensionality, making it intractable for existing techniques to analyze the data reliably. In this work, we develop a computational framework named contrastive feature analysis (CFA) to facilitate the exploration of the DNN feature space and improve the performance of AI. By utilizing the interaction relations among the features and incorporating a novel data-driven kernel formation strategy into the feature analysis pipeline, CFA mitigates the limitations of traditional approaches and provides an urgently needed solution for the analysis of feature space data. The technique allows feature data exploration in unsupervised, semi-supervised and supervised formats to address different needs of downstream applications. The potential of CFA and its applications for pruning of neural network architectures are demonstrated using several state-of-the-art networks and well-annotated datasets across different disciplines.

Adsorption-desorption behavior of florpyrauxifen-benzyl on three microplastics in aqueous environment as well as its mechanism and various influencing factors.

Microplastics (MPs) and pesticides are two categories contaminants with proposed negative impacts to aqueous ecosystems, and adsorption of pesticides on MPs may result in their long-range transport and compound combination effects. Florpyrauxifen-benzyl, a novel pyridine-2-carboxylate auxin herbicide has been widely used to control weeds in paddy field, but the insights of which are extremely limited. Therefore, adsorption and desorption behaviors of florpyrauxifen-benzyl on polyvinyl chloride (PVC), polyethylene (PE) and disposable face masks (DFMs) in five water environment were investigated. The impacts of various environmental factors on adsorption capacity were evaluated, as well as adsorption mechanisms. The results revealed significant variations in adsorption capacity of florpyrauxifen-benzyl on three MPs, with approximately order of DFMs > PE > PVC. The discrepancy can be attributed to differences in structural and physicochemical properties, as evidenced by various characterization analysis. The kinetics and isotherm of florpyrauxifen-benzyl on three MPs were suitable for different models, wherein physical force predominantly governed adsorption process. Thermodynamic analysis revealed that both high and low temperatures weakened PE and DFMs adsorption, whereas temperature exhibited negligible impact on PVC adsorption. The adsorption capacity was significantly influenced by most environmental factors, particularly pH, cations and coexisting herbicide. This study provides valuable insights into the fate of florpyrauxifen-benzyl in presence of MPs, suggesting that PVC, PE and DFMs can serve as carriers of florpyrauxifen-benzyl in aquatic environment.

A time- and space-saving Monte Carlo simulation method using post-collimation generative adversarial network for dose calculation of an O-ring gantry Linac.

PURPOSE: This study explores the feasibility of employing Generative Adversarial Networks (GANs) to model the RefleXion X1 Linac. The aim is to investigate the accuracy of dose simulation and assess the potential computational benefits. METHODS: The X1 Linac is a new radiotherapy machine with a binary multi-leaf collimation (MLC) system, facilitating innovative biology-guided radiotherapy. A total of 34 GAN generators, each representing a desired MLC aperture, were developed. Each generator was trained using a phase space file generated underneath the corresponding aperture, enabling the generation of particles and serving as a beam source for Monte Carlo simulation. Dose distributions in water were simulated for each aperture using both the GAN and phase space sources. The agreement between dose distributions was evaluated. The computational time reduction from bypassing the collimation simulation and storage space savings were estimated. RESULTS: The percentage depth dose at 10 cm, penumbra, and full-width half maximum of the GAN simulation agree with the phase space simulation, with differences of 0.4 % ± 0.2 %, 0.32 ± 0.66 mm, and 0.26 ± 0.44 mm, respectively. The gamma passing rate (1 %/1mm) for the planar dose exceeded 90 % for all apertures. The estimated time-saving for simulating an plan using 5766 beamlets was 530 CPU hours. The storage usage was reduced by a factor of 102. CONCLUSION: The utilization of the GAN in simulating the X1 Linac demonstrated remarkable accuracy and efficiency. The reductions in both computational time and storage requirements make this approach highly valuable for future dosimetry studies and beam modeling.

The WuC-Adam algorithm based on joint improvement of Warmup and cosine annealing algorithms.

The Adam algorithm is a common choice for optimizing neural network models. However, its application often brings challenges, such as susceptibility to local optima, overfitting and convergence problems caused by unstable learning rate behavior. In this article, we introduce an enhanced Adam optimization algorithm that integrates Warmup and cosine annealing techniques to alleviate these challenges. By integrating preheating technology into traditional Adam algorithms, we systematically improved the learning rate during the initial training phase, effectively avoiding instability issues. In addition, we adopt a dynamic cosine annealing strategy to adaptively adjust the learning rate, improve local optimization problems and enhance the model's generalization ability. To validate the effectiveness of our proposed method, extensive experiments were conducted on various standard datasets and compared with traditional Adam and other optimization methods. Multiple comparative experiments were conducted using multiple optimization algorithms and the improved algorithm proposed in this paper on multiple datasets. On the MNIST, CIFAR10 and CIFAR100 datasets, the improved algorithm proposed in this paper achieved accuracies of 98.87%, 87.67% and 58.88%, respectively, with significant improvements compared to other algorithms. The experimental results clearly indicate that our joint enhancement of the Adam algorithm has resulted in significant improvements in model convergence speed and generalization performance. These promising results emphasize the potential of our enhanced Adam algorithm in a wide range of deep learning tasks.

Inhaled nanoparticles for treating idiopathic pulmonary fibrosis by inhibiting honeycomb cyst and alveoli interstitium remodeling.

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with high mortality. The Food and Drug Administration-approved drugs, nintedanib and pirfenidone, could delay progressive fibrosis by inhibiting the overactivation of fibroblast, however, there was no significant improvement in patient survival due to low levels of drug accumulation and remodeling of honeycomb cyst and interstitium surrounding the alveoli. Herein, we constructed a dual drug (verteporfin and pirfenidone)-loaded nanoparticle (Lip@VP) with the function of inhibiting airway epithelium fluidization and fibroblast overactivation to prevent honeycomb cyst and interstitium remodeling. Specifically, Lip@VP extensively accumulated in lung tissues via atomized inhalation. Released verteporfin inhibited the fluidization of airway epithelium and the formation of honeycomb cyst, and pirfenidone inhibited fibroblast overactivation and reduced cytokine secretion that promoted the fluidization of airway epithelium. Our results indicated that Lip@VP successfully rescued lung function through inhibiting honeycomb cyst and interstitium remodeling. This study provided a promising strategy to improve the therapeutic efficacy for IPF.