A screened predictive model for esophageal squamous cell carcinoma based on salivary flora data

Esophageal squamous cell carcinoma (ESCC) is a malignant tumor of the digestive system in the esophageal squamous epithelium. Many studies have linked esophageal cancer (EC) to the imbalance of oral microecology. In this work, different machine learning (ML) models including Random Forest (RF), Gaussian mixture model (GMM), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM) and extreme gradient boosting (XGBoost) based on Genetic Algorithm (GA) optimization was developed to predict the relationship between salivary flora and ESCC by combining the relative abundance data of Bacteroides, Firmicutes, Proteobacteria, Fusobacteria and Actinobacteria in the saliva of patients with ESCC and healthy control. The results showed that the XGBoost model without parameter optimization performed best on the entire dataset for ESCC diagnosis by cross-validation (Accuracy = 73.50%). Accuracy and the other evaluation indicators, including Precision, Recall, F1-score and the area under curve (AUC) of the receiver operating characteristic (ROC), revealed XGBoost optimized by the GA (GA-XGBoost) achieved the best outcome on the testing set (Accuracy = 89.88%, Precision = 89.43%, Recall = 90.75%, F1-score = 90.09%, AUC = 0.97). The predictive ability of GA-XGBoost was validated in phylum-level salivary microbiota data from ESCC patients and controls in an external cohort. The results obtained in this validation (Accuracy = 70.60%, Precision = 46.00%, Recall = 90.55%, F1-score = 61.01%) illustrate the reliability of the predictive performance of the model. The feature importance rankings obtained by XGBoost indicate that Bacteroides and Actinobacteria are the two most important factors in predicting ESCC. Based on these results, GA-XGBoost can predict and diagnose ESCC according to the relative abundance of salivary flora, providing an effective tool for the non-invasive prediction of esophageal malignancies

Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models

Recently, the diffusion model has emerged as a superior generative model that can produce high quality and realistic images. However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images. For instance, errors in image translation may distort, shift, or even remove structures and tumors, leading to incorrect diagnosis and inadequate treatments. Training and conditioning diffusion models using paired source and target images with matching anatomy can help. However, such paired data are very difficult and costly to obtain, and may also reduce the robustness of the developed model to out-of-distribution testing data. We propose a frequency-guided diffusion model (FGDM) that employs frequency-domain filters to guide the diffusion model for structure-preserving image translation. Based on its design, FGDM allows zero-shot learning, as it can be trained solely on the data from the target domain, and used directly for source-to-target domain translation without any exposure to the source-domain data during training. We evaluated it on three cone-beam CT (CBCT)-to-CT translation tasks for different anatomical sites, and a cross-institutional MR imaging translation task. FGDM outperformed the state-of-the-art methods (GAN-based, VAE-based, and diffusion-based) in metrics of Frechet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), showing its significant advantages in zero-shot medical image translation

Two-dimensional monolayer salt nanostructures can spontaneously aggregate rather than dissolve in dilute aqueous solutions

It is well known that NaCl salt crystals can easily dissolve in dilute aqueous solutions at room temperature. Herein, we reported the first computational evidence of a novel salt nucleation behavior at room temperature, i.e., the spontaneous formation of two-dimensional (2D) alkali chloride crystalline/non-crystalline nanostructures in dilute aqueous solution under nanoscale confinement. Microsecond-scale classical molecular dynamics (MD) simulations showed that NaCl or LiCl, initially fully dissolved in confined water, can spontaneously nucleate into 2D monolayer nanostructures with either ordered or disordered morphologies. Notably, the NaCl nanostructures exhibited a 2D crystalline square-unit pattern, whereas the LiCl nanostructures adopted non-crystalline 2D hexagonal ring and/or zigzag chain patterns. These structural patterns appeared to be quite generic, regardless of the water and ion models used in the MD simulations. The generic patterns formed by 2D monolayer NaCl and LiCl nanostructures were also confirmed by ab initio MD simulations. The formation of 2D salt structures in dilute aqueous solution at room temperature is counterintuitive. Free energy calculations indicated that the unexpected spontaneous salt nucleation behavior can be attributed to the nanoscale confinement and strongly compressed hydration shells of ions. Supplementary files, including 6 movies, attached below

Rapid FRD determination for multiplexed fibre systems -- I. The quasi-near field model and its uncertainties

Focal Ratio Degradation (FRD) in fibres is a crucial factor to control in astronomical instruments in order to minimize light loss. As astronomical instrumentation has advanced, the integration of large populations of fibres has become common. However, determining FRD in multiplexed fibre systems has become a challenging and time-consuming task. The Integral Field Unit for the Fiber Arrayed Solar Optical Telescope (FASOT-IFU) represents the most densely arranged fibre-based IFU in a single unit. Due to the close packing of fibres in the V-groove of the slit end, measuring FRD is particularly challenging as the output spots are prone to overlapping with adjacent fibres. In this paper, a novel method based on the quasi-near field model is proposed to enable rapid FRD measurement in highly multiplexed fibre systems like IFUs and multi-object observation systems. The principle and uncertainties associated with the method are investigated. The method's validity is demonstrated by applying it to determine the FRD in FASOT-IFU, with the achieved FRD performance meeting the acceptable requirements of FASOT-IFU, where the output focal ratio primarily falls within the range of 5.0-7.0. The results indicate that the proposed method offers several advantages, including the simultaneous and rapid measurement of FRD in multiple fibres with high accuracy (error smaller than 0.35 in F-ratio). Furthermore, besides FRD, the method exhibits potential for extensive measurements of throughput, scrambling, and spectral analysis.Comment: 10 pages, 12 figures, submitted to MNRA

Identification and validation of a novel CD8+ T cell-associated prognostic model based on ferroptosis in acute myeloid leukemia

Acute myeloid leukemia (AML) is a highly aggressive cancer with great heterogeneity and variability in prognosis. Though European Leukemia Net (ELN) 2017 risk classification has been widely used, nearly half of patients were stratified to “intermediate” risk and requires more accurate classification via excavating biological features. As new evidence showed that CD8+ T cell can kill cancer cells through ferroptosis pathway. We firstly use CIBERSORT algorithm to divide AMLs into CD8+ high and CD8+ low T cell groups, then 2789 differentially expressed genes (DEGs) between groups were identified, of which 46 ferroptosis-related genes associated with CD8+ T cell were sorted out. GO, KEGG analysis and PPI network were conducted based on these 46 DEGs. By jointly using LASSO algorithm and Cox univariate regression, we generated a 6-gene prognostic signature comprising VEGFA, KLHL24, ATG3, EIF2AK4, IDH1 and HSPB1. Low-risk group shows a longer overall survival. We then validated the prognostic value of this 6-gene signature using two independent external datasets and patient sample collection dataset. We also proved that incorporation of the 6-gene signature obviously enhanced the accuracy of ELN risk classification. Finally, gene mutation analysis, drug sensitive prediction, GSEA and GSVA analysis were conducted between high-risk and low-risk AML patients. Collectively, our findings suggested that the prognostic signature based on CD8+ T cell-related ferroptosis genes can optimize the risk stratification and prognostic prediction of AML patients

Augmented reality technology enhanced science learning

Augmented reality (AR) is the technology which creates the interaction between the version generated by a computer and the real-world environment. The interaction includes the use of sound, visual element, or sensory stimuli, which gives the user a unique and immersive experience. Compared with other subjects of education, science has more interesting contents which are difficult to share with students. With many limitations, the science class in primary school always ends with videos or models which are based on students’ imagination. Regarding the ability of interaction with the virtual system, augmented reality is considered applying in science education and some of the developing applications are already tested in the primary school. Hence, this report also covers the development of an “QIMS” based application, which gives the idea of the science education improvement by using AR mediated technology. The application’s purpose is to develop an AR-integrated science trail for an inquiry-based curriculum, and students will learn science in an AR environment by using the “QIMS” framework. The educational “QIMS” system is about questioning, investigating, making and synthesising, which will be introduced later. In this case, a “QIMS” based AR integrated celery experiment is designed with the help of science teachers from the primary school. The whole process is about the system topic of the textbook science 5&6, and the learning objective is to identify and describe the water transport system by doing several experiments on the celery. Hence, this application called “Celery Lab” gives a new version of the traditional natural experiment lesson, which will be explained later in this report.Bachelor of Engineering (Mechanical Engineering

Fluorescence Studies on Hydrophobic Associations of Fluorocarbon-Modified Poly(acrylic acid) Solutions

ABSTRACT: Fluorocarbon-containing hydrophobically associating polymers have been synthesized by copolymerization of acrylic acid with a small amount of C8 fluorocarbon-containing methacrylate. The association behavior of the fluorocarbon-modified poly(acrylic acid) (FA) over a broad pH range has been investigated by a fluorescent probe technique and viscosity measurements. The copolymer has the strongest intermolecular association and maximum viscosity at the acidic condition of pH 5.5. Both pyrene and fluorocarbon-substituted pyrene (PyCOR f) are usable to detect this strong association and its dependences on both the fluorocarbon content and polymer concentration. Less acidic pH causes progressive disruptions of hydrophobic association, leading to a dramatic decrease in viscosity. At pH > 7, the stretched polymer chains reach a viscosity plateau much lower than the maximum viscosity but still higher than the viscosity of the poly(acrylic acid) homopolymer. This indicates that relatively weak associations are present. PyCORf, due to its high affinity to the fluorocarbon domains, is effective in monitoring the formation of this kind of weak association while pyrene fails to do so