Effect of Zr on the Microstructure and High-Temperature Phase Separation Evolution of SiOC Aerogels.

SiZrOC aerogels were synthesized through the pyrolysis of the zirconium source-doped SiOC system using zirconyl chloride octahydrate (ZrOCl2·8H2O) at temperatures ranging from 900 to 1300 °C. This study investigates the microstructure evolution and phase separation of SiOC and SiZrOC aerogels during the pyrolysis process. Upon pyrolysis, both aerogels exhibited a Si-O-C structure with a high thermal stability. The introduction of zirconium elements significantly enhanced the pore volume (3.20 cm3/g) and porosity (96.0%) and reduced the thermal conductivity (0.023 W·m-1·K-1) of the organic-inorganic precursor aerogel. Moreover, the three-dimensional pore structure was retained even under high-temperature pyrolysis conditions. SiZrOC-1100 displayed a high specific surface area of 273.52 m2/g, a high pore volume of 1.70 cm3/g, and a low thermal conductivity of 0.033 W·m-1·K-1. At high temperatures, the SiZrOC phase transformation produces tetragonal ZrO2, which inhibits the graphitization process of free carbon and the growth of SiC grains. Furthermore, the phase separation process of the SiOxCy matrix structure generated oxygen-rich SiOxC4-x units, while carbon-rich SiOxC4-x units were negligible below a pyrolysis temperature of 1200 °C. Between 900 and 1200 °C, SiZrOC is composed of amorphous SiOC, amorphous ZrO2, microcrystalline t-ZrO2, and free carbon phase. These findings provide valuable insights into the preparation of high-performance SiOC aerogels.

The heterogeneity of tumour immune microenvironment revealing the CRABP2/CD69 signature discriminates distinct clinical outcomes in breast cancer.

BACKGROUND: It has been acknowledged that the tumour immune microenvironment (TIME) plays a critical role in determining therapeutic responses and clinical outcomes in breast cancer (BrCa). Thus, the identification of the TIME features is essential for guiding therapy and prognostic assessment for BrCa. METHODS: The heterogeneous cellular composition of the TIME in BrCa by single-cell RNA sequencing (scRNA-seq). Two subtype-special genes upregulated in the tumour-rich subtype and the immune-infiltrating subtype were extracted, respectively. The CRABP2/CD69 signature was established based on CRABP2 and CD69 expression, and its predictive values for the clinical outcome and the neoadjuvant chemotherapy (NAT) responses were validated in multiple cohorts. Moreover, the oncogenic role of CRABP2 was explored in BrCa cells. RESULTS: Based on the heterogeneous cellular composition of the TIME in BrCa, the BrCa samples could be divided into the tumour-rich subtype and the immune-infiltrating subtype, which exhibited distinct prognosis and chemotherapeutic responses. Next, we extracted CRABP2 as the biomarker for the tumour-rich subtype and CD69 as the biomarker for the immune-infiltrating subtype. Based on the CRABP2/CD69 signature, BrCa samples were re-divided into three subtypes, and the CRABP2highCD69low subtype exhibited the worst prognosis and the lowest chemotherapeutic response, while the CRABP2lowCD69high subtype showed the opposite results. Furthermore, CARBP2 functioned as a novel oncogene in BrCa, which promoted tumour cell proliferation, migration, and invasion, and CRABP2 inhibition triggered the activation of cytotoxic T lymphocytes (CTLs). CONCLUSION: The CRABP2/CD69 signature is significantly associated with the TIME features and could effectively predict the clinical outcome. Also, CRABP2 is determined to be a novel oncogene, which could be a therapeutic target in BrCa.

Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-Ray.

Chest X-ray (CXR) is commonly performed as an initial investigation in COVID-19, whose fast and accurate diagnosis is critical. Recently, deep learning has a great potential in detecting people who are suspected to be infected with COVID-19. However, deep learning resulting with black-box models, which often breaks down when forced to make predictions about data for which limited supervised information is available and lack inter-pretability, still is a major barrier for clinical integration. In this work, we hereby propose a semantic-powered explainable model-free few-shot learning scheme to quickly and precisely diagnose COVID-19 with higher reliability and transparency. Specifically, we design a Report Image Explanation Cell (RIEC) to exploit clinically indicators derived from radiology reports as interpretable driver to introduce prior knowledge at training. Meanwhile, multi-task collaborative diagnosis strategy (MCDS) is developed to construct N-way K-shot tasks, which adopts a cyclic and collaborative training approach for producing better generalization performance on new tasks. Extensive experiments demonstrate that the proposed scheme achieves competitive results (accuracy of 98.91%, precision of 98.95%, recall of 97.94% and F1-score of 98.57%) to diagnose COVID-19 and other pneumonia infected categories, even with only 200 paired CXR images and radiology reports for training. Furthermore, statistical results of comparative experiments show that our scheme provides an interpretable window into the COVID-19 diagnosis to improve the performance of the small sample size, the reliability and transparency of black-box deep learning models. Our source codes will be released on https://github.com/AI-medical-diagnosis-team-of-JNU/SPEMFSL-Diagnosis-COVID-19.

A hybrid hemodynamic knowledge-powered and feature reconstruction-guided scheme for breast cancer segmentation based on DCE-MRI.

Automatically and accurately annotating tumor in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which provides a noninvasive in vivo method to evaluate tumor vasculature architectures based on contrast accumulation and washout, is a crucial step in computer-aided breast cancer diagnosis and treatment. However, it remains challenging due to the varying sizes, shapes, appearances and densities of tumors caused by the high heterogeneity of breast cancer, and the high dimensionality and ill-posed artifacts of DCE-MRI. In this paper, we propose a hybrid hemodynamic knowledge-powered and feature reconstruction-guided scheme that integrates pharmacokinetics prior and feature refinement to generate sufficiently adequate features in DCE-MRI for breast cancer segmentation. The pharmacokinetics prior expressed by time intensity curve (TIC) is incorporated into the scheme through objective function called dynamic contrast-enhanced prior (DCP) loss. It contains contrast agent kinetic heterogeneity prior knowledge, which is important to optimize our model parameters. Besides, we design a spatial fusion module (SFM) embedded in the scheme to exploit intra-slices spatial structural correlations, and deploy a spatial-kinetic fusion module (SKFM) to effectively leverage the complementary information extracted from spatial-kinetic space. Furthermore, considering that low spatial resolution often leads to poor image quality in DCE-MRI, we integrate a reconstruction autoencoder into the scheme to refine feature maps in an unsupervised manner. We conduct extensive experiments to validate the proposed method and show that our approach can outperform recent state-of-the-art segmentation methods on breast cancer DCE-MRI dataset. Moreover, to explore the generalization for other segmentation tasks on dynamic imaging, we also extend the proposed method to brain segmentation in DSC-MRI sequence. Our source code will be released on https://github.com/AI-medical-diagnosis-team-of-JNU/DCEDuDoFNet.

The Impact of Algorithmic Price Discrimination on Consumers' Perceived Betrayal.

With the development of artificial intelligence technology, data support is increasing in importance, as are problems such as information disclosure, algorithmic discrimination and the digital divide. Algorithmic price discrimination occurs when online retailers or platforms charge experienced consumers who are purchasing products on their online platforms higher prices than those charged to new consumers for the same products at the same time. The purpose of this paper is to investigate the impact of algorithmic price discrimination on consumers' perceived betrayal. This paper employed a field experimental method involving two studies. In total, 696 questionnaires were distributed to consumers: 310 for Study 1 and 386 for Study 2. The collected data were analyzed using variance analysis and process analysis methods and SPSS software. Our findings suggest (1) Increased algorithmic price discrimination leads to increased perceived betrayal. (2) Increased algorithmic price discrimination leads to lower perceived price fairness and therefore to increased perceived betrayal among consumers. (3) Higher perceived ease of use of online retailers decreases the impact of algorithmic price discrimination on consumers' perceived betrayal. We are a small group of researchers focusing on algorithmic price discrimination and integrating algorithmic discrimination into the consumer research field. Our research introduces the concept of consumer perceived betrayal to the field of artificial intelligence. We adopt a field experimental study to examine the impact of algorithmic price discrimination on consumers' perceived betrayal by introducing variables of perceived price fairness and perceived ease of use.

Effects of demineralization and devolatilization on fast pyrolysis behaviors and product characteristics of penicillin mycelial residues.

In this study, the effects of demineralization and devolatilization methods including of water washing (WW), torrefaction (TF), washing-torrefaction (WT) and hydrothermal treatment (HT) on the fast pyrolysis characteristics of penicillin mycelial residues were studied. The materials and pyrolysis products were characterized by analysis methods including of thermogravimetric (TG), gas chromatograph (GC), gas chromatography-mass spectrometry (GC-MS), x-ray diffractometer (XRD), fourier transform-infrared spectroscopy (FT-IR) and x-ray photoelectron spectroscopy (XPS), etc. The results showed WW increased the yields of tar and decreased the yields of pyrolysis biochar due to the removal of alkali and alkaline earth metals (AAEMs), while TF and HT showed opposite results due the devolatilization. XPS and FT-IR results proved that the conversion from aliphatic C-(C, H) to aromatic groups C-O-C and CO was the key point for improving the aromatization of biochar. Pretreatments increased the relative proportions of N-containing heterocyclic compounds and phenolic compounds, reduced the proportions of O-containing heterocyclic compounds in pyrolysis tar. And TF and HT could eliminate the residual antibiotic and satisfy the principle of AMR harmless disposal.

Study on physicochemical characteristics, solidification and utilisation of tannery sludge gasification ash.

Gasification is an attractive method for tannery sludge (TS) disposal because of its advantages: volume reduction, stabilisation, harmlessness, and energy recovery. TS reduction ash (AR) and TS oxidation ash (AO), simulated from a downdraft fixed bed gasifier (DFBG) and an updraft fixed bed gasifier (UFBG), were investigated on their physicochemical characteristics, solidification behaviour, and value-added utilisation. Results showed that the main mineral matters in AR and AO consisted of Fe-oxids and Fe-Cr compounds, and the DFBG was more suitable for TS gasification than the UFBG because of the lower content of Cr(Ⅵ) in AR. With the addition of waste glass bottles (WGB), the ash fusion temperatures (AFTs) and leaching concentrations of heavy metals in AR and AO decreased significantly, and the heavy metals in AR and AO were successfully immobilised by the wrapping effect of the molten WGB. Moreover, gasification ash, as an auxiliary material for rock wool, reduced the AFTs and viscosity coefficient of the main chemical compositions in rock wool. With the addition of AR, the occurrence of Fe-containing compounds and the extremely low risk of leaching toxicity of heavy metals were observed. The maximum addition proportion of gasification ash was dependent on the maximum content of Fe2O3 allowed in the raw materials of rock wool, and its addition ratio must be below 15%.

Steam gasification of land, coastal zone and marine biomass by thermal gravimetric analyzer and a free-fall tubular gasifier: Biochars reactivity and hydrogen-rich syngas production.

The steam gasification properties and kinetics, products distribution and syngas composition derived from land, coastal zone and marine biomass have been studied by TGA and free-fall tubular gasifier. Volume model, shrinking core model and random pore model were applied to describe the reaction kinetics. The influence of temperature and fuel types on steam gasification in a free-fall tubular gasifier were clarified simultaneously. Results showed that gasification reactivity of reed (Re) and Sargassum horneri (Sh) chars were better than that of corn stalks (Cs) char, which mostly determined by its carbonaceous structure and the varying inorganic contents. RPM model was applied successfully to corresponding to the experimental data. Bench scale reactor test found that the steam gasification of Re gave the largest amount of gaseous product than Sh and Cs, while no liquidus formation in Sh. An increase in the temperature during gasification process boosted produced sharply total gas production yield, more yield of H2 and CO2 and less CO and CH4 from different biomass.