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Intrahepatic cholangiocarcinoma is a rare disease associated with a poor prognosis, primarily due to early recurrence and metastasis. An important feature of this condition is microvascular invasion (MVI). However, current predictive models based on imaging have limited efficacy in this regard. This study employed a random forest model to construct a predictive model for MVI identification and uncover its biological basis. Single-cell transcriptome sequencing, whole exome sequencing, and proteome sequencing were performed. The area under the curve of the prediction model in the validation set was 0.93. Further analysis indicated that MVI-associated tumor cells exhibited functional changes related to epithelial–mesenchymal transition and lipid metabolism due to alterations in the nuclear factor-kappa B and mitogen-activated protein kinase signaling pathways. Tumor cells were also differentially enriched for the interleukin-17 signaling pathway. There was less infiltration of SLC30A1+ CD8+ T cells expressing cytotoxic genes in MVI-associated intrahepatic cholangiocarcinoma, whereas there was more infiltration of myeloid cells with attenuated expression of the major histocompatibility complex II pathway. Additionally, MVI-associated intercellular communication was closely related to the SPP1–CD44 and ANXA1–FPR1 pathways. These findings resulted in a brilliant predictive model and fresh insights into MVI. [ABSTRACT FROM AUTHOR]

Hemodynamic parameters can provide surveillance for the risk of complication of abdominal aortic aneurysms following endovascular aneurysm repair (EVAR). However, obtaining hemodynamic parameters through computational fluid dynamics (CFD) has disadvantages of complex operation and high computational costs. Recently proposed physics-informed neural networks offer novel solutions to solve these issues by leveraging fundamental physical conservation principles of fluid dynamics. Based on cardiovascular point datasets, we further propose an integration algorithm combining physics-informed PointNet and quadratic residual networks (PIPN-QN) that is capable of mapping sparse point clouds to four-dimensional hemodynamic parameters. The implemented workflow includes generating point cloud datasets through CFD simulation and dynamically reproducing the three-dimensional flow field in the spatial and temporal dimensions through deep learning. Compared with physics-informed PointNet (PIPN), the PIPN-QN reduces the mean square error of pressure and wall shear stress by around 32.1% and 33.1% and anticipates hemodynamic parameters in less than 2 s (14 400 times faster than CFD). To address the challenge of big data requirements, we quantify the universal flow field using a reduced number of supervision points, as opposed to the large number of point clouds generated from the CFD simulation. The PIPN-QN can meet the real-time hemodynamic parameters obtained from patients with abdominal aortic aneurysms following EVAR with higher accuracy, faster speed, and lower training costs. [ABSTRACT FROM AUTHOR]

The proliferation of COVID-19 fake news on social media poses a severe threat to the health information ecosystem. We show that affective computing can make significant contributions to combat this infodemic. Given that fake news is often presented with emotional appeals, we propose a new perspective on the role of emotion in the attitudes, perceptions, and behaviors of the dissemination of information. We study emotions in conjunction with fake news, and explore different aspects of their interaction. To process both emotion and ‘falsehood’ based on the same set of data, we auto-tag emotions on existing COVID-19 fake news datasets following an established emotion taxonomy. More specifically, based on the distribution of seven basic emotions (e.g., Happiness, Like, Fear, Sadness, Surprise, Disgust, Anger), we find across domains and styles that COVID-19 fake news is dominated by emotions of Fear (e.g., of coronavirus), and Disgust (e.g., of social conflicts). In addition, the framing of fake news in terms of gain-versus-loss reveals a close correlation between emotions, perceptions, and collective human reactions. Our analysis confirms the significant role of emotion Fear in the spreading of the fake news, especially when contextualized in the loss frame. Our study points to a future direction of incorporating emotion footprints in models of automatic fake news detection, and establishes an affective computing approach to information quality in general and fake news detection in particular. [ABSTRACT FROM AUTHOR]

A simulated tobacco cutting method based on strip image was developed by incorporating digital image processing technology, and a simulated cut strip length index was introduced. The influence of the rotational interval of strip image on the results of simulated cutting was investigated, and the accuracy of the developed method was verified by paper cutting experiments. The relationships between simulated cut tobacco length and strip's shapes were analyzed, the differences of the simulated cut tobacco length between strips of different sizes were clarified, and the application of the tobacco cutting method to the characterization of the tobacco strip structure was studied. The results showed that: 1) When the strip image was rotated with an interval of 30°, the deviation of the simulated cut tobacco length relative to 1° was 2.84%, and the calculation time reduced by 96.70%. 2) The Pearson correlation of the cut paper length distribution between the simulated cutting and the actual paper cutting was 0.949 3, and the detection deviation of the average length was 6.95%. 3) The simulated cut tobacco length was multivariately non-linearly correlated to the area and length of tobacco strip with a correlation coefficient of 0.950 6. 4) The average lengths of simulated cutting produced from medium and small size strips, scraps and dust showed an approximately 2-fold gradient decreasing trend. 5) The classification limits of the lengths of the simulated cut tobacco produced from large and medium strips, medium and small strips, and small strips, scrap and dust were 22.23, 11.80 and 5.39 mm, respectively. There was no significant difference in the detection results of the tobacco strip structure between the developed method and the sieving method, with the correlation coefficient of 0.998 2, indicating a strong correlation. This method provides support for predicting the cut length of strips. [ABSTRACT FROM AUTHOR]

Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses. [ABSTRACT FROM AUTHOR]

Copper-based catalyst is uniquely positioned to catalyze the hydrocarbon formations through electrochemical CO2 reduction. The catalyst design freedom is limited for alloying copper with H-affinitive elements represented by platinum group metals because the latter would easily drive the hydrogen evolution reaction to override CO2 reduction. We report an adept design of anchoring atomically dispersed platinum group metal species on both polycrystalline and shape-controlled Cu catalysts, which now promote targeted CO2 reduction reaction while frustrating the undesired hydrogen evolution reaction. Notably, alloys with similar metal formulations but comprising small platinum or palladium clusters would fail this objective. With an appreciable amount of CO-Pd1 moieties on copper surfaces, facile CO* hydrogenation to CHO* or CO-CHO* coupling is now viable as one of the main pathways on Cu(111) or Cu(100) to selectively produce CH4 or C2H4 through Pd-Cu dual-site pathways. The work broadens copper alloying choices for CO2 reduction in aqueous phases. The inclusion of platinum-group metals for CO2 reduction electrocatalyst design may trigger the unwanted hydrogen evolution reaction. However, here the authors show that single-atom Pd and Pt on facet-selective Cu can selectively boost CO2 to CH4 or C2H4 conversion through dual-site pathways. [ABSTRACT FROM AUTHOR]

Hybrid organic‐inorganic heterogeneous catalytic interfaces, where traditional catalytic materials are modified with self‐assembled monolayers (SAMs), create promising features to control a wide range of catalytic processes through the design of dual organic‐inorganic active sites and the induced confinement effect. To provide a fundamental insight, we investigated CO2 electroreduction into valuable C2 chemicals (CO2RR‐to‐C2) over SAM‐modulated Cu. Our theoretical results show that 1/4 monolayer aminothiolates improve the stability, activity and selectivity of CO2RR‐to‐C2 by: (1) decreasing surface energy to suppress surface reconstruction; (2) facilitating CO2 activation and C−C coupling through dual organic‐inorganic (i. e. −NH, Cu) active sites; (3) promoting C−C coupling via confinement effects that enlarge the adsorption energy difference between CO* and COH*; (4) inducing local electric fields to Cu surface and changing its dipole moment and polarizability to be in favor of C−C coupling under electrode/electrolyte interfacial electric field. [ABSTRACT FROM AUTHOR]

Hybrid organic‐inorganic heterogeneous catalytic interfaces, where traditional catalytic materials are modified with self‐assembled monolayers (SAMs), create promising features to control a wide range of catalytic processes through the design of dual organic‐inorganic active sites and the induced confinement effect. To provide a fundamental insight, we investigated CO2 electroreduction into valuable C2 chemicals (CO2RR‐to‐C2) over SAM‐modulated Cu. Our theoretical results show that 1/4 monolayer aminothiolates improve the stability, activity and selectivity of CO2RR‐to‐C2 by: (1) decreasing surface energy to suppress surface reconstruction; (2) facilitating CO2 activation and C−C coupling through dual organic‐inorganic (i. e. −NH, Cu) active sites; (3) promoting C−C coupling via confinement effects that enlarge the adsorption energy difference between CO* and COH*; (4) inducing local electric fields to Cu surface and changing its dipole moment and polarizability to be in favor of C−C coupling under electrode/electrolyte interfacial electric field. [ABSTRACT FROM AUTHOR]

Genre characterizes a document differently from a subject that has been the focus of most document retrieval and classification applications. This work hypothesizes a close interaction between syntactic variation and genre differentiation by introspecting stylistic cues in functional and structural aspects beyond word level. It has engineered 14 syntactic feature sets of internal representations for genre classification through Machine Learning devices. Experiment results show significant superiority of fusing structural and lexical features for genre classification (F∆max. = 9.2%, sig. = 0.001), suggesting the effectiveness of incorporating syntactic cues for genre discrimination. In addition, the PCA analysis reports the noun phrases (NP) as the most principle component (66%) for genre variation and prepositional phrases (PP) the second. Particularly, noun phrases with dominant structures of prepositional complements and pronouns functioning as a subject are most effective for identifying printed texts of high formality, while prepositional phrases are useful for identifying speeches of low formality. Error analysis suggests that the phrasal features are particularly useful for classifying four groups of genre classes, i.e. unscripted speech, fiction, news reports, and academic writing, all distributed with distinct structural characteristics, and they demonstrate an incremental degree of formality in the continuum of language complexity. [ABSTRACT FROM AUTHOR]

In their Communication, M. Wan et al. demonstrated that aminothiolate SAM-modulated Cu catalyst could improve the activity and selectivity of CO SB 2 sb RR-to-C SB 2 sb using DFT calculations. Keywords: Density function calculations; Electrochemistry; Self-assembly EN Density function calculations Electrochemistry Self-assembly 1 1 1 02/21/22 20220218 NES 220218 B The Front Cover b shows carbon dioxide reduction reaction (CO SB 2 sb RR) to C SB 2 sb (e.g., ethylene, ethanol) driven by aminothiolate self-assembled monolayer (SAM)-modulated copper (Cu) catalysts. The confined space effect induced by the high surface coverage of SAM tunes the binding energy of the key intermediate CO SP * sp on the inorganic Cu site, enlarges the binding energy difference between CO SP * sp and COH SP * sp , and consequently, further promotes carbon-carbon coupling. [Extracted from the article]

Metallic catalyst modification by organic ligands is an emerging catalyst design in enhancing the activity and selectivity of electrocatalytic carbon dioxide (CO 2 ) reactive capture and reduction to value-added fuels. However, a lack of fundamental science on how these ligand-metal interfaces interact with CO 2 and key intermediates under working conditions has resulted in a trial-and-error approach for experimental designs. With the aid of density functional theory calculations, we provided a comprehensive mechanism study of CO 2 reduction to multicarbon products over aminothiolate-coated copper (Cu) catalysts. Our results indicate that the CO 2 reduction performance was closely related to the alkyl chain length, ligand coverage, ligand configuration, and Cu facet. The aminothiolate ligand-Cu interface significantly promoted initial CO 2 activation and lowered the activation barrier of carbon-carbon coupling through the organic (nitrogen (N)) and inorganic (Cu) interfacial active sites. Experimentally, the selectivity and partial current density of the multicarbon products over aminothiolate-coated Cu increased by 1.5-fold and 2-fold, respectively, as compared to the pristine Cu at -1.16 V RHE , consistent with our theoretical findings. This work highlights the promising strategy of designing the ligand-metal interface for CO 2 reactive capture and conversion to multicarbon products.