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The generation of radiological results from image data represents a pivotal aspect of medical image analysis. The latest iteration of ChatGPT-4, a large multimodal model that integrates both text and image inputs, including dermatoscopy images, histology images, and X-ray images, has attracted considerable attention in the field of radiology. To further investigate the performance of ChatGPT-4 in medical image recognition, we examined the ability of ChatGPT-4 to recognize credible osteosarcoma X-ray images. The results demonstrated that ChatGPT-4 can more accurately diagnose bone with or without significant space-occupying lesions but has a limited ability to differentiate between malignant lesions in bone compared to adjacent normal tissue. Thus far, the current capabilities of ChatGPT-4 are insufficient to make a reliable imaging diagnosis of osteosarcoma. Therefore, users should be aware of the limitations of this technology. [ABSTRACT FROM AUTHOR]

Abstract Background 18F-FDG PET/CT is a key molecular imaging modality to noninvasively assess and differentiate benign and malignant cardiac tumors. However, few benign cardiac tumors can be characterized by increased 18F-FDG uptake, which makes differential diagnosis difficult. This study sought to retrospectively evaluate whether combined 18F-FDG PET/CT with thoracic contrast-enhanced CT (CECT) helps in assessing primary cardiac tumors in adult patients, compared with CECT or PET/CT alone. Methods Forty-six consecutive patients who were diagnosed as primary cardiac tumors were enrolled. All patients underwent 18F-FDG PET/CT followed by thoracic CECT before biopsy or surgery. Visual qualitative interpretation and quantitative analysis were performed, and diagnostic performance was evaluated. Results More than half (16/29) of benign tumors exhibited with mild 18F-FDG uptake. There were significant differences in 18F-FDG uptake and the degree of absolute enhancement between benign and malignant tumors (P < 0.001). The combination of two modalities improved the specificity from 79 to 93%, the positive predictive value from 73 to 89%, and the accuracy of diagnosis from 85 to 93%. There were significant differences between PET/CT alone or thoracic CECT alone and combined modalities (P = 0.034 and P = 0.026, respectively). The combination with the optimal SUVmax cutoff value generated 94% sensitivity, 100% specificity, 97% negative predictive values, 100% positive predictive values, and 98% accuracy rates. Conclusions Combining 18F-FDG PET/C with thoracic CECT significantly improved specificity and accuracy compared to CECT or PET/CT alone in detecting tumors. This combination of diagnostic imaging is effective in differentiating malignant from benign masses.

High-frequency imaging characteristics allow a geostationary satellite (GSS) to capture the diurnal variation in vegetation canopy reflectance spectra, which is of very important practical significance for monitoring vegetation via remote sensing (RS). However, the observation angle and solar angle of high-frequency GSS RS data usually differ, and the differences in bidirectional reflectance from the reflectance spectra of the vegetation canopy are significant, which makes it necessary to normalize angles for GSS RS data. The BRDF (Bidirectional Reflectance Distribution Function) prototype library is effective for the angle normalization of RS data. However, its spatiotemporal applicability and error propagation are currently unclear. To resolve this problem, we herein propose a synthetic angle normalization model (SANM) for RS vegetation canopy reflectance; this model exploits the GSS imaging characteristics, whereby each pixel has a fixed observation angle. The established model references a topographic correction method for vegetation canopies based on path-length correction, solar zenith angle normalization, and the Minnaert model. It also considers the characteristics of diurnal variations in vegetation canopy reflectance spectra by setting the time window. Experiments were carried out on the eight Geostationary Ocean Color Imager (GOCI) images obtained on 22 April 2015 to validate the performance of the proposed SANM. The results show that SANM significantly improves the phase-to-phase correlation of the GOCI band reflectance in the morning time window and retains the instability of vegetation canopy spectra in the noon time window. The SANM provides a preliminary solution for normalizing the angles for the GSS RS data and makes the quantitative comparison of spatiotemporal RS data possible.

The heavy fermion physics is dictated by subtle competing exchange interactions, posing a challenge to their understanding. One-dimensional (1D) Kondo lattice model has attracted special attention in theory, because of its exact solvability and expected unusual quantum criticality. However, such experimental material systems are extremely rare. Here, we demonstrate the realization of quasi-1D Kondo lattice behavior in a monolayer van der Waals crystal NbSe2, that is driven into a stripe phase via Se-deficient line defects. Spectroscopic imaging scanning tunneling microscopy measurements and first-principles calculations indicate that the stripe-phase NbSe2 undergoes a novel charge-density wave transition, creating a matrix of local magnetic moments. The Kondo lattice behavior is manifested as a Fano resonance at the Fermi energy that prevails the entire film with a high Kondo temperature. Importantly, coherent Kondo screening occurs only in the direction of the stripes. Upon approaching defects, the Fano resonance exhibits prominent spatial 1D oscillations along the stripe direction, reminiscent of Kondo holes in a quasi-1D Kondo lattice. Our findings provide a platform for exploring anisotropic Kondo lattice behavior in the monolayer limit. The realization of heavy-fermion physics in van der Waals materials with d-electrons has attracted attention recently. Here the authors present evidence for a quasi-1D Kondo lattice in monolayer NbSe2, driven into a stripe phase by Se-deficient line defects created during growth. [ABSTRACT FROM AUTHOR]

The new requirements of trial-centered litigation in China are a concrete manifestation of the reform of the rule of law under Xi Jinping's new socialist era. The effective implementation of judicial appraisal lays a solid foundation for the correct identification of facts, accurate conviction, and sentencing, and technically, it should guarantee a fair trial. However, in practice, judicial appraisal opinion carries the risk of a diminished trial that deviates from the trial-centered requirement to substantiate a charge and can easily lead to unjust cases. This paper seeks to discuss the system of identification of property loss involved in criminal proceedings, which is implemented by a third-party appraisal institution entrusted by the investigation agency, and highlights the fact that the third-party appraisal institutions in judicial practice are currently reliant on cooperative thinking, but countenance the lack of a mechanism for effective accountability, and the court's propensity for their support. This leads to the verification components of the trial being depreciated, which may lessen the validity of the appraisal opinion (substantiation). In response to this problem, the author hopes to provide some suggestions to assist third-party appraisal agencies in the completion of judicial appraisal and achievement of their trial-centered reform goal by rationalizing the relationship between the investigation agency and identification institution, establishing a hierarchical accountability mechanism, and improving the rules governing testimony in court.