An Economic Perspective on the Intergenerational Transmission of Wealth Inequality

Intergenerational transmission of wealth is a long-standing component of society. With the current accelerated economic development, the forms of wealth transmission and the ways in which it affects individuals’ lives have gradually become more complicated. In this article, we explore the economic performance and basic flow patterns of intergenerational transmission. We first discuss the key factors of personal and family wealth accumulation. We then consider how social performance affects the phenomenon of intergenerational transmission and the macro-channels of the current transmission mode. Finally, while intergenerational transmission is widespread in society, its importance has not attracted widespread attention from socioeconomic researchers and this paper makes suggestions for further study of the phenom ena. Our main conclusion is that in current society, intergenerational transmission both directly and indirectly influences the lives of members of society in multiple ways, such as through income, employment and education. If a basic understanding of the phenomenon of intergenerational transmission can be established, it will assist people in making relevant decisions more scientifically and allow them to have a fairer life experience

PathAsst: Redefining Pathology through Generative Foundation AI Assistant for Pathology

As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, with significant applications in natural image interpretation. However, the field of pathology has largely remained untapped in this regard, despite the growing need for accurate, timely, and personalized diagnostics. To bridge the gap in pathology MLLMs, we present the PathAsst in this study, which is a generative foundation AI assistant to revolutionize diagnostic and predictive analytics in pathology. To develop PathAsst, we collect over 142K high-quality pathology image-text pairs from a variety of reliable sources, including PubMed, comprehensive pathology textbooks, reputable pathology websites, and private data annotated by pathologists. Leveraging the advanced capabilities of ChatGPT/GPT-4, we generate over 180K instruction-following samples. Furthermore, we devise additional instruction-following data, specifically tailored for the invocation of the pathology-specific models, allowing the PathAsst to effectively interact with these models based on the input image and user intent, consequently enhancing the model's diagnostic capabilities. Subsequently, our PathAsst is trained based on Vicuna-13B language model in coordination with the CLIP vision encoder. The results of PathAsst show the potential of harnessing the AI-powered generative foundation model to improve pathology diagnosis and treatment processes. We are committed to open-sourcing our meticulously curated dataset, as well as a comprehensive toolkit designed to aid researchers in the extensive collection and preprocessing of their own datasets. Resources can be obtained at https://github.com/superjamessyx/Generative-Foundation-AI-Assistant-for-Pathology.Comment: 13 pages, 5 figures, conferenc

Self-assembling supramolecular dendrimer nanosystem for PET imaging of tumors

Bioimaging plays an important role in cancer diagnosis and treatment. However, imaging sensitivity and specificity still constitute key challenges. Nanotechnology-based imaging is particularly promising for overcoming these limitations because nanosized imaging agents can specifically home in on tumors via the "enhanced permeation and retention" (EPR) effect, thus resulting in enhanced imaging sensitivity and specificity. Here, we report an original nanosystem for positron emission tomography (PET) imaging based on an amphiphilic dendrimer, which bears multiple PET reporting units at the terminals. This dendrimer is able to self-assemble into small and uniform nanomicelles, which accumulate in tumors for effective PET imaging. Benefiting from the combined dendrimeric multivalence and EPR-mediated passive tumor targeting, this nanosystem demonstrates superior imaging sensitivity and specificity, with up to 14-fold increased PET signal ratios compared with the clinical gold reference 2-fluorodeoxyglucose ([18F]FDG). Most importantly, this dendrimer system can detect imaging-refractory low-glucose-uptake tumors that are otherwise undetectable using [18F]FDG. In addition, it is endowed with an excellent safety profile and favorable pharmacokinetics for PET imaging. Consequently, this dendrimer nanosystem constitutes an effective and promising approach for cancer imaging. Our study also demonstrates that nanotechnology based on self-assembling dendrimers provides a fresh perspective for biomedical imaging and cancer diagnosis

Improving Typical Urban Land-Use Classification with Active-Passive Remote Sensing and Multi-Attention Modules Hybrid Network: A Case Study of Qibin District, Henan, China

The study of high-precision land-use classification is essential for the sustainable development of land resources. This study addresses the problem of classification errors in optical remote-sensing images under high surface humidity, cloud cover, and hazy weather. The synthetic aperture radar (SAR) images are sensitive to soil moisture, and the microwave can penetrate clouds, haze, and smoke. By using both the active and passive remote-sensing data, the Sentinel-1A SAR and Sentinel-2B multispectral (MS) images are combined synergistically. The full-band data combining the SAR + MS + spectral indexes is thus constructed. Based on the high dimensionality and heterogeneity of this data set, a new framework (MAM-HybridNet) based on two-dimensional (2D) and three-dimensional (3D) hybrid convolutional neural networks combined with multi-attention modules (MAMs) is proposed for improving the accuracy of land-use classification in cities with high surface humidity. In addition, the same training samples supported by All bands data (SAR + MS + spectral index) are selected and compared with k-Nearest Neighbors (KNN), support vector machine (SVM), 2D convolutional neural networks, 3D convolutional neural networks, and hybridSN classification models to verify the accuracy of the proposed classification model. The results show that (1) fusion classification based on Sentinel-2B MSI and Sentinel-1A SAR data produce an overall accuracy (OA) of 95.10%, a kappa coefficient (KC) of 0.93, and an average accuracy (AA) of 92.86%, which is better than the classification results using Sentinel-2B MSI and Sentinel-1A SAR images separately. (2) The classification accuracy improves upon adding the spectral index, and the OA, KC, and AA improve by 3.77%, 0.05, and 5.5%, respectively. (3) With the support of full-band data, the algorithm proposed herein produces better results than other classification algorithms, with an OA of 98.87%, a KC of 0.98, and an AA of 98.36%. These results indicate that the synergistic effect of active-passive remote-sensing data improves land-use classification. Additionally, the results verify the effectiveness of the proposed deep-learning classification model for land-use classification

Improving Typical Urban Land-Use Classification with Active-Passive Remote Sensing and Multi-Attention Modules Hybrid Network: A Case Study of Qibin District, Henan, China

The study of high-precision land-use classification is essential for the sustainable development of land resources. This study addresses the problem of classification errors in optical remote-sensing images under high surface humidity, cloud cover, and hazy weather. The synthetic aperture radar (SAR) images are sensitive to soil moisture, and the microwave can penetrate clouds, haze, and smoke. By using both the active and passive remote-sensing data, the Sentinel-1A SAR and Sentinel-2B multispectral (MS) images are combined synergistically. The full-band data combining the SAR + MS + spectral indexes is thus constructed. Based on the high dimensionality and heterogeneity of this data set, a new framework (MAM-HybridNet) based on two-dimensional (2D) and three-dimensional (3D) hybrid convolutional neural networks combined with multi-attention modules (MAMs) is proposed for improving the accuracy of land-use classification in cities with high surface humidity. In addition, the same training samples supported by All bands data (SAR + MS + spectral index) are selected and compared with k-Nearest Neighbors (KNN), support vector machine (SVM), 2D convolutional neural networks, 3D convolutional neural networks, and hybridSN classification models to verify the accuracy of the proposed classification model. The results show that (1) fusion classification based on Sentinel-2B MSI and Sentinel-1A SAR data produce an overall accuracy (OA) of 95.10%, a kappa coefficient (KC) of 0.93, and an average accuracy (AA) of 92.86%, which is better than the classification results using Sentinel-2B MSI and Sentinel-1A SAR images separately. (2) The classification accuracy improves upon adding the spectral index, and the OA, KC, and AA improve by 3.77%, 0.05, and 5.5%, respectively. (3) With the support of full-band data, the algorithm proposed herein produces better results than other classification algorithms, with an OA of 98.87%, a KC of 0.98, and an AA of 98.36%. These results indicate that the synergistic effect of active-passive remote-sensing data improves land-use classification. Additionally, the results verify the effectiveness of the proposed deep-learning classification model for land-use classification

Comparison of Typical Alpine Lake Surface Elevation Variations and Different Driving Forces by Remote Sensing Altimetry Method

Alpine lakes play a significant role in improving watershed ecology, adjusting water storage, and managing regional water resources. They are also a valuable freshwater reservoir, flood storage, and species gene pool in Central Asia. This article validated the accuracy of the CryoSat-2 footprints altimetry dataset for the Lake Bosten and Lake Issyk-Kul ranges. The time series for the surface elevations of the Central Asian alpine lakes Karakul and Chatyrkul were established, based on footprints altimetry data. The lake hydrological drivers were analyzed using remote sensing meteorological reanalysis data of the lake basins. The following main conclusions were reached. The CryoSat-2 footprints altimetry dataset has high confidence in lake surface elevation monitoring. Compared with Hydroweb monitoring results, the agreement between the monitoring results in the range between Lake Bosten and Lake Issyk-Kul are 0.96 and 0.84. The surface elevation of Lake Karakul shows an overall increasing trend with a variation rate of +7.7 cm/yr from 2010 to 2020, which has a positive correlation with the temperature in the basin. This indicates that the increased temperature, which results in the increased snow and ice meltwater in the basin, is the main driving force of the increased lake evolution. The lake surface elevation of Lake Chatyrkul shows an overall decreasing trend, with a variation rate of −9.9 cm/yr from 2010 to 2020, which has a negative correlation with the temperature in the basin. This suggests that Lake Chatyrkul is poorly recharged by snow and ice meltwater. The main driving force of its evolution is the increased evaporative output of the lake due to the increase in temperature. These conclusions prove that temperature and alpine glacial variability within the lake basin play an important role in lake surface elevation variations in alpine regions of Central Asia

Modular Self‐Assembling Dendrimer Nanosystems for Magnetic Resonance And Multimodality Imaging of Tumors

Bioimaging is a powerful tool for diagnosing tumors but remains limited in terms of sensitivity and specificity. Nanotechnology‐based imaging probes able to accommodate abundant imaging units with different imaging modalities are particularly promising for overcoming these limitations. In addition, the nanosized imaging agents can specifically increase the contrast of tumors by exploiting the enhanced permeability and retention effect. We performed a proof‐of‐concept study on pancreatic cancer to demonstrate the use of modular amphiphilic dendrimer‐based nanoprobes for magnetic resonance imaging (MRI) or MR/near‐infrared fluorescence (NIRF) multimodality imaging of tumors. Specifically, self‐assembly of an amphiphilic dendrimer bearing multiple Gd 3+ contrast units at its terminals, generated a nanomicellar agent exhibiting favorable relaxivity for MRI with a good safety profile. MRI revealed an up to 2‐fold higher contrast enhancement in tumors than in normal muscle. Encapsulating the NIRF dye within the core of the nanoprobe yielded an MR/NIRF bimodal imaging agent for tumor detection that was efficient both for MRI, at Gd 3+ concentrations 1/10 the standard clinical dose, and for NIRF imaging, allowing over two‐fold stronger fluorescence intensities. These self‐assembling dendrimer nanosystems thus constitute effective probes for MRI and MR/NIRF multimodality imaging, and offer a promising nanotechnology platform for elaborating multimodality imaging probes in biomedical applications. This article is protected by copyright. All rights reserve