RSGPT: A Remote Sensing Vision Language Model and Benchmark

The emergence of large-scale large language models, with GPT-4 as a prominent example, has significantly propelled the rapid advancement of artificial general intelligence and sparked the revolution of Artificial Intelligence 2.0. In the realm of remote sensing (RS), there is a growing interest in developing large vision language models (VLMs) specifically tailored for data analysis in this domain. However, current research predominantly revolves around visual recognition tasks, lacking comprehensive, large-scale image-text datasets that are aligned and suitable for training large VLMs, which poses significant challenges to effectively training such models for RS applications. In computer vision, recent research has demonstrated that fine-tuning large vision language models on small-scale, high-quality datasets can yield impressive performance in visual and language understanding. These results are comparable to state-of-the-art VLMs trained from scratch on massive amounts of data, such as GPT-4. Inspired by this captivating idea, in this work, we build a high-quality Remote Sensing Image Captioning dataset (RSICap) that facilitates the development of large VLMs in the RS field. Unlike previous RS datasets that either employ model-generated captions or short descriptions, RSICap comprises 2,585 human-annotated captions with rich and high-quality information. This dataset offers detailed descriptions for each image, encompassing scene descriptions (e.g., residential area, airport, or farmland) as well as object information (e.g., color, shape, quantity, absolute position, etc). To facilitate the evaluation of VLMs in the field of RS, we also provide a benchmark evaluation dataset called RSIEval. This dataset consists of human-annotated captions and visual question-answer pairs, allowing for a comprehensive assessment of VLMs in the context of RS

Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification

To better address challenging issues of the irregularity and inhomogeneity inherently present in 3D point clouds, researchers have been shifting their focus from the design of hand-craft point feature towards the learning of 3D point signatures using deep neural networks for 3D point cloud classification. Recent proposed deep learning based point cloud classification methods either apply 2D CNN on projected feature images or apply 1D convolutional layers directly on raw point sets. These methods cannot adequately recognize fine-grained local structures caused by the uneven density distribution of the point cloud data. In this paper, to address this challenging issue, we introduced a density-aware convolution module which uses the point-wise density to re-weight the learnable weights of convolution kernels. The proposed convolution module is able to fully approximate the 3D continuous convolution on unevenly distributed 3D point sets. Based on this convolution module, we further developed a multi-scale fully convolutional neural network with downsampling and upsampling blocks to enable hierarchical point feature learning. In addition, to regularize the global semantic context, we implemented a context encoding module to predict a global context encoding and formulated a context encoding regularizer to enforce the predicted context encoding to be aligned with the ground truth one. The overall network can be trained in an end-to-end fashion with the raw 3D coordinates as well as the height above ground as inputs. Experiments on the International Society for Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark demonstrated the superiority of the proposed method for point cloud classification. Our model achieved a new state-of-the-art performance with an average F1 score of 71.2% and improved the performance by a large margin on several categories

Genetic Evolution and Molecular Selection of the HE Gene of Influenza C Virus

Influenza C virus (ICV) was first identified in humans and swine, but recently also in cattle, indicating a wider host range and potential threat to both the livestock industry and public health than was originally anticipated. The ICV hemagglutinin-esterase (HE) glycoprotein has multiple functions in the viral replication cycle and is the major determinant of antigenicity. Here, we developed a comparative approach integrating genetics, molecular selection analysis, and structural biology to identify the codon usage and adaptive evolution of ICV. We show that ICV can be classified into six lineages, consistent with previous studies. The HE gene has a low codon usage bias, which may facilitate ICV replication by reducing competition during evolution. Natural selection, dinucleotide composition, and mutation pressure shape the codon usage patterns of the ICV HE gene, with natural selection being the most important factor. Codon adaptation index (CAI) and relative codon deoptimization index (RCDI) analysis revealed that the greatest adaption of ICV was to humans, followed by cattle and swine. Additionally, similarity index (SiD) analysis revealed that swine exerted a stronger evolutionary pressure on ICV than humans, which is considered the primary reservoir. Furthermore, a similar tendency was also observed in the M gene. Of note, we found HE residues 176, 194, and 198 to be under positive selection, which may be the result of escape from antibody responses. Our study provides useful information on the genetic evolution of ICV from a new perspective that can help devise prevention and control strategies

AutoEncoding Tree for City Generation and Applications

City modeling and generation have attracted an increased interest in various applications, including gaming, urban planning, and autonomous driving. Unlike previous works focused on the generation of single objects or indoor scenes, the huge volumes of spatial data in cities pose a challenge to the generative models. Furthermore, few publicly available 3D real-world city datasets also hinder the development of methods for city generation. In this paper, we first collect over 3,000,000 geo-referenced objects for the city of New York, Zurich, Tokyo, Berlin, Boston and several other large cities. Based on this dataset, we propose AETree, a tree-structured auto-encoder neural network, for city generation. Specifically, we first propose a novel Spatial-Geometric Distance (SGD) metric to measure the similarity between building layouts and then construct a binary tree over the raw geometric data of building based on the SGD metric. Next, we present a tree-structured network whose encoder learns to extract and merge spatial information from bottom-up iteratively. The resulting global representation is reversely decoded for reconstruction or generation. To address the issue of long-dependency as the level of the tree increases, a Long Short-Term Memory (LSTM) Cell is employed as a basic network element of the proposed AETree. Moreover, we introduce a novel metric, Overlapping Area Ratio (OAR), to quantitatively evaluate the generation results. Experiments on the collected dataset demonstrate the effectiveness of the proposed model on 2D and 3D city generation. Furthermore, the latent features learned by AETree can serve downstream urban planning applications

The effects of (+)-Gossypol on 11β-HSD and the concentration of corticosterone and dehydrocorticosterone in mice serum and tissues

11β-hydroxysteroid dehydrogenase (11β-HSD) plays an important part in mediating glucocorticoid action, catalyzing the interconversion of corticosterone (B) and dehydrocorticosterone (A) in rodents. The aim of our study is to investigate the effects of (+)-gossypol (G+) on 11β-HSD. Adult ICR mice were given B and B + (G+) by intraperitoneal injection. The activity of 11β-HSD was evaluated by measuring the ratio of A and B, meanwhile the effects of (+)-gossypol on the conversion rate of B to A was determined with HPLC. Serum A/B levels of the B+(G+) group decreased by 2.42, 7.32, 17.85, 31.39, and 40.02 % compared to the B group at each measured time interval. A/B levels at 1 h for the B + (G+) group decreased by 43.78, 21.29 and 34.47% in liver, kidney and adrenal glands, respectively, in comparison to the B group. However, (+)-gossypol had no effect on brain and testis. (+)-Gossypol was an inhibitor of 11β-HSD.Colegio de Farmacéuticos de la Provincia de Buenos Aire

The effects of (+)-Gossypol on 11β-HSD and the concentration of corticosterone and dehydrocorticosterone in mice serum and tissues

11β-hydroxysteroid dehydrogenase (11β-HSD) plays an important part in mediating glucocorticoid action, catalyzing the interconversion of corticosterone (B) and dehydrocorticosterone (A) in rodents. The aim of our study is to investigate the effects of (+)-gossypol (G+) on 11β-HSD. Adult ICR mice were given B and B + (G+) by intraperitoneal injection. The activity of 11β-HSD was evaluated by measuring the ratio of A and B, meanwhile the effects of (+)-gossypol on the conversion rate of B to A was determined with HPLC. Serum A/B levels of the B+(G+) group decreased by 2.42, 7.32, 17.85, 31.39, and 40.02 % compared to the B group at each measured time interval. A/B levels at 1 h for the B + (G+) group decreased by 43.78, 21.29 and 34.47% in liver, kidney and adrenal glands, respectively, in comparison to the B group. However, (+)-gossypol had no effect on brain and testis. (+)-Gossypol was an inhibitor of 11β-HSD.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Simultaneous determination of cortisone and cortisol in serum by HPLC-DAD and application for pharmacokinetics

To develop a high performance liquid chromatography method for the simultaneous determination of cortisone and cortisol in rat serum and apply it for pharmacokinetics. After addition of pirfenidone as internal standard (IS), a liquid-liquid extraction with ethylacetate was employed for the sample preparation. Samples were separated on Zorbax SB-C18 column at 25 ºC using mobile phase consisting of acetonitrile-water-0.1 % trifluoroacetic acid with flow rate of 0.9 mL/min, utilizing DAD detection at 246 nm. Excellent liner relationships of the cortisone and cortisol concentrations were obtained from 50 to 6000 ng/mL, with r = 0.9997, 0.9999 respectively, and the lower limit of quantitation (LLOQ) were both 50 ng/mL. The developed method was successfully applied to pharmacokinetic studies of cortisone and cortisol in rats following single dose of 20 mg/kg via intraperitoneal injection.Colegio de Farmacéuticos de la Provincia de Buenos Aire