Changes to Captions: An Attentive Network for Remote Sensing Change Captioning

In recent years, advanced research has focused on the direct learning and analysis of remote sensing images using natural language processing (NLP) techniques. The ability to accurately describe changes occurring in multi-temporal remote sensing images is becoming increasingly important for geospatial understanding and land planning. Unlike natural image change captioning tasks, remote sensing change captioning aims to capture the most significant changes, irrespective of various influential factors such as illumination, seasonal effects, and complex land covers. In this study, we highlight the significance of accurately describing changes in remote sensing images and present a comparison of the change captioning task for natural and synthetic images and remote sensing images. To address the challenge of generating accurate captions, we propose an attentive changes-to-captions network, called Chg2Cap for short, for bi-temporal remote sensing images. The network comprises three main components: 1) a Siamese CNN-based feature extractor to collect high-level representations for each image pair; 2) an attentive decoder that includes a hierarchical self-attention block to locate change-related features and a residual block to generate the image embedding; and 3) a transformer-based caption generator to decode the relationship between the image embedding and the word embedding into a description. The proposed Chg2Cap network is evaluated on two representative remote sensing datasets, and a comprehensive experimental analysis is provided. The code and pre-trained models will be available online at https://github.com/ShizhenChang/Chg2Cap

Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection

In recent years, multi-view subspace learning has been garnering increasing attention. It aims to capture the inner relationships of the data that are collected from multiple sources by learning a unified representation. In this way, comprehensive information from multiple views is shared and preserved for the generalization processes. As a special branch of temporal series hyperspectral image (HSI) processing, the anomalous change detection task focuses on detecting very small changes among different temporal images. However, when the volume of datasets is very large or the classes are relatively comprehensive, existing methods may fail to find those changes between the scenes, and end up with terrible detection results. In this paper, inspired by the sketched representation and multi-view subspace learning, a sketched multi-view subspace learning (SMSL) model is proposed for HSI anomalous change detection. The proposed model preserves major information from the image pairs and improves computational complexity by using a sketched representation matrix. Furthermore, the differences between scenes are extracted by utilizing the specific regularizer of the self-representation matrices. To evaluate the detection effectiveness of the proposed SMSL model, experiments are conducted on a benchmark hyperspectral remote sensing dataset and a natural hyperspectral dataset, and compared with other state-of-the art approaches

Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks

Change detection, as an important application for high-resolution remote sensing images, aims to monitor and analyze changes in the land surface over time. With the rapid growth in the quantity of high-resolution remote sensing data and the complexity of texture features, a number of quantitative deep learning-based methods have been proposed. Although these methods outperform traditional change detection methods by extracting deep features and combining spatial-temporal information, reasonable explanations about how deep features work on improving the detection performance are still lacking. In our investigations, we find that modern Hopfield network layers achieve considerable performance in semantic understandings. In this paper, we propose a Deep Supervision and FEature Retrieval network (Dsfer-Net) for bitemporal change detection. Specifically, the highly representative deep features of bitemporal images are jointly extracted through a fully convolutional Siamese network. Based on the sequential geo-information of the bitemporal images, we then design a feature retrieval module to retrieve the difference feature and leverage discriminative information in a deeply supervised manner. We also note that the deeply supervised feature retrieval module gives explainable proofs about the semantic understandings of the proposed network in its deep layers. Finally, this end-to-end network achieves a novel framework by aggregating the retrieved features and feature pairs from different layers. Experiments conducted on three public datasets (LEVIR-CD, WHU-CD, and CDD) confirm the superiority of the proposed Dsfer-Net over other state-of-the-art methods. Code will be available online (https://github.com/ShizhenChang/Dsfer-Net)

Lipids driving protein structure? Evolutionary adaptations in Kir channels

Many eukaryotic channels, transporters and receptors are activated by phosphatidyl inositol bisphosphate (PIP(2)) in the membrane, and every member of the eukaryotic inward rectifier potassium (Kir) channel family requires membrane PIP(2) for activity. In contrast, a bacterial homolog (KirBac1.1) is specifically inhibited by PIP(2). We speculate that a key evolutionary adaptation in eukaryotic channels is the insertion of additional linkers between trans-membrane and cytoplasmic domains, revealed by new crystal structures, that convert PIP(2) inhibition to activation. Such an adaptation may reflect a novel evolutionary drive to protein structure,; one that was necessary to permit channel function within the highly negatively charged membranes that evolved in the eukaryotic lineage

AI Security for Geoscience and Remote Sensing: Challenges and Future Trends

Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and applied widely to RS data analysis. The successful application of AI covers almost all aspects of Earth observation (EO) missions, from low-level vision tasks like super-resolution, denoising and inpainting, to high-level vision tasks like scene classification, object detection and semantic segmentation. While AI techniques enable researchers to observe and understand the Earth more accurately, the vulnerability and uncertainty of AI models deserve further attention, considering that many geoscience and RS tasks are highly safety-critical. This paper reviews the current development of AI security in the geoscience and RS field, covering the following five important aspects: adversarial attack, backdoor attack, federated learning, uncertainty and explainability. Moreover, the potential opportunities and trends are discussed to provide insights for future research. To the best of the authors' knowledge, this paper is the first attempt to provide a systematic review of AI security-related research in the geoscience and RS community. Available code and datasets are also listed in the paper to move this vibrant field of research forward

Carbapenemase-Producing Escherichia coli among Humans and Backyard Animals

Background: The rapidly increasing dissemination of carbapenem-resistant Enterobacteriaceae (CRE) in both humans and animals poses a global threat to public health. However, the transmission of CRE between humans and animals has not yet been well studied. Objectives: We investigated the prevalence, risk factors, and drivers of CRE transmission between humans and their backyard animals in rural China. Methods: We conducted a comprehensive sampling strategy in 12 villages in Shandong, China. Using the household [residents and their backyard animals (farm and companion animals)] as a single surveillance unit, we assessed the prevalence of CRE at the household level and examined the factors associated with CRE carriage through a detailed questionnaire. Genetic relationships among human- and animal-derived CRE were assessed using whole-genome sequencing–based molecular methods. Results: A total of 88 New Delhi metallo-β-lactamases –type carbapenem-resistant Escherichia coli (NDM-EC), including 17 from humans, 44 from pigs, 12 from chickens, 1 from cattle, and 2 from dogs, were isolated from 65 of the 746 households examined. The remaining 12 NDM-EC were from flies in the immediate backyard environment. The NDM-EC colonization in households was significantly associated with a) the number of species of backyard animals raised/kept in the same household, and b) the use of human and/or animal feces as fertilizer. Discriminant analysis of principal components (DAPC) revealed that a large proportion of the core genomes of the NDM-EC belonged to strains from hosts other than their own, and several human isolates shared closely related core single-nucleotide polymorphisms and blaNDM genetic contexts with isolates from backyard animals. Conclusions: To our knowledge, we are the first to report evidence of direct transmission of NDM-EC between humans and animals. Given the rise of NDM-EC in community and hospital infections, combating NDM-EC transmission in backyard farm systems is needed. https://doi.org/10.1289/EHP525

Hypoglycemic effect of white (Morus alba L.) and black (Morus nigra L.) mulberry fruits in diabetic rat

The aim of the present study was to investigate the hypoglycemic effect of white (Morus alba L.) and black (Morus nigra L.) mulberry fruits either used individually or in a combination on alloxan diabetic rats. These fruits are reported to be rich in antioxidants, flavonoids and phenolics that can potentially fight against diabetes mellitus. Male albino rats were divided into 5 groups: normal control, alloxan-diabetic control, diabetic rats treated with white mulberry fruit powder at 5% in the diet, diabetic rats treated with black mulberry fruit powder at 5% in the diet and diabetic rats treated with mixture of white and black mulberry fruits powder at 5% in the diet. After 4 weeks of treatment, blood glucose level, liver and kidney enzymes activity, lipid profile, lipid peroxidation and histopathological studies on liver, kidney and pancreas were evaluated. The mixture of white and black mulberry fruits showed the most significant (p < 0.05) improvement in feed efficiency ratio with increasing body weight gain, as well as decrease in blood glucose level and liver-kidney dysfunction when compared with diabetic control rats. Significant decrease in serum cholesterol, triglycerides and low density lipoprotein cholesterol (LDLc) as well as significant increase in high density lipoprotein cholesterol (HDLc) in diabetic rats was observed with all treatments. Moreover, mulberry fruits administration caused significant inhibition in lipid peroxidation and α-amylase activity. In addition, the beneficial effect of all treatments was further confirmed with histopathological examination of liver, kidney and pancreas. This study reveals hypoglycemic and hypolipidemic effects of white and black mulberry fruits either used individually or in combination as a dietary supplement in alloxan diabetic rats

Linking Changes in Epithelial Morphogenesis to Cancer Mutations Using Computational Modeling

Most tumors arise from epithelial tissues, such as mammary glands and lobules, and their initiation is associated with the disruption of a finely defined epithelial architecture. Progression from intraductal to invasive tumors is related to genetic mutations that occur at a subcellular level but manifest themselves as functional and morphological changes at the cellular and tissue scales, respectively. Elevated proliferation and loss of epithelial polarization are the two most noticeable changes in cell phenotypes during this process. As a result, many three-dimensional cultures of tumorigenic clones show highly aberrant morphologies when compared to regular epithelial monolayers enclosing the hollow lumen (acini). In order to shed light on phenotypic changes associated with tumor cells, we applied the bio-mechanical IBCell model of normal epithelial morphogenesis quantitatively matched to data acquired from the non-tumorigenic human mammary cell line, MCF10A. We then used a high-throughput simulation study to reveal how modifications in model parameters influence changes in the simulated architecture. Three parameters have been considered in our study, which define cell sensitivity to proliferative, apoptotic and cell-ECM adhesive cues. By mapping experimental morphologies of four MCF10A-derived cell lines carrying different oncogenic mutations onto the model parameter space, we identified changes in cellular processes potentially underlying structural modifications of these mutants. As a case study, we focused on MCF10A cells expressing an oncogenic mutant HER2-YVMA to quantitatively assess changes in cell doubling time, cell apoptotic rate, and cell sensitivity to ECM accumulation when compared to the parental non-tumorigenic cell line. By mapping in vitro mutant morphologies onto in silico ones we have generated a means of linking the morphological and molecular scales via computational modeling. Thus, IBCell in combination with 3D acini cultures can form a computational/experimental platform for suggesting the relationship between the histopathology of neoplastic lesions and their underlying molecular defects

Nonnegative-Constrained Joint Collaborative Representation with Union Dictionary for Hyperspectral Anomaly Detection

Recently, many collaborative representation-based (CR) algorithms have been proposed for hyperspectral anomaly detection. CR-based detectors approximate the image by a linear combination of background dictionaries and the coefficient matrix, and derive the detection map by utilizing recovery residuals. However, these CR-based detectors are often established on the premise of precise background features and strong image representation, which are very difficult to obtain. In addition, pursuing the coefficient matrix reinforced by the general $l_2$-min is very time consuming. To address these issues, a nonnegative-constrained joint collaborative representation model is proposed in this paper for the hyperspectral anomaly detection task. To extract reliable samples, a union dictionary consisting of background and anomaly sub-dictionaries is designed, where the background sub-dictionary is obtained at the superpixel level and the anomaly sub-dictionary is extracted by the pre-detection process. And the coefficient matrix is jointly optimized by the Frobenius norm regularization with a nonnegative constraint and a sum-to-one constraint. After the optimization process, the abnormal information is finally derived by calculating the residuals that exclude the assumed background information. To conduct comparable experiments, the proposed nonnegative-constrained joint collaborative representation (NJCR) model and its kernel version (KNJCR) are tested in four HSI data sets and achieve superior results compared with other state-of-the-art detectors

Experimental study on flame combustion characteristics of large-bore marine diesel engine based on endoscopic technology

Large-bore marine diesel engines have the characteristics of poor ignition performance and insufficient combustion in the cylinder. This work revealed the combustion and emission performance of large-bore marine diesel engines based on endoscopic visualization technology. The flame temperature and soot distribution were analyzed in radial and axial directions. Results show that the large-bore diesel engine has a poor combustion effect because of the large fuel injection quantity and insufficient fuel-air mixing effect in the cylinder. The temperature in the cylinder rises twice in the late stage of combustion. The average temperature rises by 3.8%, caused by the secondary ignition of part of the unburned diesel. In addition, the large-bore engine produces a large amount of soot due to an insufficient mixing effect. It can be observed from the radial flame visualization images that the propagation speed of the flame is slow. The time required for the flame to propagate to the wall at 50% load is reduced by 31% compared with 25% load. The downward movement of the piston causes the flame tumble flow in the cylinder, resulting in an apparent asymmetric structure of the flame development