Optimal Clustering Framework for Hyperspectral Band Selection

Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal solutions toward a specific objective function. This paper focuses on clustering-based band selection, and proposes a new framework to solve the above dilemma, claiming the following contributions: 1) An optimal clustering framework (OCF), which can obtain the optimal clustering result for a particular form of objective function under a reasonable constraint. 2) A rank on clusters strategy (RCS), which provides an effective criterion to select bands on existing clustering structure. 3) An automatic method to determine the number of the required bands, which can better evaluate the distinctive information produced by certain number of bands. In experiments, the proposed algorithm is compared to some state-of-the-art competitors. According to the experimental results, the proposed algorithm is robust and significantly outperform the other methods on various data sets

Self-supervised Domain-agnostic Domain Adaptation for Satellite Images

Domain shift caused by, e.g., different geographical regions or acquisition conditions is a common issue in machine learning for global scale satellite image processing. A promising method to address this problem is domain adaptation, where the training and the testing datasets are split into two or multiple domains according to their distributions, and an adaptation method is applied to improve the generalizability of the model on the testing dataset. However, defining the domain to which each satellite image belongs is not trivial, especially under large-scale multi-temporal and multi-sensory scenarios, where a single image mosaic could be generated from multiple data sources. In this paper, we propose an self-supervised domain-agnostic domain adaptation (SS(DA)2) method to perform domain adaptation without such a domain definition. To achieve this, we first design a contrastive generative adversarial loss to train a generative network to perform image-to-image translation between any two satellite image patches. Then, we improve the generalizability of the downstream models by augmenting the training data with different testing spectral characteristics. The experimental results on public benchmarks verify the effectiveness of SS(DA)2

Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects

Object detection is an essential and fundamental task in computer vision and satellite image processing. Existing deep learning methods have achieved impressive performance thanks to the availability of large-scale annotated datasets. Yet, in real-world applications the availability of labels is limited. In this context, few-shot object detection (FSOD) has emerged as a promising direction, which aims at enabling the model to detect novel objects with only few of them annotated. However, many existing FSOD algorithms overlook a critical issue: when an input image contains multiple novel objects and only a subset of them are annotated, the unlabeled objects will be considered as background during training. This can cause confusions and severely impact the model's ability to recall novel objects. To address this issue, we propose a self-training-based FSOD (ST-FSOD) approach, which incorporates the self-training mechanism into the few-shot fine-tuning process. ST-FSOD aims to enable the discovery of novel objects that are not annotated, and take them into account during training. On the one hand, we devise a two-branch region proposal networks (RPN) to separate the proposal extraction of base and novel objects, On another hand, we incorporate the student-teacher mechanism into RPN and the region of interest (RoI) head to include those highly confident yet unlabeled targets as pseudo labels. Experimental results demonstrate that our proposed method outperforms the state-of-the-art in various FSOD settings by a large margin. The codes will be publicly available at https://github.com/zhu-xlab/ST-FSOD

The structural and photosynthetic characteristics of the exposed peduncle of wheat (Triticum aestivum L.): an important photosynthate source for grain-filling

<p>Abstract</p> <p>Background</p> <p>In wheat (<it>Triticum aestivum </it>L), the flag leaf has been thought of as the main source of assimilates for grain growth, whereas the peduncle has commonly been thought of as a transporting organ. The photosynthetic characteristics of the exposed peduncle have therefore been neglected. In this study, we investigated the anatomical traits of the exposed peduncle during wheat grain ontogenesis, and we compared the exposed peduncle to the flag leaf with respect to chloroplast ultrastructure, photosystem II (PSII) quantum yield, and phospho<it>enol</it>pyruvate carboxylase (PEPCase; EC 4.1.1.31) activity.</p> <p>Results</p> <p>Transmission electron microscope observations showed well-developed chloroplasts with numerous granum stacks at grain-filling stages 1, 2 and 3 in both the flag leaf and the exposed peduncle. In the exposed peduncle, the membranes constituting the thylakoids were very distinct and plentiful, but in the flag leaf, there was a sharp breakdown at stage 4 and complete disintegration of the thylakoid membranes at stage 5. PSII quantum yield assays revealed that the photosynthetic efficiency remained constant at stages 1, 2 and 3 and then declined in both organs. However, the decline occurred more dramatically in the flag leaf than in the exposed peduncle. An enzyme assay showed that at stages 1 and 2 the PEPCase activity was lower in the exposed peduncle than in the flag leaf; but at stages 3, 4 and 5 the value was higher in the exposed peduncle, with a particularly significant difference observed at stage 5. Subjecting the exposed part of the peduncle to darkness following anthesis reduced the rate of grain growth.</p> <p>Conclusion</p> <p>Our results suggest that the exposed peduncle is a photosynthetically active organ that produces photosynthates and thereby makes a crucial contribution to grain growth, particularly during the late stages of grain-filling.</p

The Arabidopsis Elongator Subunit ELP3 and ELP4 Confer Resistance to Bacterial Speck in Tomato

Although production of tomato (Solanum lycopersicum) is threatened by a number of major diseases worldwide, it has been difficult to identify effective and durable management measures against these diseases. In this study, we attempted to improve tomato disease resistance by transgenic overexpression of genes encoding the Arabidopsis thaliana Elongator (AtELP) complex subunits AtELP3 and AtELP4. We show that overexpression of AtELP3 and AtELP4 significantly enhanced resistance to tomato bacterial speck caused by the Pseudomonas syringae pv. tomato strain J4 (Pst J4) without clear detrimental effects on plant growth and development. Interestingly, the transgenic plants exhibited resistance to Pst J4 only when inoculated through foliar sprays but not through infiltration into the leaf apoplast. Although this result suggested possible involvement of stomatal immunity, we found that Pst J4 inoculation did not induce stomatal closure and there were no differences in stomatal apertures and conductance between the transgenic and control plants. Further RNA sequencing and real-time quantitative PCR analyses revealed a group of defense-related genes to be induced to higher levels after infection in the AtELP4 transgenic tomato plants than in the control, suggesting that the enhanced disease resistance of the transgenic plants may be attributed to elevated induction of defense responses. Additionally, we show that the tomato genome contains single-copy genes encoding all six Elongator subunits (SlELPs), which share high identities with the AtELP proteins, and that SlELP3 and SlELP4 complemented the Arabidopsis Atelp3 and Atelp4 mutants, respectively, indicating that the function of tomato Elongator is probably conserved. Taken together, our results not only shed new light on the tomato Elongator complex, but also revealed potential candidate genes for engineering disease resistance in tomato

End-of-treatment anti-HBs levels and HBeAg status identify durability of HBsAg loss after PEG-IFN discontinuation

BackgroundHepatitis B surface antigen (HBsAg) loss, namely, the functional cure, can be achieved through the pegylated interferon (PEG-IFN)-based therapy. However, it is an unignorable fact that a small proportion of patients who achieved functional cure develop HBsAg reversion (HRV) and the related factors are not well described.MethodsA total of 112 patients who achieved PEG-IFN-induced HBsAg loss were recruited. HBV biomarkers and biochemical parameters were examined dynamically. HBV RNA levels were assessed in the cross-sectional analysis. The primary endpoint was HRV, defined as the reappearance of HBsAg after PEG-IFN discontinuation.ResultsHRV occurred in 17 patients during the follow-up period. Univariable analysis indicated that hepatitis B e antigen (HBeAg) status, different levels of hepatitis B surface antibody (anti-HBs), and hepatitis B core antibody (anti-HBc) at the end of PEG-IFN treatment (EOT) were significantly associated with the incidence of HRV through using the log-rank test. Additionally, time-dependent receiver operating characteristic (ROC) analysis showed that the anti-HBs was superior to anti-HBc in predictive power for the incidence of HRV during the follow-up period. Multivariable Cox proportional hazard analysis found that anti-HBs ≥1.3 log10IU/L (hazard ratio (HR), 0.148; 95% confidence interval (CI), 0.044-0.502) and HBeAg negativity (HR, 0.183; 95% CI, 0.052-0.639) at EOT were independently associated with lower incidence of HRV. Cross-sectional analysis indicated that the HBV RNA levels were significantly correlated with the HBsAg levels in patients with HRV (r=0.86, p=0.003).ConclusionsEOT HBeAg negativity and anti-HBs ≥1.3 log10IU/L identify the low risk of HRV after PEG-IFN discontinuation

The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery

The scientific outcomes of the 2022 Landslide4Sense (L4S) competition organized by the Institute of Advanced Research in Artificial Intelligence (IARAI) are presented here. The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally. The 2022 L4S aims to foster interdisciplinary research on recent developments in deep learning (DL) models for the semantic segmentation task using satellite imagery. In the past few years, DL-based models have achieved performance that meets expectations on image interpretation, due to the development of convolutional neural networks (CNNs). The main objective of this article is to present the details and the best-performing algorithms featured in this competition. The winning solutions are elaborated with state-of-the-art models like the Swin Transformer, SegFormer, and U-Net. Advanced machine learning techniques and strategies such as hard example mining, self-training, and mix-up data augmentation are also considered. Moreover, we describe the L4S benchmark data set in order to facilitate further comparisons, and report the results of the accuracy assessment online. The data is accessible on Future Development Leaderboard for future evaluation at https://www.iarai.ac.at/landslide4sense/challenge/ , and researchers are invited to submit more prediction results, evaluate the accuracy of their methods, compare them with those of other users, and, ideally, improve the landslide detection results reported in this article

Domain-Agnostic Domain Adaption for Building Footprint Extraction

For global range satellite imaging mission, images captured from different areas may have large distribution biases due to different illuminations, shooting angles and atmospheric conditions. A straightforward idea to mitigate this problem is to categorize the images into different domains according the cities they belong to, and apply domain adaptation approaches. However, categorization by cities becomes unreasonable with the increase of the city number, and the emergence of inter-city similarity and intra-city discrepancy. With such consideration, this paper proposes a novel domain adaptation method named domain-agnostic domain adaptation (DADA) to reduce the distribution biases without explicitly defining the domain each image belongs to. To implement this, we augment the images to the styles of different domains by Generative Adversarial Networks (GAN) and contrastive learning to increase the generalizability of down-stream tasks. Experiments on Planetscope building footprint extraction datasets verify the effectiveness of our method

lncRNA ELFN1-AS1 promotes proliferation, migration and invasion and suppresses apoptosis in colorectal cancer cells by enhancing G6PD activity

Tumour cells change their metabolic patterns to support high proliferation rates and cope with oxidative stress. The lncRNA ELFN1-AS1 is highly expressed in a wide range of cancers and is essential to the proliferation and apoptosis of tumour cells. Nevertheless, its function in the metabolic reprogramming of tumour cells is unclear. Here we show that ELFN1-AS1 promotes glucose consumption as well as lactate and NADPH production. Database searching, bioinformatics analysis, RNA immunoprecipitation (RIP) and RNA pull-down assays show that ELFN1-AS1 enhances glucose-6-phosphate dehydrogenase ( G6PD) expression and activates the pentose phosphate pathway (PPP) by promoting TP53 degradation. In addition, luciferase reporter assay and chromatin immunoprecipitation (ChIP) show that YY1 binds to the ELFN1-AS1 promoter to promote transcriptional activation of ELFN1-AS1. Consistent with the in vitro experiments, knockdown of ELFN1-AS1 impedes the growth of tumours transplanted into mice by inhibiting the expression of G6PD. In conclusion, this study reveals that ELFN1-AS1 activates the PPP, and validates the regulatory role of the YY1/ ELFN1-AS1/ TP53/ G6PD axis in colorectal cancer