Phytoremediation of copper-contaminated soils by rapeseed (Brassica napus L.) and underlying molecular mechanisms for copper absorption and sequestration.

High levels of copper released in the soil, mainly from anthropogenic activity, can be hazardous to plants, animals, and humans. The present research aimed to estimate the suitability and effectiveness of rapeseed (Brassica napus L.) as a possible soil remediation option and to uncover underlying adaptive mechanisms A pot experiment was conducted to explore the effect of copper stress on agronomic and yield traits for 32 rapeseed genotypes. The copper-tolerant genotype H2009 and copper-sensitive genotype ZYZ16 were selected for further physiological, metabolomic, and transcriptomic analyses. The results exhibited a significant genotypic variation in copper stress tolerance in rapeseed. Specifically, the ratio of seed yield under copper stress to control ranged from 0.29 to 0.74. Furthermore, the proline content and antioxidant enzymatic activities in the roots were greater than those in the shoots. The accumulated copper in the roots accounted for about 50% of the total amount absorbed by plants; thus, the genotypes possessing high root volumes can be used for rhizofiltration to uptake and sequester copper. Additionally, the pectin and hemicellulose contents were significantly increased by 15.6% and 162%, respectively, under copper stress for the copper-tolerant genotype, allowing for greater sequestration of copper ions in the cell wall and lower oxidative stress. Comparative analysis of transcriptomes and metabolomes revealed that excessive copper enhanced the up-regulation of functional genes or metabolites related to cell wall binding, copper transportation, and chelation in the copper-tolerant genotype. Our results suggest that copper-tolerant rapeseed can thrive in heavily copper-polluted soils with a 5.85% remediation efficiency as well as produce seed and vegetable oil without exceeding food quality standards for the industry. This multi-omics comparison study provides insights into breeding copper-tolerant genotypes that can be used for the phytoremediation of heavy metal-polluted soils.

Morpho-Physiochemical Indices and Transcriptome Analysis Reveal the Role of Glucosinolate and Erucic Acid in Response to Drought Stress during Seed Germination of Rapeseed.

The global expansion of rapeseed seed quality has been focused on maintaining glucosinolate (GSL) and erucic acid (EA) contents. However, the influence of seed GSL and EA contents on the germination process under drought stress remains poorly understood. Herein, 114 rapeseed accessions were divided into four groups based on GSL and EA contents to investigate their performance during seed imbibition under drought stress. Our results revealed significant variations in seed germination-related traits, particularly with higher GSL and EA, which exhibited higher germination % (G%) and lower mean germination time (MGT) under drought stress conditions. Moreover, osmoregulation, enzymatic system and hormonal regulation were improved in high GSL and high EA (HGHE) versus low GSL and low EA (LGLE) seeds, indicating the essential protective role of GSL and EA during the germination process in response to drought stress. The transcriptional regulation mechanism for coordinating GSL-EA-related pathways in response to drought stress during seed imbibition was found to involve the differential expression of sugar metabolism-, antioxidant-, and hormone-related genes with higher enrichment in HGHE compared to LGLE seeds. GO enrichment analysis showed higher variations in transcription regulator activity and DNA-binding transcription factors, as well as ATP and microtubule motor activity in GSL-EA-related pathways. Furthermore, KEGG analysis identified cellular processes, environmental information processing, and metabolism categories, with varied gene participation between GSL, EA and GSL-EA-related pathways. For further clarification, QY7 (LGLE) seeds were primed with different concentrations of GSL and EA under drought stress conditions. The results showed that 200 µmol/L of GSL and 400 µmol/L of EA significantly improved G%, MGT, and seedling fresh weight, besides regulating stress and fatty acid responsive genes during the seed germination process under drought stress conditions. Conclusively, exogenous application of GSL and EA is considered a promising method for enhancing the drought tolerance of LGLE seeds. Furthermore, the current investigation could provide a theoretical basis of GSL and EA roles and their underlying mechanisms in stress tolerance during the germination process.

Research on the influence of surface crack development on radon exhalation in uranium tailing ponds under wet and dry cycles.

Compacted soil layers effectively prevent the migration of radon gas from uranium tailings impoundments to the nearby environment. However, surface damage caused by wet and dry cycles (WDCs) weakens this phenomenon.In order to study the effect of crack network on radon exhalation under WDCs, a homemade uranium tailing pond model was developed to carry out radon exhalation tests under five WDCs. Based on image processing and morphological methods, the area, length, mean width and fractal dimension of the drying cracks were quantitatively analyzed, and multiple linear regression was used to establish the relationship between the geometric characteristics of the cracks and the radon exhalation rate under multiple WDCs. The results suggested that the radon release rate and crack network of the uranium tailings pond gradually stabilized as the water content decreased, following rapid development in a single WDC process. The radon release rate increased continuously after each cycle, with a cumulative increase of 25.9% over 5 cycles. The radon release rate and average crack width remained consistent in size, and a binary linear regression considering width and fractal dimension could explain the changes in radon release rate after multiple WDCs.

Self-Supervised Medical Image Segmentation Using Deep Reinforced Adaptive Masking.

Self-supervised learning aims to learn transferable representations from unlabeled data for downstream tasks. Inspired by masked language modeling in natural language processing, masked image modeling (MIM) has achieved certain success in the field of computer vision, but its effectiveness in medical images remains unsatisfactory. This is mainly due to the high redundancy and small discriminative regions in medical images compared to natural images. Therefore, this paper proposes an adaptive hard masking (AHM) approach based on deep reinforcement learning to expand the application of MIM in medical images. Unlike predefined random masks, AHM uses an asynchronous advantage actor-critic (A3C) model to predict reconstruction loss for each patch, enabling the model to learn where masking is valuable. By optimizing the non-differentiable sampling process using reinforcement learning, AHM enhances the understanding of key regions, thereby improving downstream task performance. Experimental results on two medical image datasets demonstrate that AHM outperforms state-of-the-art methods. Additional experiments under various settings validate the effectiveness of AHM in constructing masked images.

Inferring neural activity before plasticity as a foundation for learning beyond backpropagation.

For both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output, a challenge that is known as 'credit assignment'. It has long been assumed that credit assignment is best solved by backpropagation, which is also the foundation of modern machine learning. Here, we set out a fundamentally different principle on credit assignment called 'prospective configuration'. In prospective configuration, the network first infers the pattern of neural activity that should result from learning, and then the synaptic weights are modified to consolidate the change in neural activity. We demonstrate that this distinct mechanism, in contrast to backpropagation, (1) underlies learning in a well-established family of models of cortical circuits, (2) enables learning that is more efficient and effective in many contexts faced by biological organisms and (3) reproduces surprising patterns of neural activity and behavior observed in diverse human and rat learning experiments.

Automatic data augmentation for medical image segmentation using Adaptive Sequence-length based Deep Reinforcement Learning.

Although existing deep reinforcement learning-based approaches have achieved some success in image augmentation tasks, their effectiveness and adequacy for data augmentation in intelligent medical image analysis are still unsatisfactory. Therefore, we propose a novel Adaptive Sequence-length based Deep Reinforcement Learning (ASDRL) model for Automatic Data Augmentation (AutoAug) in intelligent medical image analysis. The improvements of ASDRL-AutoAug are two-fold: (i) To remedy the problem of some augmented images being invalid, we construct a more accurate reward function based on different variations of the augmentation trajectories. This reward function assesses the validity of each augmentation transformation more accurately by introducing different information about the validity of the augmented images. (ii) Then, to alleviate the problem of insufficient augmentation, we further propose a more intelligent automatic stopping mechanism (ASM). ASM feeds a stop signal to the agent automatically by judging the adequacy of image augmentation. This ensures that each transformation before stopping the augmentation can smoothly improve the model performance. Extensive experimental results on three medical image segmentation datasets show that (i) ASDRL-AutoAug greatly outperforms the state-of-the-art data augmentation methods in medical image segmentation tasks, (ii) the proposed improvements are both effective and essential for ASDRL-AutoAug to achieve superior performance, and the new reward evaluates the transformations more accurately than existing reward functions, and (iii) we also demonstrate that ASDRL-AutoAug is adaptive for different images in terms of sequence length, as well as generalizable across different segmentation models.

Hybrid Reinforced Medical Report Generation With M-Linear Attention and Repetition Penalty.

To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where deep convolutional neural networks (CNNs) are employed to encode the input images, and recurrent neural networks (RNNs) are used to decode the visual features into medical reports automatically. However, these state-of-the-art methods mainly suffer from three shortcomings: 1) incomprehensive optimization; 2) low-order and unidimensional attention; and 3) repeated generation. In this article, we propose a hybrid reinforced medical report generation method with m-linear attention and repetition penalty mechanism (HReMRG-MR) to overcome these problems. Specifically, a hybrid reward with different weights is employed to remedy the limitations of single-metric-based rewards, and a local optimal weight search algorithm is proposed to significantly reduce the complexity of searching the weights of the rewards from exponential to linear. Furthermore, we use m-linear attention modules to learn multidimensional high-order feature interactions and to achieve multimodal reasoning, while a new repetition penalty is proposed to apply penalties to repeated terms adaptively during the model's training process. Extensive experimental studies on two public benchmark datasets show that HReMRG-MR greatly outperforms the state-of-the-art baselines in terms of all metrics. The effectiveness and necessity of all components in HReMRG-MR are also proved by ablation studies. Additional experiments are further conducted and the results demonstrate that our proposed local optimal weight search algorithm can significantly reduce the search time while maintaining superior medical report generation performances.