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Rare earth elements (REEs) play indispensable roles in various advanced technologies, from electronics to renewable energy. However, the heavy global REEs supply and the environmental impact of traditional mining practices have spurred the search for sustainable REEs recovery methods. Polymeric materials have emerged as promising candidates due to their selective adsorption capabilities, versatility, scalability, and regenerability. This paper provides an extensive overview of polymeric materials for REEs recovery, including polymeric resins, polymer membranes, cross-linked polymer networks, and nanocomposite polymers. Each category is examined for its advantages, challenges, and notable developments. Furthermore, we highlight the potential of polymeric materials to contribute to eco-friendly and efficient REEs recovery, while acknowledging the need to address challenges such as selectivity, stability, and scalability. The research in this field actively seeks innovative solutions to reduce reliance on hazardous chemicals and minimize waste generation. As the demand for REEs continues to rise, the development of sustainable REEs recovery technologies remains a critical area of investigation, with the collaboration between researchers and industry experts driving progress in this evolving field.

Salinity stress severely hampers plant growth and productivity. How to improve plants’ salt tolerance is an urgent issue. However, the molecular basis of plant resistance to salinity still remains unclear. In this study, we used two poplar species with different salt sensitivities to conduct RNA-sequencing and physiological and pharmacological analyses; the aim is to study the transcriptional profiles and ionic transport characteristics in the roots of the two Populus subjected to salt stress under hydroponic culture conditions. Our results show that numerous genes related to energy metabolism were highly expressed in Populus alba relative to Populus russkii, which activates vigorous metabolic processes and energy reserves for initiating a set of defense responses when suffering from salinity stress. Moreover, we found the capacity of Na+ transportation by the P. alba high-affinity K+ transporter1;2 (HKT1;2) was superior to that of P. russkii under salt stress, which enables P. alba to efficiently recycle xylem-loaded Na+ and to maintain shoot K+/Na+ homeostasis. Furthermore, the genes involved in the synthesis of ethylene and abscisic acid were up-regulated in P. alba but downregulated in P. russkii under salt stress. In P. alba, the gibberellin inactivation and auxin signaling genes with steady high transcriptions, several antioxidant enzymes activities (such as peroxidase [POD], ascorbate peroxidase [APX], and glutathione reductase [GR]), and glycine-betaine content were significantly increased under salt stress. These factors altogether confer P. alba a higher resistance to salinity, achieving a more efficient coordination between growth modulation and defense response. Our research provides significant evidence to improve the salt tolerance of crops or woody plants.

Most penalized maximum likelihood methods for tomographic image reconstruction based on Bayes’ law include a freely adjustable hyperparameter to balance the data fidelity term and the prior/penalty term for a specific noise–resolution tradeoff. The hyperparameter is determined empirically via a trial-and-error fashion in many applications, which then selects the optimal result from multiple iterative reconstructions. These penalized methods are not only time-consuming by their iterative nature, but also require manual adjustment. This study aims to investigate a theory-based strategy for Bayesian image reconstruction without a freely adjustable hyperparameter, to substantially save time and computational resources. The Bayesian image reconstruction problem is formulated by two probability density functions (PDFs), one for the data fidelity term and the other for the prior term. When formulating these PDFs, we introduce two parameters. While these two parameters ensure the PDFs completely describe the data and prior terms, they cannot be determined by the acquired data; thus, they are called complete but unobservable parameters. Estimating these two parameters becomes possible under the conditional expectation and maximization for the image reconstruction, given the acquired data and the PDFs. This leads to an iterative algorithm, which jointly estimates the two parameters and computes the to-be reconstructed image by maximizing a posteriori probability, denoted as joint-parameter-Bayes. In addition to the theoretical formulation, comprehensive simulation experiments are performed to analyze the stopping criterion of the iterative joint-parameter-Bayes method. Finally, given the data, an optimal reconstruction is obtained without any freely adjustable hyperparameter by satisfying the PDF condition for both the data likelihood and the prior probability, and by satisfying the stopping criterion. Moreover, the stability of joint-parameter-Bayes is investigated through factors such as initialization, the PDF specification, and renormalization in an iterative manner. Both phantom simulation and clinical patient data results show that joint-parameter-Bayes can provide comparable reconstructed image quality compared to the conventional methods, but with much less reconstruction time. To see the response of the algorithm to different types of noise, three common noise models are introduced to the simulation data, including white Gaussian noise to post-log sinogram data, Poisson-like signal-dependent noise to post-log sinogram data and Poisson noise to the pre-log transmission data. The experimental outcomes of the white Gaussian noise reveal that the two parameters estimated by the joint-parameter-Bayes method agree well with simulations. It is observed that the parameter introduced to satisfy the prior’s PDF is more sensitive to stopping the iteration process for all three noise models. A stability investigation showed that the initial image by filtered back projection is very robust. Clinical patient data demonstrated the effectiveness of the proposed joint-parameter-Bayes and stopping criterion.

Objective: As an effective lesion heterogeneity depiction, texture information extracted from computed tomography has become increasingly important in polyp classification. However, variation and redundancy among multiple texture descriptors render a challenging task of integrating them into a general characterization. Considering these two problems, this work proposes an adaptive learning model to integrate multi-scale texture features. Methods: To mitigate feature variation, the whole feature set is geometrically split into several independent subsets that are ranked by a learning evaluation measure after preliminary classifications. To reduce feature redundancy, a bottom-up hierarchical learning framework is proposed to ensure monotonic increase of classification performance while integrating these ranked sets selectively. Two types of classifiers, traditional (random forest + support vector machine)- and convolutional neural network (CNN)-based, are employed to perform the polyp classification under the proposed framework with extended Haralick measures and gray-level co-occurrence matrix (GLCM) as inputs, respectively. Experimental results are based on a retrospective dataset of 63 polyp masses (defined as greater than 3 cm in largest diameter), including 32 adenocarcinomas and 31 benign adenomas, from adult patients undergoing first-time computed tomography colonography and who had corresponding histopathology of the detected masses. Results: We evaluate the performance of the proposed models by the area under the curve (AUC) of the receiver operating characteristic curve. The proposed models show encouraging performances of an AUC score of 0.925 with the traditional classification method and an AUC score of 0.902 with CNN. The proposed adaptive learning framework significantly outperforms nine well-established classification methods, including six traditional methods and three deep learning ones with a large margin. Conclusions: The proposed adaptive learning model can combat the challenges of feature variation through a multiscale grouping of feature inputs, and the feature redundancy through a hierarchal sorting of these feature groups. The improved classification performance against comparative models demonstrated the feasibility and utility of this adaptive learning procedure for feature integration.

Metal tolerance proteins (MTPs) are plant divalent cation transporters that play important roles in plant metal tolerance and homeostasis. Poplar is an ideal candidate for the phytoremediation of heavy metals because of its numerous beneficial attributes. However, the definitive phylogeny and heavy metal transport mechanisms of the MTP family in poplar remain unknown. Here, 22 MTP genes in P. trichocarpa were identified and classified into three major clusters and seven groups according to phylogenetic relationships. An evolutionary analysis suggested that PtrMTP genes had undergone gene expansion through tandem or segmental duplication events. Moreover, all PtrMTPs were predicted to localize in the vacuole and/or cell membrane, and contained typical structural features of the MTP family, cation efflux domain. The temporal and spatial expression pattern analysis results indicated the involvement of PtrMTP genes in poplar developmental control. Under heavy metal stress, most of PtrMTP genes were induced by at least two metal ions in roots, stems or leaves. In addition, PtrMTP8.1, PtrMTP9 and PtrMTP10.4 displayed the ability of Mn transport in yeast cells, and PtrMTP6 could transport Co, Fe and Mn. These findings will provide an important foundation to elucidate the biological functions of PtrMTP genes, and especially their role in regulating heavy metal tolerance in poplar.

Poly (N-isopropylacrylamide) (pNIPAm)-based hydrogels and hydrogel particles (microgels) have been extensively studied since their discovery a number of decades ago. While their utility seems to have no limit, this feature article is focused on their development and application for sensing small molecules, macromolecules, and biomolecules. We highlight hydrogel/microgel-based photonic materials that have order in one, two, or three dimensions, which exhibit optical properties that depend on the presence and concentration of various analytes. A particular focus is put on one-dimensional materials developed in the Serpe Group.