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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.

Abstract Malignant lesions have a high tendency to invade their surrounding environment compared to benign ones. This paper proposes a dynamic lesion model and explores the 2nd order derivatives at each image voxel, which reflect the rate of change of image intensity, as a quantitative measure of the tendency. The 2nd order derivatives at each image voxel are usually represented by the Hessian matrix, but it is difficult to quantify a matrix field (or image) through the lesion space as a measure of the tendency. We conjecture that the three eigenvalues contain important information of the Hessian matrix and are chosen as the surrogate representation of the Hessian matrix. By treating the three eigenvalues as a vector, called Hessian vector, which is defined in a local coordinate formed by three orthogonal Hessian eigenvectors and further adapting the gray level occurrence computing method to extract the vector texture descriptors (or measures) from the Hessian vector, a quantitative presentation for the dynamic lesion model is completed. The vector texture descriptors were applied to differentiate malignant from benign lesions from two pathologically proven datasets: colon polyps and lung nodules. The classification results not only outperform four state-of-the-art methods but also three radiologist experts.

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.

Abstract Texture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

Soil salinity is a widespread stress in semi-arid forests worldwide, but how to manage nitrogen (N) nutrition to improve plant saline tolerance remains unclear. Here, the cuttings of a widely distributed poplar from central Asia, Populus russikki Jabl., were exposed to either normal or low nitrogen (LN) concentrations for two weeks in semi-controlled greenhouse, and then they were added with moderate salt solution or not for another two weeks to evaluate their physiological, biochemical, metabolites and transcriptomic profile changes. LN-pretreating alleviated the toxicity caused by the subsequent salt stress in the poplar plants, demonstrated by a significant reduction in the influx of Na+ and Cl- and improvement of the K+/Na+ ratio. The other salt-stressed traits were also ameliarated, indicated by the variations of chlorophyll content, PSII photochemical activity and lipid peroxidation. Stress alleviation resulted from two different processes. First, LN pretreatment caused a significant increase of non-structural carbohydrates (NSC), allowed for an increased production of osmolytes and a higher potential fueling ion transport under subsequent salt condition, along with increased transcript levels of the cation/H+ ATPase. Second, LN pretreatment enhanced the transcript levels of stress signaling components and phytohormones pathway as well as antioxidant enzyme activities. The results indicate that early restrictions of N supply could enhance posterior survival under saline stress in poplar plants, which is important for plantation programs and restoration activities in semi-arid areas.

With the booming demand of the electric vehicle industry, the concentration of manganese (Mn) and cobalt (Co) flowing into land ecosystems has also increased significantly. While these transition metals can promote the growth and development of plants, they may become toxic under high concentrations. It is thus important to understand how Mn and Co are distributed in plants to develop novel germplasms for the remediation of these heavy metals in contaminated soils. Here, an MTP gene that encodes the CDF (cation diffusion facilitator) protein in Populus trichocarpa, PtrMTP6, was screened as the key gene involved in the distribution of both Mn and Co in poplar. The PtrMTP6-GFP fusion protein was co-localized with the mRFP-VSR2, showing that PtrMTP6 proteins are present at the pre-vacuolar compartment (PVC). Yeast mutant complementation assays further identified that PtrMTP6 serves as a Mn and Co transporter, reducing yeast cell toxicity after exposure to excessive Mn or Co. Histochemical analyses showed that PtrMTP6 was mainly expressed in phloem, suggesting that PtrMTP6 probably involved in the Mn and Co transport via phloem in plants. Under excess Co, PtrMTP6 overexpressing poplar lines were more severely damaged than the control due to higher Co accumulations in young tissue. PtrMTP6 overexpressing lines showed little change in their tolerance to excess Mn, although young tissues also accumulated more Mn. PtrMTP6 play important roles in Mn and Co distribution in poplar and further research on its regulation will be important to increase bioremediation in Mn and Co polluted ecosystems.

Abstract Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels, i.e., the tissue heterogeneity, and has been recognized as important biomarkers in various clinical tasks. Spectral computed tomography (CT) is believed to be able to enrich tissue texture by providing different voxel contrast images using different X-ray energies. Therefore, this paper aims to address two related issues for clinical usage of spectral CT, especially the photon counting CT (PCCT): (1) texture enhancement by spectral CT image reconstruction, and (2) spectral energy enriched tissue texture for improved lesion classification. For issue (1), we recently proposed a tissue-specific texture prior in addition to low rank prior for the individual energy-channel low-count image reconstruction problems in PCCT under the Bayesian theory. Reconstruction results showed the proposed method outperforms existing methods of total variation (TV), low-rank TV and tensor dictionary learning in terms of not only preserving texture features but also suppressing image noise. For issue (2), this paper will investigate three models to incorporate the enriched texture by PCCT in accordance with three types of inputs: one is the spectral images, another is the co-occurrence matrices (CMs) extracted from the spectral images, and the third one is the Haralick features (HF) extracted from the CMs. Studies were performed on simulated photon counting data by introducing attenuation-energy response curve to the traditional CT images from energy integration detectors. Classification results showed the spectral CT enriched texture model can improve the area under the receiver operating characteristic curve (AUC) score by 7.3%, 0.42% and 3.0% for the spectral images, CMs and HFs respectively on the five-energy spectral data over the original single energy data only. The CM- and HF-inputs can achieve the best AUC of 0.934 and 0.927. This texture themed study shows the insight that incorporating clinical important prior information, e.g., tissue texture in this paper, into the medical imaging, such as the upstream image reconstruction, the downstream diagnosis, and so on, can benefit the clinical tasks.

Abstract Computer aided detection (CADe) of pulmonary nodules plays an important role in assisting radiologists’ diagnosis and alleviating interpretation burden for lung cancer. Current CADe systems, aiming at simulating radiologists’ examination procedure, are built upon computer tomography (CT) images with feature extraction for detection and diagnosis. Human visual perception in CT image is reconstructed from sinogram, which is the original raw data acquired from CT scanner. In this work, different from the conventional image based CADe system, we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain. Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain, we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram. The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database, with each case having at least one juxtapleural nodule annotation. Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve (AUC) of receiver operating characteristic based on sinogram alone, comparing to 0.89 based on CT image alone. Moreover, a combination of sinogram and CT image could further improve the value of AUC to 0.92. This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.

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.

Tomatoes: a protein control for flavonoids Insights into the production of important nutrients called flavonoids are revealed by a molecular and genetic study of tomatoes. Flavonoids are a diverse group of chemicals with a variety of metabolic functions, and which contribute to the characteristic colors of many fruits and vegetables. They have been linked with health benefits due to their antioxidant, anti-inflammatory, and immune-regulating activities. Researchers in China, led by Yinan Yao at Southwest University of Science and Technology, explored the activity of a protein known as SlAN11. They found that the protein interacts with other proteins to regulate the activity of specific genes, increasing the production of flavonoids called anthocyanins. SlAN11 also regulates the dormancy process in tomato seeds. Geneticists may use the results to generate new breeds of tomato plants enriched in flavonoids to promote good health.

Plant metal tolerance proteins (MTPs) comprise a family of membrane divalent cation transporters that play essential roles in plant mineral nutrition maintenance and heavy metal stresses resistance. However, the evolutionary relationships and biological functions of MTP family in tobacco remain unclear. In the present study, 26, 13, and 12 MTPs in three main Nicotiana species (N. tabacum, N. sylvestris, and N. tomentosiformis) were identified and designated, respectively. The phylogenetic relationships, gene structures, chromosome distributions, conserved motifs, and domains of NtMTPs were systematic analyzed. According to the phylogenetic features, 26 NtMTPs were classified into three major substrate-specific groups that were Zn-cation diffusion facilitators (CDFs), Zn/Fe-CDFs, and Mn-CDFs, and seven primary groups (1, 5, 6, 7, 8, 9, and 12). All of the NtMTPs contained a modified signature sequence and the cation_efflux domain, whereas some of them also harbored the ZT_dimer. Evolutionary analysis showed that NtMTP family of N. tabacum originated from its parental genome of N. sylvestris and N. tomentosiformis, and further underwent gene loss and expanded via one segmental duplication event. Moreover, the prediction of cis-acting elements (CREs) and the microRNA target sites of NtMTP genes suggested the diverse and complex regulatory mechanisms that control NtMTPs gene expression. Expression profile analysis derived from transcriptome data and quantitative real-time reverse transcription-PCR (qRT-PCR) analysis showed that the tissue expression patterns of NtMTPs in the same group were similar but varied among groups. Besides, under heavy metal toxicity, NtMTP genes exhibited various responses in either tobacco leaves or roots. 19 and 15 NtMTPs were found to response to at least one metal ion treatment in leaves and roots, respectively. In addition, NtMTP8.1, NtMTP8.4, and NtMTP11.1 exhibited Mn transport abilities in yeast cells. These results provided a perspective on the evolution of MTP genes in tobacco and were helpful for further functional characterization of NtMTP genes.

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.

Epigenetic modification generally refers to phenotypic changes by a mechanism other than changes in DNA sequence and plays a significant role in developmental processes. In this study, we found that overexpression of one alternatively spliced tomato DDB1 transcript, DDB1(F) that is prevalently present in all tested tissues, resulted in reduction of organ size. Transgenic plants constitutively expressing the DDB1(F) from a strong cauliflower mosaic virus (CaMV) 35S promoter displayed moderately reduced size in vegetative organs (leaves and stems) and radically decreased size in reproductive organs (flowers, seeds and fruits), in which several genes encoding negative regulators for cell division were upregulated. Significantly, reduction of organ size conferred by overexpression of DDB1(F) transgene appears not to segregate in the subsequent generations, suggesting the phenotypic alternations are manipulated in an epigenetic manner and can be transmitted over generations. This notion was further substantiated by analysis of DNA methylation level at the SlWEE1 gene (encoding a negative regulator of cell division), revealing a correlation between less methylation in the promoter region and elevated expression level of this gene. Thus, our results suggest DDB1 plays an important role in regulation of the epigenetic state of genes involved in organogenesis, despite the underlying mechanism remains to be elucidated.