Prediction of the Critical Temperature of Superconductors Based on Two-Layer Feature Selection and the Optuna-Stacking Ensemble Learning Model.

The study of superconductors' critical temperature (T c) has been a matter of interest. A method combining a two-layer feature selection (TL) and Optuna-Stacking ensemble learning model is proposed in the study for predicting T c from physicochemical components. Since most machine-learning models require a large amount of prior knowledge to construct the feature vectors associated with T c manually, they may contain redundant or invalid features that adversely affect the analysis and prediction of T c. The TL model combines the advantages of filtered and packed feature selection. In the first layer, feature importance is ranked by "SHapley Additive explain (SHAP)" in combination with CatBoost, followed by maximum mutual information coefficient (MIC) and distance correlation coefficient (DCC) for initial feature selection in terms of feature importance ranking. The second layer uses a cross-validation-based genetic algorithm (cv-GA) to eliminate the remaining redundant/invalid features. The selected features are fed into the Stacking integrated learning model to achieve prediction of Tc, and the multidimensional hyperparametric optimization of the metamodel is achieved by Optuna, an improved Bayesian hyperparametric optimization framework based on the Tree-structured Parzen Estimator (TPE) and pruning strategy. The model has obvious advantages and generality in terms of prediction performance and feature reduction rate, and it also proves to be suitable for high-temperature superconductor T c prediction. It provides an efficient and cost-effective method for data-driven superconductor research.

Investigating changes of proteome in the bovine milk serum after retort processing using proteomics techniques.

The objective of this study was to investigate the changes of the proteins in bovine milk serum after retort processing by label-free quantification proteomics techniques. A total of 96 and 106 proteins were quantified in control group (CG) and retort group (RG), respectively. Hierarchical clustering analysis of the identified milk serum proteins showed a decrease in the abundance of most proteins, such as serum albumin, lactoperoxidase, lactotransferrin, and complement C3, and an increase in the abundance of other proteins such as κ-casein, lipocalin 2, and Perilipin. Student's t-test showed 21 proteins significantly differential abundance between CG and RG (p < .05), of which intensity-based absolute quantification (iBAQ) of five proteins decreased and iBAQ of 16 proteins increased. Bioinformatics analysis demonstrated that retort processing increased the digestibility of proteins, but this improvement was offset by a decrease in the digestibility of proteins caused by protein modification. Our results provide insight into the proteome of retort sterilized milk for the first time. Given the extremely high security of retort sterilized milk, the proteome of bovine milk serum changes after retort sterilization exposed in this study will contribute to the formula design of retort sterilized milk products.

Co-transport and competitive retention of different ionic rare earth elements (REEs) in quartz sand: Effect of kaolinite.

The increasing excavation and utilization of rare earth elements (REEs) have resulted in an elevated release of these elements into the environment. Therefore, investigating the transport behavior of REEs is critical for a comprehensive understanding of their geochemical cycles and to propose potential pollution control strategies. This study investigated the transport, co-transport, and competitive retention of three REEs: La (a light REE), Gd (a middle REE), and Yb (a heavy REE), as well as the co-transport of REEs and kaolinite (a representative clay mineral) in porous media. Both observed and simulated breakthrough curves and retention profiles demonstrated that all ionic REEs exhibited considerable breakthrough and slight retention with almost uniform shapes in quartz sand (QS) owing to the weak affinity of ionic REEs to QS. The breakthrough of REEs in all experiments followed the order of La > Gd > Yb, indicating that REE breakthrough increased with decreasing atomic number. The same elements exhibited their highest breakthrough during the co-transport of the three REEs, followed by co-transport of two REEs, and finally single transport. Furthermore, mathematical modeling indicated that the retention of REEs in QS was a predominantly kinetic process, whereby competitive blocking was the dominant mechanism for the enhanced breakthrough of REEs during co-transport, as compared to single transport. The co-transport of REEs and kaolinite demonstrated that kaolinite has a slight influence on the transport of REEs in QS under adsorption kinetics. However, REEs inhibited the transport and strongly enhanced the retention of kaolinite in QS due to a decreasing electrostatic repulsion between kaolinite and QS in the presence of REEs, even if the adsorption of REEs onto kaolinite was weak under adsorption kinetics. Therefore, this study increases our understanding of the transport mechanisms of REEs in the environment.

The complex interactions between novel DEHP-metabolising bacteria and the microbes in agricultural soils.

The indigenous microorganisms with the ability of metabolising di-(2-ethylhexyl) phthalate (DEHP) in agricultural soils and their interactions with non-degrading microbes were revealed by DNA-based stable isotope probing coupled with molecular ecological network. Aside from the previously reported DEHP degraders (family Planococcaceae and genus Sphingobacterium), five OTUs representing bacteria affiliated with genus Brevundimona, class Spartobacteria, genus Singulisphaera, genus Dyella and class Ktedonobacteria were linked with DEHP biodegradation. The analysis of the constructed ecological network based on soil microbial communities demonstrated the negative relationships between DEHP degraders and the dominant family Oxalobacteraceae in soils. Additionally, two cultivable bacteria isolated from the same soils, Rhizobium-1 and Ensifer-1, had strong capabilities in degrading DEHP but their involvement in in situ DEHP degradation was questioned, as their DNA was not labelled with 13C from DEHP. These findings provide deeper understanding on the indigenous DEHP-degrading communities and will benefit the remediation of phthalate esters contaminated soils.

An ultrasensitive homogeneous aptasensor for carcinoembryonic antigen based on upconversion fluorescence resonance energy transfer.

Carcinoembryonic antigen (CEA) has been recognized as one of the most important tumor markers. Herein, we reported an ultrasensitive homogeneous aptasensor based on fluorescence resonance energy transfer (FRET) between upconversion nanoparticles (UCNPs) and graphene oxide (GO) for CEA detection. The CEA aptamer modified UCNPs can bind to the surface of GO through π-π stacking interaction, resulting in fluorescence quenching due to the energy transfer from UCNPs to GO. After the introduction of CEA, the CEA aptamer preferentially combined with CEA to form three-dimensional structure which made UCNPs-aptamer dissociate from the GO, blocking the energy transfer process. The fluorescence of UCNPs was accordingly restored in a CEA concentration-dependent manner both aqueous solution and human serum samples. The aptasensor could monitor CEA level directly in human serum and the results were strongly correlated with commercial chemiluminescence kits. The excellent detection performance suggested promising prospect of the aptasensor in practical application.