Waste PET Plastic-Derived CoNi-Based Metal–Organic Framework as an Anode for Lithium-Ion Batteries

Recycling waste PET plastics into metal–organic frameworks is conducive to both pollution alleviation and sustainable economic development. Herein, we have utilized waste PET plastic to synthesize CoNi-MOF applied to lithium battery anode materials via a low-temperature solvothermal method for the first time. The preparation process is effortless, and the sources’ conversion rate can reach almost 100%. In addition, the anode performance of MOFs with various Co/Ni mole ratios was investigated. The as-synthesized Co0.8Ni-MOF exhibits excellent crystallinity, purity, and electrochemical performance. The initial discharge and charge capacities are 2496 and 1729 mAh g–1, respectively. Even after 200 cycles, the Co0.8Ni-MOF electrode can exhibit a high Coulombic efficiency of over 99%. Consequently, given the environmental and economic benefits, the Co0.8Ni-MOF derived from waste PET plastic is thought to be an appealing anode material for lithium-ion batteries

Machine Learning for Sequence and Structure-Based Protein–Ligand Interaction Prediction

Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein–ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein–ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein–ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein–ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed

Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ

Cu-based alloy catalysts are widely used in the field of carbon dioxide reduction reaction (CO2RR), due to the good selectivity and low overpotential. In order to achieve efficient exploration of alloy catalysts for CO2RR, a machine learning (ML) model, based on a gradient boosting regression (GBR) algorithm, is developed. By implementing a rigorous feature selection process, the dimensionality of feature space is reduced from thirteen to five, including work function (W), local electronegativity (Loc_EN), electronegativity (EN), interplanar spacing (D), and atomic number (Z), which is referred to as the WLEDZ model. The few-feature model has a high performance as that with many features, and the ML model successfully and rapidly predicts the adsorption energy of the key intermediates (HCOO, CO, and COOH) in the CO2RR process. In addition, eight Cu-based bimetallic catalysts are predicted with highly promising alternatives. This demonstrates that the WLEDZ few-feature ML model can screen highly promising bimetallic alloy for CO2RR and can also be used for the design of other types of catalysts

Influences of <i>APOA5</i> Variants on Plasma Triglyceride Levels in Uyghur Population

<div><p>Objective</p><p>Single nucleotide polymorphisms (SNPs) in apolipoprotein A5 (<i>APOA5</i>) gene are associated with triglyceride (TG) levels. However, the minor allele frequencies and linkage disequilibriums (LDs) of the SNPs in addition to their effects on TG levels vary greatly between Caucasians and East Asians. The distributions of the SNPs/haplotypes and their associations with TG levels in Uyghur population, an admixture population of Caucasians and East Asians, have not been reported to date. Here, we performed a cross-sectional study to address these.</p><p>Methods</p><p>Genotyping of four SNPs in <i>APOA5</i> (rs662799, rs3135506, rs2075291, and rs2266788) was performed in 1174 unrelated Uyghur subjects. SNP/haplotype and TG association analyses were conducted.</p><p>Results</p><p>The frequencies of the SNPs in Uyghurs were in between those in Caucasians and East Asians. The LD between rs662799 and rs2266788 in Uyghurs was stronger than that in East Asians but weaker than that in Caucasians, and the four SNPs resulted in four haplotypes (TGGT, CGGC, TCGT, and CGTT arranged in the order of rs662799, rs3135506, rs2075291, and rs2266788) representing 99.2% of the population. All the four SNPs were significantly associated with TG levels. Compared with non-carriers, carriers of rs662799-C, rs3135506-C, rs2075291-T, and rs2266788-C alleles had 16.0%, 15.1%, 17.1%, and 12.4% higher TG levels, respectively. When haplotype TGGT was defined as the reference, the haplotypes CGGC, TCGT, and CGTT resulted in 16.1%, 19.0%, and 19.8% higher TG levels, respectively. The proportions of variance in TG explained by <i>APOA5</i> locus were 2.5%, 0.3%, 0.4%, and 1.9% for single SNP rs662799, rs3135506, rs2075291, and rs2266788, respectively, and 3.0% for the haplotypes constructed by them.</p><p>Conclusions</p><p>The association profiles between the SNPs and haplotypes at <i>APOA5</i> locus and TG levels in this admixture population differed from those in Caucasians and East Asians. The functions of these SNPs and haplotypes need to be elucidated comprehensively.</p></div

Data from: Influences of APOA5 variants on plasma triglyceride levels in Uyghur population

original dat

The minor allele frequencies (MAFs) of the polymorphisms in APOA5 in the different populations.

<p>Based on the online databases and literatures in addition to our own data.</p

The frequencies of the haplotypes derived from the four SNPs in APOA5 in different populations.

<p>Based on the online databases and literatures in addition to our own data.</p

Association between the SNPs in <i>APOA5</i> and plasma TG levels in the general Uyghur population.

<p>*unadjusted <i>P</i> value.</p>#<p>adjusted for age, gender, BMI, smoking, and drinking.</p><p>Bold fonts represent significant difference after multiple correction (<i>P</i><0.0125).</p><p>Association between the SNPs in <i>APOA5</i> and plasma TG levels in the general Uyghur population.</p

Association between the haplotypes in <i>APOA5</i> and plasma TG levels in the general Uyghur population.

$<p>The four SNPs in the haplotypes were arranged in the order of 5′ to 3′, i.e., rs662799, rs3135506, rs2075291, and rs2266788.</p><p>*unadjusted <i>P</i> value.</p>#<p>adjusted for age, gender, BMI, smoking, and drinking.</p><p>Bold fonts represent significant difference after multiple correction (<i>P</i><0.0125).</p><p>Association between the haplotypes in <i>APOA5</i> and plasma TG levels in the general Uyghur population.</p

Clinical characteristics of the studied subjects.

<p>Values shown are numbers (frequencies) for categorical variables and mean ± standard deviation for continuous variables.</p>$<p>calculated by dividing weight (kg) by height squared (m²).</p><p>Clinical characteristics of the studied subjects.</p