Interpretable Machine-Learning and Big Data Mining to Predict the CO Separation in Polymer-MOF Mixed Matrix Membranes.

Mixed matrix membranes (MMMs) are renowned for their exceptional gas separation capabilities. In this work, high-throughput computing screening and machine learning are employed to evaluate the CO separation performance of 54117 MMMs composed of 9 polymers and 6013 metal-organic frameworks (MOFs). The structure-property relationships of MMMs are analyzed for 4 binary mixtures (CO/X, X = CH, N, H, O), and the best-performing combinations of MOFs and polymers are found, with which the MMM performance exceeded the Robeson's upper limit.

Deep Learning-Assisted Discovery of Protein Entangling Motifs.

Natural topological proteins exhibit unique properties including enhanced stability, controlled quaternary structures, and dynamic switching properties, highlighting topology as a unique dimension in protein engineering. Although artificial design and synthesis of topological proteins have achieved certain success, their diversity and complexity remain rather limited due to the scarcity of available entangling motifs essential for the construction of nontrivial protein topologies. In this work, we developed a deep-learning model to predict the entanglement features of a homodimer based solely on its amino acid sequence via the Gauss linking number matrices.

Dietary supplementation with contributes to the improvement of hormone levels, gut microbiota, and serum metabolite composition in the Chinese forest musk deer ().

The Chinese forest musk deer () is a small ruminant animal with special economic value. It is listed as a National Level I key protected species in China. However, these animals are prone to stress responses in captive environments.

Cathepsin B Modulates Alzheimer's Disease Pathology Through SAPK/JNK Signals Following Administration of Porphyromonas gingivalis-Derived Outer Membrane Vesicles.

Aim: Porphyromonas gingivalis , a consensus periodontal pathogen, is thought to be involved in Alzheimer's disease (AD) progression, and P. gingivalis -derived outer membrane vesicles (PgOMVs) are a key toxic factor in inducing AD pathology. This study aimed to clarify the regulatory mechanism underlying the PgOMV-induced AD-like phenotype.

MolNexTR: a generalized deep learning model for molecular image recognition.

In the field of chemical structure recognition, the task of converting molecular images into machine-readable data formats such as SMILES string stands as a significant challenge, primarily due to the varied drawing styles and conventions prevalent in chemical literature. To bridge this gap, we proposed MolNexTR, a novel image-to-graph deep learning model that collaborates to fuse the strengths of ConvNext, a powerful Convolutional Neural Network variant, and Vision-TRansformer. This integration facilitates a more detailed extraction of both local and global features from molecular images.

Zinc oxide nanoparticles improve lactation and metabolism in dairy goats by modulating the rumen microbiota.

This study aimed to investigate the effects of dietary supplementation with zinc oxide nanoparticles (ZnONPs) on lactation, rumen microbiota, and metabolomics in dairy goats. Twenty Guanzhong dairy goats, with comparable milk yields and in the mid-lactation stage, were randomly divided into two groups, with 10 goats in each group. The control group was fed a standard diet, while the ZnONP group received the control diet plus 30 mg ZnONPs/kg DM.

An automatic end-to-end chemical synthesis development platform powered by large language models.

The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build an LLM-based reaction development framework (LLM-RDF) to handle fundamental tasks involved throughout the chemical synthesis development. LLM-RDF comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks.

A Machine Learning Model for Predicting the Propagation Rate Coefficient in Free-Radical Polymerization.

The propagation rate coefficient () is one of the most crucial kinetic parameters in free-radical polymerization (FRP) as it directly governs the rate of polymerization and the resulting molecular weight distribution. The in FRP can typically be obtained through experimental measurements or quantum chemical calculations, both of which can be time consuming and resource intensive. Herein, we developed a machine learning model based solely on the structural features of monomers involved in FRP, utilizing molecular embedding and a Lasso regression algorithm to predict more efficiently and accurately.

First principles study of a triazine-based covalent organic framework as a high-capacity anode material for Na/K-ion batteries.

The rational design of high-performance anode materials is crucial for the development of rechargeable Na-ion batteries (NIBs) and K-ion batteries (KIBs). In this study, based on density functional theory (DFT) calculations, we have systematically investigated the possibility of a bilayer triazine-based covalent organic framework (bilayer TCOF) as an anode for NIBs and KIBs. The calculation of the electronic band structure shows that the bilayer TCOF is a direct band gap semiconductor with a band gap of 2.

Mechanistic insights into super-enhancer-driven genes as prognostic signatures in patients with glioblastoma.

Background: Glioblastoma (GBM) is one of the most common malignant brain tumors in adults and is characterized by high aggressiveness and rapid progression, poor treatment, high recurrence rate, and poor prognosis. Although super-enhancer (SE)-driven genes haven been recognized as prognostic markers for several cancers, whether it can be served as effective prognostic markers for patients with GBM has not been evaluated.

Methods: We first combined histone modification data with transcriptome data to identify SE-driven genes associated with prognosis in patients with GBM.

Design, synthesis and anticancer evaluation of novel oncolytic peptide-chlorambucil conjugates.

Nitrogen mustards (NMs) are an important class of chemotherapeutic drugs and have been widely employed for the treatment of various cancers. However, due to the high reactivity of nitrogen mustard, most NMs react with proteins and phospholipids within the cell membrane. Therefore, only a very small fraction of NMs can reach the reach nucleus, alkylating and cross-linking DNA.

The Tumor Stemness Indice mRNAsi can Act as Molecular Typing Tool for Lung Adenocarcinoma.

Due to the high heterogeneity, lung adenocarcinoma (LUAD) cannot be distinguished into precise molecular subtypes, thereby resulting in poor therapeutic effect and low 5-year survival rate clinically. Although the tumor stemness score (mRNAsi) has been shown to accurately characterize the similarity index of cancer stem cells (CSCs), whether mRNAsi can serve as an effective molecular typing tool for LUAD isn't reported to date. In this study, we first demonstrate that mRNAsi is significantly correlated with the prognosis and disease degree of LUAD patients, i.

Hybrid peptide NTP-217 triggers ROS-mediated rapid necrosis in liver cancer cells by induction of mitochondrial leakage.

Liver cancer is the third leading cause of cancer-associated mortality globally, and >830,000 patients with liver cancer undergoing treatment succumbed to the disease in 2020, which indicates the urgent need to develop a more effective anti-liver cancer drug. In our previous study, nucleus-targeting hybrid peptides obtained from the fusion of LTX-315 and the rhodamine B group possessed potent anti-adherent cancer cell activity. Hybrid peptides accumulated in the cell nucleus and damaged the nuclear membrane, resulting in the transfer of reactive oxygen species (ROS) from the cytoplasm to the nucleus and the induction of apoptosis.

Delving into the Heterogeneity of Different Breast Cancer Subtypes and the Prognostic Models Utilizing scRNA-Seq and Bulk RNA-Seq.

Background: Breast cancer (BC) is the most common malignancy in women with high heterogeneity. The heterogeneity of cancer cells from different BC subtypes has not been thoroughly characterized and there is still no valid biomarker for predicting the prognosis of BC patients in clinical practice.

Methods: Cancer cells were identified by calculating single cell copy number variation using the inferCNV algorithm.

mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes.

Background: Recent studies have shown that the mRNA expression-based stemness index (mRNAsi) can accurately quantify the similarity of cancer cells to stem cells, and mRNAsi-related genes are used as biomarkers for cancer. However, mRNAsi-driven tumor heterogeneity is rarely investigated, especially whether mRNAsi can distinguish hepatocellular carcinoma (HCC) into different molecular subtypes is still largely unknown.

Methods: Using OCLR machine learning algorithm, weighted gene co-expression network analysis, consistent unsupervised clustering, survival analysis and multivariate cox regression etc.

Automated Chemical Reaction Extraction from Scientific Literature.

Access to structured chemical reaction data is of key importance for chemists in performing bench experiments and in modern applications like computer-aided drug design. Existing reaction databases are generally populated by human curators through manual abstraction from published literature (e.g.

Direct Optimization across Computer-Generated Reaction Networks Balances Materials Use and Feasibility of Synthesis Plans for Molecule Libraries.

The synthesis of thousands of candidate compounds in drug discovery and development offers opportunities for computer-aided synthesis planning to simplify the synthesis of molecule libraries by leveraging common starting materials and reaction conditions. We develop an optimization-based method to analyze large organic chemical reaction networks and design overlapping synthesis plans for entire molecule libraries so as to minimize the overall number of unique chemical compounds needed as either starting materials or reaction conditions. We consider multiple objectives, including the number of starting materials, the number of catalysts/solvents/reagents, and the likelihood of success of the overall syntheses plan, to select an optimal reaction network to access the target molecules.

A robotic platform for flow synthesis of organic compounds informed by AI planning.

The synthesis of complex organic molecules requires several stages, from ideation to execution, that require time and effort investment from expert chemists. Here, we report a step toward a paradigm of chemical synthesis that relieves chemists from routine tasks, combining artificial intelligence-driven synthesis planning and a robotically controlled experimental platform. Synthetic routes are proposed through generalization of millions of published chemical reactions and validated in silico to maximize their likelihood of success.

Using Machine Learning To Predict Suitable Conditions for Organic Reactions.

Reaction condition recommendation is an essential element for the realization of computer-assisted synthetic planning. Accurate suggestions of reaction conditions are required for experimental validation and can have a significant effect on the success or failure of an attempted transformation. However, de novo condition recommendation remains a challenging and under-explored problem and relies heavily on chemists' knowledge and experience.

[Seasonal Variation and Source Analysis of Water-soluble Inorganic Salts in PM in the Southern Suburbs of Beijing].

In order to study the variation of water-soluble inorganic ions in the four suburbs of Beijing using the atmospheric fine particulate matter rapid trapping system and chemical composition analysis system (RCFP-IC), we carried out measurements for nine water-soluble inorganic ions (Cl, NO, NO, SO, Na, NH, K, Mg, Ca) in PM with continuous on-line observations for one year in Beijing's southern suburbs in 2016. The transport process of pollutants and the potential sources of pollutants were evaluated by combining a trajectory clustering method and potential source contribution factor analysis method (PSCF). During the observation period, the total concentration of the nine water-soluble inorganic ions was 38.