Lattice Strain Engineering on Metal-Organic Frameworks by Ligand Doping to Boost the Electrocatalytic Biomass Valorization.

As an efficient and environmental-friendly strategy, electrocatalytic oxidation can realize biomass lignin valorization by cleaving its aryl ether bonds to produce value-added chemicals. However, the complex and polymerized structure of lignin presents challenges in terms of reactant adsorption on the catalyst surface, which hinders further refinement. Herein, NiCo-based metal-organic frameworks (MOFs) are employed as the electrocatalyst to enhance the adsorption of reactant molecules through π-π interaction. More importantly, lattice strain is introduced into the MOFs via curved ligand doping, which enables tuning of the d-band center of metal active sites to align with the reaction intermediates, leading to stronger adsorption and higher electrocatalytic activity toward bond cleavage within lignin model compounds and native lignin. When 2'-phenoxyacetophenone is utilized as the model compound, high yields of phenol (76.3%) and acetophenone (21.7%) are achieved, and the conversion rate of the reactants reaches 97%. Following pre-oxidation of extracted poplar lignin, >10 kinds of phenolic compounds are received using the as-designed MOFs electrocatalyst, providing ≈12.48% of the monomer, including guaiacol, vanillin, eugenol, etc., and p-hydroxybenzoic acid dominates all the products. This work presents a promising and deliberately designed electrocatalyst for realizing lignin valorization, making significant strides for the sustainability of this biomass resource.

Development of lignin-based 3D-printable light responsive shape memory materials: Design of optically controlled devices.

This study employs simple approaches involving melt blending and Fused Deposition Modeling (FDM) 3D printing to fabricate a light-responsive shape memory composite. And, this composite material is used for the design of optically controlled devices that mimics the blooming of flowers in the natural environment. The composite material utilizes poly(ε-caprolactone) (PCL) and thermoplastic polyurethane (TPU) as the matrix, with lignin (L) serving as a functional filler. The analysis indicates that, due to the excellent photothermal conversion efficiency of lignin, under constant illumination the shape memory materials heat up to 50 °C within 40 s, the shape recovery rate exceeds 95.06 %. Lignin ameliorated the rheological deficiencies of TPU, with the composite material viscosity decreasing from 103 to 101 at an angular frequency of 100 rad/s, enhancing its compatibility with FDM processes. This research offers greater economic efficiency compared to conventional light-responsive materials and a simpler production method.

Hydrogen-transfer strategy in lignin refinery: Towards sustainable and versatile value-added biochemicals.

Lignin, the most prevalent natural source of polyphenols on Earth, offers substantial possibilities for the conversion into aromatic compounds, which is critical for attaining sustainability and carbon neutrality. The hydrogen-transfer method has garnered significant interest owing to its environmental compatibility and economic viability. The efficacy of this approach is contingent upon the careful selection of catalytic and hydrogen-donating systems that decisively affect the yield and selectivity of the monomeric products resulting from lignin degradation. This paper highlights the hydrogen-transfer technique in lignin refinery, with a specific focus on the influence of hydrogen donors on the depolymerization pathways of lignin. It delineates the correlation between the structure and activity of catalytic hydrogen-transfer arrangements and the gamut of lignin-derived biochemicals, utilizing data from lignin model compounds, separated lignin, and lignocellulosic biomass. Additionally, the paper delves into the advantages and future directions of employing the hydrogen-transfer approach for lignin conversion. In essence, this concept investigation illuminates the efficacy of the hydrogen-transfer paradigm in lignin valorization, offering key insights and strategic directives to maximize lignin's value sustainably.

Electrocatalytic Lignin Valorization into Aromatic Products via Oxidative Cleavage of Cα-Cß Bonds.

Lignin is the most promising candidate for producing aromatic compounds from biomass. However, the challenge lies in the cleavage of C-C bonds between lignin monomers under mild conditions, as these bonds have high dissociation energy. Electrochemical oxidation, which allows for mild cleavage of C-C bonds, is considered an attractive solution. To achieve low-energy consumption in the valorization of lignin, the use of highly efficient electrocatalysts is essential. In this study, a meticulously designed catalyst consisting of cobalt-doped nickel (oxy)hydroxide on molybdenum disulfide heterojunction was developed. The presence of molybdenum in a high valence state promoted the adsorption of tert-butyl hydroperoxide, leading to the formation of critical radical intermediates. In addition, the incorporation of cobalt doping regulated the electronic structure of nickel, resulting in a lower energy barrier. As a result, the heterojunction catalyst demonstrated a selectivity of 85.36% for cleaving the Cα-Cß bond in lignin model compound, achieving a substrate conversion of 93.69% under ambient conditions. In addition, the electrocatalyst depolymerized 49.82 wt% of soluble fractions from organosolv lignin (OL), resulting in a yield of up to 13 wt% of aromatic monomers. Significantly, the effectiveness of the prepared electrocatalyst was also demonstrated using industrial Kraft lignin (KL). Therefore, this research offers a practical approach for implementing electrocatalytic oxidation in lignin refining.

Hydrogen-Transfer Reductive Catalytic Fractionation of Lignocellulose: High Monomeric Yield with Switchable Selectivity.

Lignin solubilization and in situ hydrogenolysis are crucial for reductive catalytic fractionation (RCF) of lignocellulose to aromatic monomers. In this study, we reported a typical hydrogen bond acceptor of choline chloride (ChCl) to tailor the hydrogen-donating environment of the Ru/C-catalyzed hydrogen-transfer RCF of lignocellulose. The ChCl-tailored hydrogen-transfer RCF of lignocellulose was conducted under mild temperature and low-pressure (<1 bar) conditions, which was applicable to other lignocellulosic biomass sources. We obtained an approximate theoretical yield of propylphenol monomer of 59.2 wt % and selectivity of 97.3 % using an optimal content of ChCl (10 wt %) in ethylene glycol at 190 °C for 8 h. When the content of ChCl in ethylene glycol was increased to 110 wt %, the selectivity of propylphenol switched toward propylenephenol (yield of 36.2 wt % and selectivity of 87.6 %). The findings in this work provide valuable information for transforming lignin from lignocellulose into value-added products.

One-Pot Protolignin Extraction by Targeted Unlocking Lignin-Carbohydrate Esters via Nucleophilic Addition-Elimination Strategy.

Protolignin extraction can facilitate structure elucidation and valorization of lignin in biorefinery, but is rather challenging due to the complex chemical bonds present. Here, we developed the in situ generated NH3-reline (IGNR) system to realize one-pot protolignin extraction from lignocellulose. In the IGNR system, reline consisting of choline chloride and urea acted as both a solvent and a nucleophile generator, and the nucleophilic addition-elimination mechanism was verified by model compound studies. The in situ generated NH3 could precisely cleave the lignin-carbohydrate esters in lignocellulose with a near-quantitative retention of carbohydrates. The extracted IGNR-Protolignin exhibited native lignin substructure with high molecular weight and high ß-O-4' content (41.5 per 100 aromatic units). In addition, the up-scaled kilogram reaction demonstrated the feasibility of the IGNR system for potential industrial application in a green and sustainable pathway. This work represents a breakthrough toward protolignin extraction in practice with the future goal of achieving total biorefinery.

Drug resistance mechanism of kinase inhibitors in the treatment of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer, and it usually occurs following chronic liver disease. Although some progress has been made in the treatment of HCC, the prognosis of patients with advanced HCC is not optimistic, mainly because of the inevitable development of drug resistance. Therefore, multi-target kinase inhibitors for the treatment of HCC, such as sorafenib, lenvatinib, cabozantinib, and regorafenib, produce small clinical benefits for patients with HCC. It is necessary to study the mechanism of kinase inhibitor resistance and explore possible solutions to overcome this resistance to improve clinical benefits. In this study, we reviewed the mechanisms of resistance to multi-target kinase inhibitors in HCC and discussed strategies that can be used to improve treatment outcomes.

Metal-Free Boron/Phosphorus Co-Doped Nanoporous Carbon for Highly Efficient Benzyl Alcohol Oxidation.

An in-depth understanding of the electronic structures of catalytically active centers and their surrounding vicinity is key to clarifying the structure-activity relationship, and thus enabling the design and development of novel metal-free carbon-based materials with desired catalytic performance. In this study, boron atoms are introduced into phosphorus-doped nanoporous carbon via an efficient strategy, so that the resulting material delivers better catalytic performance. The doped B atoms alter the electronic structures of active sites and cause the adjacent C atoms to act as additional active sites that catalyze the reaction. The B/P co-doped nanoporous carbon shows remarkable catalytic performance for benzyl alcohol oxidation, achieving high yield (over 91% within 2 h) and selectivity (95%), as well as low activation energy (32.2 kJ mol-1 ). Moreover, both the conversion and selectivity remain above 90% after five reaction cycles. Density functional theory calculations indicate that the introduction of B to P-doped nanoporous carbon significantly increases the electron density at the Fermi level and that the oxidation of benzyl alcohol occurs via a different reaction pathway with a very low energy barrier. These findings provide important insights into the relationship between catalytic performance and electronic structure for the design of dual-doped metal-free carbon catalysts.

Enhancing discoveries of molecular QTL studies with small sample size using summary statistic imputation.

Quantitative trait locus (QTL) analyses of multiomic molecular traits, such as gene transcription (eQTL), DNA methylation (mQTL) and histone modification (haQTL), have been widely used to infer the functional effects of genome variants. However, the QTL discovery is largely restricted by the limited study sample size, which demands higher threshold of minor allele frequency and then causes heavy missing molecular trait-variant associations. This happens prominently in single-cell level molecular QTL studies because of sample availability and cost. It is urgent to propose a method to solve this problem in order to enhance discoveries of current molecular QTL studies with small sample size. In this study, we presented an efficient computational framework called xQTLImp to impute missing molecular QTL associations. In the local-region imputation, xQTLImp uses multivariate Gaussian model to impute the missing associations by leveraging known association statistics of variants and the linkage disequilibrium (LD) around. In the genome-wide imputation, novel procedures are implemented to improve efficiency, including dynamically constructing a reused LD buffer, adopting multiple heuristic strategies and parallel computing. Experiments on various multiomic bulk and single-cell sequencing-based QTL datasets have demonstrated high imputation accuracy and novel QTL discovery ability of xQTLImp. Finally, a C++ software package is freely available at https://github.com/stormlovetao/QTLIMP.

Effective Identification of Bacterial Genomes From Short and Long Read Sequencing Data.

With the development of sequencing technology, microbiological genome sequencing analysis has attracted extensive attention. For inexperienced users without sufficient bioinformatics skills, making sense of sequencing data for microbial identification, especially for bacterial identification, through reads analysis is still challenging. In order to address the challenge of effectively analyzing genomic information, in this paper, we develop an effective approach and automatic bioinformatics pipeline called PBGI for bacterial genome identification, performing automatedly and customized bioinformatics analysis using short-reads or long-reads sequencing data produced by multiple platforms such as Illumina, PacBio and Oxford Nanopore. An evaluation of the proposed approach on the practical data set is presented, showing that PBGI provides a user-friendly way to perform bacterial identification through short or long reads analysis, and could provide accurate analyzing results. The source code of the PBGI is freely available at https://github.com/lyotvincent/PBGI.

High-Loading, Well-Dispersed Phosphorus Confined on Nanoporous Carbon Surfaces with Enhanced Catalytic Activity and Cyclic Stability.

Phosphorus-doped carbon materials are promising alternatives to noble metal-based catalysts for the highly selective oxidation of benzyl alcohol to benzaldehyde, but it is challenging to achieve high loadings of high-activity P dopants in metal-free catalysts. Here, the preparation of high-loading and well-dispersed P atoms confined to the surfaces of cellulose-derived carbon via a dissolving-doping strategy is reported. In this method, cellulose is dissolved in phosphoric acid to generate a cellulose-phosphoric supramolecular collosol, which is then directly carbonized. The as-prepared carbon possesses a high specific surface area of 1491 cm3 g-1 and a high P content of 8.8 wt%. The P-doped nanoporous carbon shows a superior catalytic activity and cyclic stability toward benzyl alcohol oxidation, with a high turnover frequency of 3.5 × 10-3 mol g-1 h-1 and a low activation energy of 35.6 kJ mol-1 . Experimental results and theoretical calculations demonstrate that the graphitic C3 PO species is the leading catalytic active center in this material. This study provides a novel strategy to prepare P dopants in nanoporous carbon materials with excellent catalytic performance.

Tunable and functional deep eutectic solvents for lignocellulose valorization.

Stabilization of reactive intermediates is an enabling concept in biomass fractionation and depolymerization. Deep eutectic solvents (DES) are intriguing green reaction media for biomass processing; however undesired lignin condensation is a typical drawback for most acid-based DES fractionation processes. Here we describe ternary DES systems composed of choline chloride and oxalic acid, additionally incorporating ethylene glycol (or other diols) that provide the desired 'stabilization' function for efficient lignocellulose fractionation, preserving the quality of all lignocellulose constituents. The obtained ethylene-glycol protected lignin displays high ß-O-4 content (up to 53 per 100 aromatic units) and can be readily depolymerized to distinct monophenolic products. The cellulose residues, free from condensed lignin particles, deliver up to 95.9 ± 2.12% glucose yield upon enzymatic digestion. The DES can be recovered with high yield and purity and re-used with good efficiency. Notably, we have shown that the reactivity of the ß-O-4 linkage in model compounds can be steered towards either cleavage or stabilization, depending on DES composition, demonstrating the advantage of the modular DES composition.

Room temperature dissolving cellulose with a metal salt hydrate-based deep eutectic solvent.

Abundant and renewable cellulose is a potential candidate for petroleum-derived synthetic polymers. However, the efficient dissolution of this material is problematic because of the high cost, severe reaction condition (e.g., high temperature) and environmentally unfriendly (e.g., toxic reagents, and solvent recyclability). Herein, to realize the room temperature dissolution of cellulose with an inexpensive and eco-friendly solvent, we design a novel low-cost deep eutectic solvent that is composed of zinc chloride, water and phosphoric acid for the efficient dissolution of cellulose. This solvent is featured as having both the superior hydrogen bonding acidity and the hydrogen bonding basicity, and thus can act as a hydrogen bond molecular scissors to cleave the hydrogen bonds within cellulose. In this process, microcrystalline cellulose can be easily dissolved in the solvent at room temperature with a dissolution ratio up to 15 wt%. The dissolved cellulose can also be recovered without any derivatization. The universality, recyclability and pilot production of dissolving cellulose using this solvent are also demonstrated. This work provides a new strategy for the design of novel deep eutectic solvent capable of disrupting the hydrogen bonds of cellulose under mild conditions.

A pipeline for RNA-seq based eQTL analysis with automated quality control procedures.

BACKGROUND: Advances in the expression quantitative trait loci (eQTL) studies have provided valuable insights into the mechanism of diseases and traits-associated genetic variants. However, it remains challenging to evaluate and control the quality of multi-source heterogeneous eQTL raw data for researchers with limited computational background. There is an urgent need to develop a powerful and user-friendly tool to automatically process the raw datasets in various formats and perform the eQTL mapping afterward. RESULTS: In this work, we present a pipeline for eQTL analysis, termed eQTLQC, featured with automated data preprocessing for both genotype data and gene expression data. Our pipeline provides a set of quality control and normalization approaches, and utilizes automated techniques to reduce manual intervention. We demonstrate the utility and robustness of this pipeline by performing eQTL case studies using multiple independent real-world datasets with RNA-seq data and whole genome sequencing (WGS) based genotype data. CONCLUSIONS: eQTLQC provides a reliable computational workflow for eQTL analysis. It provides standard quality control and normalization as well as eQTL mapping procedures for eQTL raw data in multiple formats. The source code, demo data, and instructions are freely available at https://github.com/stormlovetao/eQTLQC .

An integrated approach for copy number variation discovery in parent-offspring trios.

Whole-genome sequencing (WGS) of parent-offspring trios has become widely used to identify causal copy number variations (CNVs) in rare and complex diseases. Existing CNV detection approaches usually do not make effective use of Mendelian inheritance in parent-offspring trios and yield low accuracy. In this study, we propose a novel integrated approach, TrioCNV2, for jointly detecting CNVs from WGS data of the parent-offspring trio. TrioCNV2 first makes use of the read depth and discordant read pairs to infer approximate locations of CNVs and then employs the split read and local de novo assembly approaches to refine the breakpoints. We use the real WGS data of two parent-offspring trios to demonstrate TrioCNV2's performance and compare it with other CNV detection approaches. The software TrioCNV2 is implemented using a combination of Java and R and is freely available from the website at https://github.com/yongzhuang/TrioCNV2.

Fast and Accurate Classification of Meta-Genomics Long Reads With deSAMBA.

There is still a lack of fast and accurate classification tools to identify the taxonomies of noisy long reads, which is a bottleneck to the use of the promising long-read metagenomic sequencing technologies. Herein, we propose de Bruijn graph-based Sparse Approximate Match Block Analyzer (deSAMBA), a tailored long-read classification approach that uses a novel pseudo alignment algorithm based on sparse approximate match block (SAMB). Benchmarks on real sequencing datasets demonstrate that deSAMBA enables to achieve high yields and fast speed simultaneously, which outperforms state-of-the-art tools and has many potentials to cutting-edge metagenomics studies.

A deep learning approach for filtering structural variants in short read sequencing data.

Short read whole genome sequencing has become widely used to detect structural variants in human genetic studies and clinical practices. However, accurate detection of structural variants is a challenging task. Especially existing structural variant detection approaches produce a large proportion of incorrect calls, so effective structural variant filtering approaches are urgently needed. In this study, we propose a novel deep learning-based approach, DeepSVFilter, for filtering structural variants in short read whole genome sequencing data. DeepSVFilter encodes structural variant signals in the read alignments as images and adopts the transfer learning with pre-trained convolutional neural networks as the classification models, which are trained on the well-characterized samples with known high confidence structural variants. We use two well-characterized samples to demonstrate DeepSVFilter's performance and its filtering effect coupled with commonly used structural variant detection approaches. The software DeepSVFilter is implemented using Python and freely available from the website at https://github.com/yongzhuang/DeepSVFilter.

abPOA: an SIMD-based C library for fast partial order alignment using adaptive band.

SUMMARY: Partial order alignment, which aligns a sequence to a directed acyclic graph, is now frequently used as a key component in long-read error correction and assembly. We present abPOA (adaptive banded Partial Order Alignment), a Single Instruction Multiple Data (SIMD)-based C library for fast partial order alignment using adaptive banded dynamic programming. It can work as a stand-alone multiple sequence alignment and consensus calling tool or be easily integrated into any long-read error correction and assembly workflow. Compared to a state-of-the-art tool (SPOA), abPOA is up to 10 times faster with a comparable alignment accuracy. AVAILABILITY AND IMPLEMENTATION: abPOA is implemented in C. A stand-alone tool and a C/Python software interface are freely available at https://github.com/yangao07/abPOA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.