Correction: FragGen: towards 3D geometry reliable fragment-based molecular generation.

[This corrects the article DOI: 10.1039/D4SC04620J.].

DiffBP: generative diffusion of 3D molecules for target protein binding.

Generating molecules that bind to specific proteins is an important but challenging task in drug discovery. Most previous works typically generate atoms autoregressively, with element types and 3D coordinates of atoms generated one by one. However, in real-world molecular systems, interactions among atoms are global, spanning the entire molecule, leading to pair-coupled energy function among atoms.

Durian: A Comprehensive Benchmark for Structure-Based 3D Molecular Generation.

Three-dimensional (3D) molecular generation models employ deep neural networks to simultaneously generate both topological representation and molecular conformations. Due to their advantages in utilizing the structural and interaction information on targets, as well as their reduced reliance on existing bioactivity data, these models have attracted widespread attention. However, limited training and testing data sets and the unexpected biases inherent in single evaluation metrics pose a significant challenge in comparing these models in practical settings.

FragGen: towards 3D geometry reliable fragment-based molecular generation.

3D structure-based molecular generation is a successful application of generative AI in drug discovery. Most earlier models follow an atom-wise paradigm, generating molecules with good docking scores but poor molecular properties (like synthesizability and drugability). In contrast, fragment-wise generation offers a promising alternative by assembling chemically viable fragments.

Deep Lead Optimization: Leveraging Generative AI for Structural Modification.

The integration of deep learning-based molecular generation models into drug discovery has garnered significant attention for its potential to expedite the development process. Central to this is lead optimization, a critical phase where existing molecules are refined into viable drug candidates. As various methods for deep lead optimization continue to emerge, it is essential to classify these approaches more clearly.

Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting.

Recent years have witnessed great success in handling graph-related tasks with graph neural networks (GNNs). However, most existing GNNs are based on message passing to perform feature aggregation and transformation, where the structural information is explicitly involved in the forward propagation by coupling with node features through graph convolution at each layer. As a result, subtle feature noise or structure perturbation may cause severe error propagation, resulting in extremely poor robustness.

Effectiveness and Efficiency: Label-Aware Hierarchical Subgraph Learning for Protein-Protein Interaction.

The study of protein-protein interactions (PPIs) holds immense significance in understanding various biological activities, as well as in drug discovery and disease diagnosis. Existing deep learning methods for PPI prediction, including graph neural networks (GNNs), have been widely employed as the solutions, while they often experience a decline in performance in the real world. We claim that the topological shortcut is one of the key problems contributing negatively to the performance, according to our analysis.

Wound Dressing Based on Cassava Silk-Chitosan.

The application prospects of composite sponges with antibacterial and drug-carrying functions in the field of medical tissue engineering are extensive. A solution of cassava silk fibroin (CSF) was prepared with Ca(NO) as a solvent, which was then combined with chitosan (CS) to create a sponge-porous material by freeze-drying. The CSF-CS composite sponge with a mesh structure was successfully fabricated through hydrogen bonding.

Multi-omics analysis of immune-related microbiome and prognostic model in head and neck squamous cell carcinoma.

Objectives: The aim of our study is to explore the transcriptional and microbial characteristics of head and neck cancer's immune phenotypes using a multi-omics approach.

Materials And Methods: Employing TCGA data, we analyzed head and neck squamous cell carcinoma (HNSCC) immune cells with CIBERSORT and identified differentially expressed genes using DESeq2. Microbial profiles, obtained from the TCMA database, were analyzed using LEfSe algorithm to identify differential microbes in immune cell infiltration (ICI) subgroups.

Genesis and Accumulation Mechanism of Low-Rank CBM: A Case Study in the Jiergalangtu Block of Erlian Basin, Northern China.

In order to elucidate the origin of coalbed methane (CBM) in the Jiergalangtu block of Erlian Basin, Inner Mongolia of China, gas components, stable isotope tests of 22 gas samples, radioisotope dating measurements, and water quality analysis of 15 coproduced water samples were evaluated. On account of the geochemical data and genetic indicators, including C/C, C/(C + C), and CO/(CO + CH) (CDMI) values, δC(CO), ΔδC(), δN, and He/He combined with vitrinite reflectance (Ro) (0.29-0.

Epithelial SIRT6 governs IL-17A pathogenicity and drives allergic airway inflammation and remodeling.

Dysregulation of IL-17A is closely associated with airway inflammation and remodeling in severe asthma. However, the molecular mechanisms by which IL-17A is regulated remain unclear. Here we identify epithelial sirtuin 6 (SIRT6) as an epigenetic regulator that governs IL-17A pathogenicity in severe asthma.

Ratiometric fluorescent sensor with large pseudo-Stokes shifts for precise sensing and imaging of pH without interferential background fluorescence.

Endowing fluorescent pH sensors with large Stokes shifts promises to resolve interferential background fluorescence in practice, and yet few such method has been reported, owing to lack of luminescent materials with large Stokes shifts used in fluorescent sensors. Herein, we elaborately designed NaGdF:Ce@NaGdF:Nd@NaYF:Eu core-double shells (CDS) lanthanide-doped fluoride nanoparticles (LFNPs), employing Gd-mediated energy migration and interfacial energy transfer to realize intense red and NIR emissions under 254 nm irradiation, and pseudo-Stokes shifts of which reached up to striking 361 nm and 610 nm, respectively. The CDS LFNPs collaborated with absorption-based pH indicator bromocresol green to from a novel fluorescent sensor film, and employing low-cost dual chip RGB-NIR camera to precisely record luminescence signals.

Assessment of microplastic contamination in an eastern Pacific tuna (Katsuwonus pelamis) and evaluation of its health risk implication through molecular docking and metabolomics studies.

This work investigated microplastic (MP) pollution in a commercially-important tuna species Katsuwonus pelamis (K. pelamis) from the Eastern Pacific and health implications. 125 MPs were extracted from gills, esophagus, stomachs, intestinal tracts, and muscle of K.

An innovative biomass-driven energy systems for green energy and freshwater production with less CO2 emission: Environmental and technical approaches.

Considering the current crisis of fossil energies, the exploitation of renewables and green technologies is necessary and unavoidable. Additionally, the design and development of integrated energy systems with two or more output products and the maximum usage of thermal losses in order to improve efficiency can boost the yield and acceptability of the energy system. In this regard, this paper develops a comprehensive multi-aspect assessment of the operation of a new solar and biomass energies-driven multigeneration system (MGS).

The Unique Genetic Mutation Characteristics Based on Large Panel Next-Generation Sequencing (NGS) Detection in Multiple Primary Lung Cancers (MPLC) Patients.

Background: With the wide application of multislice spiral computed tomography (CT), the frequency of detection of multiple lung cancer is increasing. This study aimed to analyze gene mutations characteristics in multiple primary lung cancers (MPLC) using large panel next-generation sequencing (NGS) assays.

Methods: Patients with MPLC surgically removed from the Affiliated Hospital of Guangdong Medical University from Jan 2020 to Dec 2021 enrolled the study.

Homophily-Enhanced Self-Supervision for Graph Structure Learning: Insights and Directions.

Graph neural networks (GNNs) have recently achieved remarkable success on a variety of graph-related tasks, while such success relies heavily on a given graph structure that may not always be available in real-world applications. To address this problem, graph structure learning (GSL) is emerging as a promising research topic where task-specific graph structure and GNN parameters are jointly learned in an end-to-end unified framework. Despite their great progress, existing approaches mostly focus on the design of similarity metrics or graph construction, but directly default to adopting downstream objectives as supervision, which lacks deep insight into the power of supervision signals.

Characteristic mango price forecasting using combined deep-learning optimization model.

Accurate product price forecasting is helpful for scientific decision-making and precise industrial planning. As a characteristic fruit that drives regional development, mango price prediction is of great significance to several economies. However, owing to the strong volatility of mango prices, forecasting is vulnerable to uncertainties and is very challenging.

Effects of macromolecular organic acids on reducing inorganic phosphorus fixation in soil.

To improve the availability of inorganic phosphorus (P) in soil, we investigated the role of three macromolecular organic acids (MOAs), including fulvic acid (FA), polyaspartic acid (PA), and tannic acid (TA), in reducing the fixation of inorganic P fertilizer in the soil. AlPO, FePO, and CaH(PO)·5HO crystals were chosen as insoluble phosphate representatives in the soil to simulate the solubilization process of inorganic P by MOAs. The microstructural and physicochemical properties of AlPO, FePO, and CaH(PO)·5HO were determined by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS) before and after treatment of MOAs.

Simulation of CO capture from natural gas by cyclic pressure swing adsorption process using activated carbon.

This work presented modeling and simulation of CO from natural gas. One of the most promising technologies is Pressure Swing Adsorption (PSA), which is an energy-efficient and cost-effective process for separating and capturing CO from industrial processes and power plants. This paper provides an overview of the PSA process and its application for CO capture, along with a discussion of its advantages, limitations, and future research directions.

Beyond Homophily and Homogeneity Assumption: Relation-Based Frequency Adaptive Graph Neural Networks.

Graph neural networks (GNNs) have been playing important roles in various graph-related tasks. However, most existing GNNs are based on the assumption of homophily, so they cannot be directly generalized to heterophily settings where connected nodes may have different features and class labels. Moreover, real-world graphs often arise from highly entangled latent factors, but the existing GNNs tend to ignore this and simply denote the heterogeneous relations between nodes as binary-valued homogeneous edges.

A comprehensive investigation of a water and energy-based waste integrated system: Techno-eco-environmental-sustainability aspects.

In this work, a novel biomass gasifier combined energy system was offered for potable water, heating load, and power generation. The system included a gasifier, an S-CO2 cycle, a combustor, a domestic water heater, and thermal desalination unit. The plant was evaluated from various aspects, i.

Microplastic pollution threats coastal resilience and sustainability in Xiamen City, China.

Microplastics have raised growing awareness due to their ubiquity and menaces to coastal resilience and sustainability. The abundance, distribution, and characteristics of microplastics in water and organisms in Xiamen were evaluated. Results showed that the average abundance of microplastics in the surface water of Xiamen Bay was 1.

Microstructural Characterization of the As-Cast and Homogenized Al-Cu-Mg-Ag Alloy.

In this study, the as-cast microstructure and the evolution of the homogenized microstructure of large-scale industrialized Al-Cu-Mg-Ag heat-resistant aluminum alloy ingots were investigated by means of optical microscopy (OM), scanning electron microscopy (SEM), energy dispersive analysis (EDS), and differential scanning calorimetry (DSC). The results indicate that the dendritic segregation is evident in the ingot along the radial direction, and the grain boundaries are decorated with lots of net-shaped continuous eutectic structures. With the homogenization time extension and the homogenization temperature increase, the eutectic phases (i.

Transboundary microplastic pollution in Xiamen Bay and adjacent Jiulong River estuary after the outbreak of COVID-19.

Land-based transport from nearshore areas is a key pathway of microplastic (MP) pollution in the oceans. Therefore, transport, fate, and intervention on MPs necessitate an investigation of MP contamination in coastal regions. Here, MP pollution in the surface waters of Xiamen Bay and Jiulong River estuary was evaluated in 2021 after the outbreak of COVID-19.

Synthesis of Biodegradable Polyester-Polyether with Enhanced Hydrophilicity, Thermal Stability, Toughness, and Degradation Rate.

Novel poly(butylene succinate-butylene furandicarboxylate/polyethylene glycol succinate) (PBSF-PEG) was synthesized using two-step transesterification and polycondensation in the melt. There are characterized by intrinsic viscosity, GPC, H NMR, DSC, TGA, tensile, water absorption tests, and water degradation at different pH. GPC analysis showed that PBSF-PEG had high molecular weight with average molecular weight () up to 13.