Ratiometric fluorescence aptasensor for lysozyme based on the controllable excimer formation of perylene probe.

As an important biological indicator, the abnormity of the lysozyme level is closely related to many diseases. Herein, we devise a novel ratiometric fluorescence aptasensor for lysozyme based on the controllable excimer formation of a perylene probe, N, N'-bis(6-caproic acid)-3,4:9,10-perylene diimide (PDI) induced by cationic silver nanoparticles (Ag NPs). Binding of lysozyme aptamer with multiple phosphate groups to cationic Ag NPs strongly hinders the formation of excimer, yielding intense monomer fluorescence of PDI probe.

Intrinsic Multicolor Emissive Aliphatic Linear Polyphosphate Esters From the Charge-Transfer-Induced Enhanced Spatial Electronic Communication.

Unconventional fluorescent polymers are attracting increasing attention because of their excellent biocompatibility and wide applications. However, these polymers typically exhibit weak long-wavelength emission. Herein, three novel aliphatic linear polyphosphate esters are prepared via a one-pot polycondensation reaction.

NABP-BERT: NANOBODY®-antigen binding prediction based on bidirectional encoder representations from transformers (BERT) architecture.

Antibody-mediated immunity is crucial in the vertebrate immune system. Nanobodies, also known as VHH or single-domain antibodies (sdAbs), are emerging as promising alternatives to full-length antibodies due to their compact size, precise target selectivity, and stability. However, the limited availability of nanobodies (Nbs) for numerous antigens (Ags) presents a significant obstacle to their widespread application.

Effects of stand age and soil microbial communities on soil respiration throughout the growth cycle of poplar plantations in northeastern China.

Introduction: Many studies have identified stand age and soil microbial communities as key factors influencing soil respiration (Rs). However, the effects of stand age on Rs and soil microbial communities throughout the growth cycle of poplar ( cv.'I-214') plantations remain unclear.

The Co-Infection of ISKNV-II and RGNNV Resulting in Mass Mortality of Juvenile Asian Seabass (Lates calcarifer), Zhuhai, Southern China.

Infectious spleen and kidney necrosis virus (ISKNV) and nervous necrosis virus (NNV) are two common and important causative agents in marine-cultured fish. However, high viral loads of both ISKNV and NNV in the same clinical case is unusual. In this study, a mass mortality event of Asian seabass Lates calcarifer juveniles occurred in Zhuhai, the main Asian seabass cultured area in mainland China.

PreMLS: The undersampling technique based on ClusterCentroids to predict multiple lysine sites.

The translated protein undergoes a specific modification process, which involves the formation of covalent bonds on lysine residues and the attachment of small chemical moieties. The protein's fundamental physicochemical properties undergo a significant alteration. The change significantly alters the proteins' 3D structure and activity, enabling them to modulate key physiological processes.

Solubilization of K and P nutrients from coal gangue by .

Unlabelled: Accumulation of coal gangue (CG) poses significant risks to both human health and ecological systems, underscoring the urgent need for sustainable methods to utilize this abundant waste material effectively. In this study, we successfully screened and isolated the bacterial strain to enhance the solubilization of potassium (K) and phosphorus (P) nutrients from CG. The study employed XRD, FTIR, SEM, and HPLC techniques to investigate the underlying mechanisms of CG solubilization.

MSlocPRED: deep transfer learning-based identification of multi-label mRNA subcellular localization.

Subcellular localization of messenger ribonucleic acid (mRNA) is a universal mechanism for precise and efficient control of the translation process. Although many computational methods have been constructed by researchers for predicting mRNA subcellular localization, very few of these computational methods have been designed to predict subcellular localization with multiple localization annotations, and their generalization performance could be improved. In this study, the prediction model MSlocPRED was constructed to identify multi-label mRNA subcellular localization.

Effect of magnetic powder (FeO) on heterotrophic-sulfur autotrophic denitrification efficiency and electron transport system activity for marine recirculating aquacultural wastewater treatment.

As an efficient nitrogen removal process, heterotrophic-sulfur autotrophic denitrification (HSAD) has attracted extensive attention in wastewater treatment. However, the effects of magnetic powder (FeO) on the electron transport activity in HSAD process remain unclear. Therefore, in this study, a heterotrophic-sulfur autotrophic denitrification system was established to remove nitrogen from marine recirculating aquacultural wastewater for evaluating the effects of FeO.

ILYCROsite: Identification of lysine crotonylation sites based on FCM-GRNN undersampling technique.

Protein lysine crotonylation is an important post-translational modification that regulates various cellular activities. For example, histone crotonylation affects chromatin structure and promotes histone replacement. Identification and understanding of lysine crotonylation sites is crucial in the field of protein research.

A zero-background fluorescent probe for sensing and imaging of glutathione the "covalent-assembly" approach.

Developing selective and sensitive fluorescent probes for the detection of glutathione (GSH) concentration and intracellular distribution is of great significance for early diagnosis and treatment of diseases such as liver injury and cancer since GSH plays irreplaceable roles in regulating intracellular redox homeostasis. Herein, we present a new fluorescent probe that can be specifically activated by GSH through the conjugate addition and hydrolysis induced covalent-assembly approach for achieving zero-background interference fluorescence off-on sensing. Besides, the probe exhibited prominent selectivity and sensitivity, a low detection limit and cytotoxicity, thus successfully realizing specific real-time monitoring and tracking of GSH levels in living cells.

Effects of novel halogen-free flame-retardant binary additives on thermal stability, degradation kinetics and flame retardancy of cotton cellulose biomacromolecule.

The principal component of cotton fibers is the cellulose biological macromolecule. However, its highly flammable nature has significantly constrained its utilization in fields where flame retardancy is essential. Herein, in this work, a highly effective binary composite flame retardant coating (APP/MEL-SWCNHs) with ammonium polyphosphate and modified single-walled carbon nanohorns (MEL-SWCNHs) was chemically attached to cotton fabric.

Surface-based multimodal protein-ligand binding affinity prediction.

Motivation: In the field of drug discovery, accurately and effectively predicting the binding affinity between proteins and ligands is crucial for drug screening and optimization. However, current research primarily utilizes representations based on sequence or structure to predict protein-ligand binding affinity, with relatively less study on protein surface information, which is crucial for protein-ligand interactions. Moreover, when dealing with multimodal information of proteins, traditional approaches typically concatenate features from different modalities in a straightforward manner without considering the heterogeneity among them, which results in an inability to effectively exploit the complementary between modalities.

EPI-Trans: an effective transformer-based deep learning model for enhancer promoter interaction prediction.

Background: Recognition of enhancer-promoter Interactions (EPIs) is crucial for human development. EPIs in the genome play a key role in regulating transcription. However, experimental approaches for classifying EPIs are too expensive in terms of effort, time, and resources.

MoleMCL: a multi-level contrastive learning framework for molecular pre-training.

Motivation: Molecular representation learning plays an indispensable role in crucial tasks such as property prediction and drug design. Despite the notable achievements of molecular pre-training models, current methods often fail to capture both the structural and feature semantics of molecular graphs. Moreover, while graph contrastive learning has unveiled new prospects, existing augmentation techniques often struggle to retain their core semantics.

CarSitePred: an integrated algorithm for identifying carbonylated sites based on KNDUA-LNDOT resampling technique.

Carbonylated sites are the determining factors for functional changes or deletions in carbonylated proteins, so identifying carbonylated sites is essential for understanding the process of protein carbonylated and exploring the pathogenesis of related diseases. The current wet experimental methods for predicting carbonylated modification sites ae not only expensive and time-consuming, but also have limited protein processing capabilities and cannot meet the needs of researchers. The identification of carbonylated sites using computational methods not only improves the functional characterization of proteins, but also provides researchers with free tools for predicting carbonylated sites.

MLLT11 siRNA Inhibits the Migration and Promotes the Apoptosis of MDA-MB-231 Breast Cancer Cells.

Breast cancer is considered the most prevalent malignancy due to its high incidence rate, recurrence, and metastasis in women that makes it one of the deadliest cancers. The current study aimed to predict the genes associated with the recurrence and metastasis of breast cancer and to validate their effect on MDA-MB-231 cells. Through the bioinformatics analysis, the transcription factor 7 cofactor (MLLT11) as the target gene was obtained.

Chemical structure-aware molecular image representation learning.

Current methods of molecular image-based drug discovery face two major challenges: (1) work effectively in absence of labels, and (2) capture chemical structure from implicitly encoded images. Given that chemical structures are explicitly encoded by molecular graphs (such as nitrogen, benzene rings and double bonds), we leverage self-supervised contrastive learning to transfer chemical knowledge from graphs to images. Specifically, we propose a novel Contrastive Graph-Image Pre-training (CGIP) framework for molecular representation learning, which learns explicit information in graphs and implicit information in images from large-scale unlabeled molecules via carefully designed intra- and inter-modal contrastive learning.

A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.

The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for accelerating the process of drug discovery. However, chemical structures that play an important role in drug properties and high-order relations that involve a greater number of nodes are not tackled in current biomedical networks. In this study, we present a general hypergraph learning framework, which introduces Drug-Substructures relationship into Molecular interaction Networks to construct the micro-to-macro drug centric heterogeneous network (DSMN), and develop a multi-branches HyperGraph learning model, called HGDrug, for Drug multi-task predictions.

Influences of Four Kinds of Surfactants on Biodegradations of Tar-Rich Coal in the Ordos Basin by .

The biodegradation of tar-rich coal in the Ordos Basin was carried out by () under actions of four kinds of surfactants, namely, a biological surfactant (Rh), a nonionic surfactant (Triton X-100), an anionic surfactant (LAS), and a cationic surfactant (DTAB). The biodegradation rates under the actions of Triton X-100, LAS, Rh, DTAB, and the control group (without surfactant) were 59.8%, 54.

Bioactive Peptide Recognition Based on NLP Pre-Train Algorithm.

Bioactive peptides are defined as peptide sequences within a protein that can regulate important bodily functions through their myriad activities. With the development of machine learning, more computational methods were proposed for bioactive peptides recognition so that this task does not only rely on tedious and time-consuming wet-experiment. But the training and testing process of existing models are limited to small datasets, which affects model performance.

Regulation of Nontraditional Intrinsic Luminescence (NTIL) in Hyperbranched Polysiloxanes by Adjusting Alkane Chain Lengths: Mechanism, Film Fabrication, and Chemical Sensing.

Biocompatible polymers with nontraditional intrinsic luminescence (NTIL) possess the advantages of environmental friendliness and facile structural regulation. To regulate the emission wavelength of polymers with NTIL, the alkane chain lengths of hyperbranched polysiloxane (HBPSi) are adjusted. Optical investigation shows that the emission wavelength of HBPSi is closely related to the alkane chain lengths; namely, short alkane chains will generate relatively long-wavelength emission.