Physical Interaction Research Articles

Diphthamide synthesis is linked to the eEF2-client chaperone machinery.

The diphthamide modification of eukaryotic translation elongation factor (eEF2) is important for accurate protein synthesis. While the enzymes for diphthamide synthesis are known, coordination of eEF2 synthesis with the diphthamide modification to maintain only modified eEF2 is unknown. Physical and genetic interactions extracted from BioGRID show a connection between diphthamide synthesis enzymes and chaperones in yeast. This includes the Hsp90 co-chaperones Hgh1 and Cpr7. The respective co-chaperone deletion strains contained eEF2 without diphthamide. Notably, strains deficient in other co-chaperones showed no defect in the eEF2-diphthamide modification. Our results demonstrate that diphthamide synthesis involves not only Dph enzymes but also the eEF2-interacting co-chaperones Hgh1 and Cpr7 and may thus require a conformational state of eEF2 which is maintained by specific (co-)chaperones.

Central amygdala NPBWR1 neurons facilitate social novelty seeking and new social interactions.

The formation of new social interactions is vital for social animals, but the underlying neural mechanisms remain poorly understood. We identified CeANpbwr1 neurons, a population in central amygdala expressing neuropeptide B/W receptor-1 (NPBWR1), that play a critical role in these interactions. CeANpbwr1 neurons were activated during encounters with unfamiliar, but not with familiar, mice. Manipulations of CeANpbwr1 neurons showed that their excitation is essential for maintaining physical interactions with novel conspecifics. Activation of CeANpbwr1 neurons alleviated social deficits induced by chronic social defeat stress, suggesting therapeutic potential. Conversely, overexpression of human NPBWR1 in CeANpbwr1 neurons reduced activity of these neurons and impaired social interactions with unfamiliar mice. This effect was absent in a polymorphic variant of the human NPBWR1 gene (404A>T). These findings highlight how CeANpbwr1 neurons promote social novelty seeking and reveal a complex interplay between NPBWR1 genetic variations and social behavior.

An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models

Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data. The system employs a multi-layer architecture implemented through multiple LLMs, enabling not only rapid and effective processing of multi-source sensor data but also enhanced environmental perception through physical interactions. By leveraging the tool invocation and reasoning capabilities of LLM in conjunction with a coal mine safety knowledge base, the system achieves logical inference, anomalous data detection, and potential safety risk prediction. Furthermore, its memory functionality ensures the learning and utilization of historical experiences, providing a solid foundation for continuous assessment processes. This study established a comprehensive experimental framework integrating numerical simulation, scenario simulation, and real-world testing to evaluate the system through embodied intelligence. Experimental results demonstrate that the system effectively processes sensor data and exhibits rapid, efficient safety assessment capabilities during embodied interactions, offering an innovative solution for coal mine safety.

Unveiling the P-solubilizing potential of bacteria enriched from natural colonies of Red Sea Trichodesmium spp.

Phosphorus (P) is pivotal for all organisms, yet its availability is, particularly in the marine habitat, limited. Natural, puff-shaped colonies of Trichodesmium, a genus of diazotrophic cyanobacteria abundant in the Red Sea, have been demonstrated to capture and centre dust particles. While this particle mining strategy is considered to help evade nutrient limitation, details behind the mechanism remain elusive. This study explores P-solubilizing bacteria (PSB) residing within Trichodesmium's associated microbial community, their potential contribution to the host, and the possible implications for P cycling in marine ecosystems. Bacterial enrichment on YBCII medium resulted in 28 enrichment cultures, primarily comprising bacterial families such as Rhodobacteraceae, Alteromonadaceae and Burkholderiaceae. Five enrichment cultures were further grown on hydroxyapatite, revealing their ability to consume and release Nitrogen and P while forming strong physical interactions with the mineral. A drop in pH was observed, indicating acid production as the primary P-solubilizing pathway. Co-cultivation experiments confirmed a positive effect on Trichodesmium erythraeum strain IMS101 growth by the presence of putative PSBs. These results reveal that the enriched bacteria exhibit significant P-solubilizing activity, thus potentially increasing the bioavailability of P in seawater. Thus, PSB could play a vital role in maintaining the P balance in the Red Sea, supporting the growth of Trichodesmium spp. and other marine organisms. Overall, our results contribute to a deeper understanding of the P cycle in the Red Sea and have implications for developing novel strategies for P management in marine ecosystems.

Mapping neutron biological effectiveness for DNA damage induction as a function of incident energy and depth in a human sized phantom

We present new developments for an ab-initio model of the neutron relative biological effectiveness (RBE) in inducing specific classes of DNA damage. RBE is evaluated as a function of the incident neutron energy and of the depth inside a human-sized reference spherical phantom. The adopted mechanistic approach traces neutron RBE back to its origin, i.e. neutron physical interactions with biological tissues. To this aim, we combined the simulation of radiation transport through biological matter, performed with the Monte Carlo code PHITS, and the prediction of DNA damage using analytical formulas, which ground on a large database of biophysical radiation track structure simulations performed with the code PARTRAC. In particular, two classes of DNA damage were considered: sites and clusters of double-strand breaks (DSBs), which are known to be correlated with cell fate following radiation exposure. Within a coherent modelling framework, this approach tackles the variation of neutron RBE in a wide energy range, from thermal neutrons to neutrons of hundreds of GeV, and reproduces effects related to depth in the human-sized receptor, as well as to the receptor size itself. Besides providing a better mechanistic understanding of neutron biological effectiveness, the new model can support better-informed decisions for radiation protection: indeed, current neutron weighting (ICRP)/quality (U.S. NRC) factors might be insufficient for use in some radiation protection applications, because they do not account for depth. RBE predictions obtained with the reported model were successfully compared to the currently adopted radiation protection standards when the depth information is not relevant (at the shallowest depth in the phantom or for very high energy neutrons). However, our results demonstrate that great care is needed when applying weighting factors as a function of incident neutron energy only, not explicitly considering RBE variation in the target. Finally, to facilitate the use of our results, we propose look-up RBE tables, explicitly considering the depth variable, and an analytical representation of the maximal RBE vs. neutron energy.

Visual cognition in multimodal large language models

Abstract A chief goal of artificial intelligence is to build machines that think like people. Yet it has been argued that deep neural network architectures fail to accomplish this. Researchers have asserted these models’ limitations in the domains of causal reasoning, intuitive physics and intuitive psychology. Yet recent advancements, namely the rise of large language models, particularly those designed for visual processing, have rekindled interest in the potential to emulate human-like cognitive abilities. This paper evaluates the current state of vision-based large language models in the domains of intuitive physics, causal reasoning and intuitive psychology. Through a series of controlled experiments, we investigate the extent to which these modern models grasp complex physical interactions, causal relationships and intuitive understanding of others’ preferences. Our findings reveal that, while some of these models demonstrate a notable proficiency in processing and interpreting visual data, they still fall short of human capabilities in these areas. Our results emphasize the need for integrating more robust mechanisms for understanding causality, physical dynamics and social cognition into modern-day, vision-based language models, and point out the importance of cognitively inspired benchmarks.

Genome-wide association studies are enriched for interacting genes

BackgroundWith recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk.ResultsWe used genetic algorithms to measure fitness of gene-cell set proposals against a series of objective functions that capture data and annotations. The highest information objective function captured protein-protein interactions. We observed significantly greater fitness scores and subgraph sizes in foreground vs. matching sets of control variants. Furthermore, our model reliably identified known targets and ligand-receptor pairs, consistent with prior studies.ConclusionsOur findings suggested that application of genetic algorithms to association studies can generate a coherent cellular model of risk from a set of susceptibility variants. Further, we showed, using breast cancer as an example, that such variants have a greater number of physical interactions than expected due to chance.

Microcephaly protein ANKLE2 promotes Zika virus replication.

Orthoflaviviruses are positive-sense single-stranded RNA viruses that hijack host proteins to promote their own replication. Zika virus (ZIKV) is infamous among orthoflaviviruses for its association with severe congenital birth defects, notably microcephaly. We previously mapped ZIKV-host protein interactions and identified the interaction between ZIKV non-structural protein 4A (NS4A) and host microcephaly protein ankyrin repeat and LEM domain-containing 2 (ANKLE2). Using a fruit fly model, we showed that NS4A induced microcephaly in an ANKLE2-dependent manner. Here, we explore the role of ANKLE2 in ZIKV replication to understand the biological significance of the interaction from a viral perspective. We observe that ANKLE2 localization is drastically shifted to sites of NS4A accumulation during infection and that knockout of ANKLE2 reduces ZIKV replication in multiple human cell lines. This decrease in virus replication is coupled with a moderate increase in innate immune activation. Using microscopy, we observe dysregulated formation of virus-induced endoplasmic reticulum rearrangements in ANKLE2 knockout cells. Knockdown of the ANKLE2 ortholog in Aedes aegypti cells also decreases virus replication, suggesting ANKLE2 is a beneficial replication factor across hosts. Finally, we show that NS4A from four other orthoflaviviruses physically interacts with ANKLE2 and is also beneficial to their replication. Thus, ANKLE2 likely promotes orthoflavivirus replication by regulating membrane rearrangements that serve to accelerate viral genome replication and protect viral dsRNA from immune detection. Taken together with our previous results, our findings indicate that ZIKV and other orthoflaviviruses hijack ANKLE2 for a conserved role in replication, and this drives unique pathogenesis for ZIKV since ANKLE2 has essential roles in developing tissues.IMPORTANCEZIKV is a major concern due to its association with birth defects, including microcephaly. We previously identified a physical interaction between ZIKV NS4A and host microcephaly protein ANKLE2. Mutations in ANKLE2 cause congenital microcephaly, and NS4A induces microcephaly in an ANKLE2-dependent manner. Here, we establish the role of ANKLE2 in ZIKV replication. Depletion of ANKLE2 from cells significantly reduces ZIKV replication and disrupts virus-induced membrane rearrangements. ANKLE2's ability to promote ZIKV replication is conserved in mosquito cells and for other related mosquito-borne orthoflaviviruses. Our data point to an overall model in which ANKLE2 regulates virus-induced membrane rearrangements to accelerate orthoflavivirus replication and avoid immune detection. However, ANKLE2's unique role in ZIKV NS4A-induced microcephaly is a consequence of ZIKV infection of important developing tissues in which ANKLE2 has essential roles.

Functionalization of Polymer Surfaces for Organic Photoresist Materials.

Photoresists are thin film materials designed to transform an optimal image into a mechanical mask. Diverse exposure techniques such as photolithography induce modifications in the exposed areas that result in solubility changes that can then be selectively removed with appropriate agents (developers). Photoresist materials need to keep pace with the increasingly demand for feature size reduction. Typically, photoresist materials are organic polymers that can present small cross sections to the incoming photons, resulting in poor trade-off between resolution, sensitivity, or line-edge roughness. Photoresists also require a high etch-resistance relative to the substrate material, in order to preserve patterned features after the mechanical mask has been created. The main strategy to improve polymer performance during such processes is functionalization with different elements that can deliver higher efficiencies while keeping the chemical reactivity of the original polymer design. Here we consider a photoresist polymer structure model with general characteristics and investigate the functionalization of the polymer surface using density-functional theory (DFT), with a focus on reactive halogen adsorption. Physical and chemical surface reactions and the corresponding byproducts are identified, obtaining self-limitation thresholds for each specific functionalizing agent. Moreover, spectral signals of the modified polymer surfaces are analyzed in detail, to allow experimental validation of the proposed surface modifications. Finally, using ab initio molecular dynamics (AIMD) and real time time-dependent DFT (rt-TDDFT), we study the interaction of energetic ions and electrons with the modified polymer surfaces, to validate the obtained functionalizations as effectively enhanced etch-resistance strategies. The computational results provide valuable insights on the complex physical, chemical, and dynamic ion and electron interactions with functionalized polymer photoresists, with the immediate consequence of allowing the extraction of definite strategies for improving photoresist polymer performance.

Synergistic physical and chemical effects of MOF-derived porous Fe3C–NC to boost the performance of Li–S batteries

Lithium–sulfur (Li–S) batteries are one of the key objects of next-generation energy storage systems due to their high energy density and low-cost characteristics. However, the slow reaction kinetics and serious shuttle effect of lithium polysulfides (LiPSs) have hindered their practical application. In this work, metal-organic framework-derived Fe3C decorated nitrogen-doped carbon matrix (Fe3C–NC) composites were prepared to modify the separator to promote the reaction kinetics of Li–S batteries. The porous and conductive NC facilitates the trapping of LiPSs, rapid transfer of charge, and alleviated volume expansion, while the Fe3C–NC with optimum Fe3C content can significantly reduce the energy barrier of the electrochemical conversion reaction, accelerate the transport of lithium ions, and enhance the reaction kinetics of LiPSs, which are conducive to inhibit the shuttle effect through synergistic physical and chemical interactions. The Li–S battery with Fe3C–NC separator exhibits excellent cycle stability with an initial discharge specific capacity of 1099.19 mAh g−1 at 1 C and a low-capacity decay of 0.068% per cycle over 500 cycles. Even at a high S loading of 5.93 mg cm−2, it still delivers reliable cyclic stability with an initial discharge specific capacity of 903.65 mAh g−1 at 0.1 C. This work provides a convenient and effective method for the application of metallic materials combined with nitrogen-doped carbon matrix in high-performance Li–S batteries.

A Bioinspired Virus-Like Mechano-Bactericidal Nanomotor for Ocular Multidrug-Resistant Bacterial Infection Treatment.

Multidrug-resistant (MDR) bacteria and their associated biofilms are major causative factors in eye infections, often resulting in blindness and presenting considerable global health challenges. Presently, mechano-bactericidal systems, which combine distinct topological geometries with mechanical forces to physically induce bacterial apoptosis, show promising potential. However, the physical interaction process between current mechano-bactericidal systems and bacteria is generally based on passive diffusion or Brownian motion and lacks the force required for biofilm penetration; thus, featuring low antibacterial efficacy. Here, a biomimetic mechano-bactericidal nanomotor (VMSNT) is synthesized by functionalizing COOH-PEG-phenylboronic acid (PBA) on virus-like mesoporous silica, with subsequent partial coating of Au caps. Enhanced by self-thermophoresis capabilities and virus-like topological shapes, VMSNT significantly improves mechanical antibacterial effects and biofilm penetration. In addition, scanning electron microscope (SEM) and confocal laser scanning microscope (CLSM)analyses demonstrate that VMSNT can precisely target bacteria within the infection microenvironment, facilitated by PBA's ability to recognize and bind to the peptidoglycan on bacterial surfaces. Remarkably, VMSNT is also effective in eliminating MDR bacteria and reducing inflammation in mice models of methicillin-resistant Staphylococcus aureus (MRSA)-infected keratitis and endophthalmitis, with minimal adverse effects. Overall, such a nanomotor presents a promising approach for addressing the challenges of ocular MDR bacterial infections.

A Machine Learning Approach to Resolving Conflicts in Physical Human-Robot Interaction

A Machine Learning Approach to Resolving Conflicts in Physical Human-Robot Interaction

Mechanisms of interactions and the significance of different colloidal structures in the vertical transport of glyphosate in soils with contrasting mineralogies.

Soil is regarded as a natural repository for strongly adsorbed pollutants since glyphosate (GLY) is preferentially adsorbed by the inorganic fraction of the soil, which may greatly limits its leaching. In this way, understanding how clay mineralogy influences the sorption and transport processes of glyphosate in soils with different mineralogical characteristics is highly relevant. In this work, two clay mineralogy contrasting soils were used to evaluate GLY retention: a Oxisol (OX) with high levels of iron oxides (amorphous and crystalline) and a Inceptisol (IN) with a predominance of kaolinite. According to results obtained, the sorption process is influenced by more than one mechanism, including intraparticle diffusion, which is particularly favored at pH 4.00, and mass transfer across the boundary layer, which is favored at pH 6.50. When evaluating the adsorption isotherms, some differences associated with pH were also observed. At pH 4.00, good fits were obtained with the Freundlich model, suggesting electrostatic interaction between the compound and the soil. At pH 6.50, the best modeling involves the Langmuir-Freundlich model, indicating the occurrence of chemical and physical interactions. Desorption studies suggest that GLY sorption at pH 4.00 mostly involves the formation of inner-sphere complexes, while at pH 6.50, much of the sorption involves outer-sphere complexes. In column studies, GLY leaching was observed in both soils at concentrations between 0.01 and 0.02mgL-1. After pH correction by liming, differences were observed in the leached GLY concentration, especially in the second rain event in, which leached concentrations greater than 0.04mgL-1. These results confirm the strong sorption of GLY in the soil, as well as its evident mobilization through the soil column, probably due to colloid-facilitated transport.

A foundation model of transcription across human cell types.

Transcriptional regulation, which involves a complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate to unseen cell types and conditions. Here we introduce GET (general expression transformer), an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types1,2. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types3. GET also shows remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovers universal and cell-type-specific transcription factor interaction networks. We evaluated its performance in prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors and found that it outperforms current models4 in predicting lentivirus-based massively parallel reporter assay readout5,6. In fetal erythroblasts7, we identified distal (greater than 1 Mbp) regulatory regions that were missed by previous models, and, in B cells, we identified a lymphocyte-specific transcription factor-transcription factor interaction that explains the functional significance of a leukaemia risk predisposing germline mutation8-10. In sum, we provide a generalizable and accurate model for transcription together with catalogues of gene regulation and transcription factor interactions, all with cell type specificity.

Influence of Lens Power on IOL/Posterior Lens Capsule Interactions and IOL's PCO Potential.

Severely myopic eyes have been associated with high posterior capsule opacification (PCO) incidence. Although it has been reported that myopic eyes have weaker or more delayed capsule adhesion than emmetropic eyes, it is unclear whether/how dioptric power and posterior curvature of IOLs affect IOLs' affinity for the posterior lens capsule (PLC) and their PCO potential. To investigate this, acrylic foldable IOLs with increasing dioptric power of 6.0D (for high myopia), 20.0D, and 30.0D (for low/non-myopia) were tested on their binding affinity toward PLC and their ability to inhibit the proliferation and infiltration of lens epithelial cells (LECs) using an in vitro simulated human PLC (sPLC) model. We found that IOL power and posterior radius of curvature (PRC) had significant impacts on IOL/sPLC adhesion forces, which are in the following order: 20.0D ≈ 30.0D > 6.0D. Optical coherence tomography (OCT) images showed that loose binding between 6.0D IOLs and sPLC contributed to larger interface spaces and significantly greater LEC infiltration, proliferation, metabolic activity, and transdifferentiation compared to 20.0D and 30.0D IOLs. Statistical analyses showed that IOLs' PRC may have a substantial influence on IOL/sPLC physical interactions, LEC responses, and PCO incidence. The overall results suggest that the high PRC of low-diopter (6.0D) IOLs reduces their binding affinity toward the PLC, facilitates LEC reactions, thus causes high PCO incidence in myopic eyes. These findings strongly support that a new design to increase IOL posterior surfaces' PLC affinity may reduce PCO potential and increase safety for myopic patients.

Identification of CENPM as a key gene driving adrenocortical carcinoma metastasis via physical interaction with immune checkpoint ligand FGL1.

Distant metastasis occurs in the majority of adrenocortical carcinoma (ACC), leading to an extremely poor prognosis. However, the key genes driving ACC metastasis remain unclear. Weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were conducted to identify ACC metastasis-related genes. Data from RNA-seq and microarray were analyzed to reveal correlations of the CENPM gene with cancer, metastasis, and survival in ACC. Immunohistochemistry was used to assess CENPM protein expression. The impact of CENPM on metastasis behaviour was verified in ACC (H295R and SW-13) cells and xenograft NPG mice. DIA quantitative proteomics analysis, western blot, immunofluorescence, and co-immunoprecipitation assay were performed to identify the downstream target of CENPM. Among the 12035 analyzed genes, 363 genes were related to ACC metastasis and CENPM was identified as the hub gene. CENPM was upregulated in ACC samples and associated with metastasis and poor prognosis. Knockdown of CENPM inhibited proliferation, invasion, and migration of ACC cells and suppressed liver metastasis in xenograft NPG mice. Collagen-containing extracellular matrix signalling was primarily downregulated when CENPM was knocked down. FGL1, important components of ECM signalling and immune checkpoint ligand of LAG3, were downregulated following CENPM silence, overexpressed in human advanced ACC samples, and colocalized with CENPM. Physical interaction between CENPM and FGL1 was identified. Overexpression of FGL1 rescued migration and invasion of CENPM knockdown ACC cells. CENPM is a key gene in driving ACC metastasis. CENPM promotes ACC metastasis through physical interaction with the immune checkpoint ligand FGL1. CENPM can be used as a new prognostic biomarker and therapeutic target for metastatic ACC. CENPM is the key gene that drives ACC metastasis, and a robust biomarker for ACC prognosis. Silencing CENPM impedes ACC metastasis in vitro and in vivo by physical interaction with immune checkpoint ligand FGL1. FGL1 is overexpressed in ACC and promotes ACC metastasis.

Bio-inspired anti-swelling amyloid-fiber lysozyme adhesive for rapid wound closure and hemostasis.

Instant adhesion to wet biological surfaces and reduced swelling of tissue adhesives are crucial for rapid wound closure and hemostasis. However, previous strategies to reduce swelling were always accompanied by a decrease in the tissue bonding strength of the adhesive. Moreover, the irreducibility of the covalent bonds in currently reported adhesives results in the adhesives losing their tissue adhesive ability. To tackle the challenge, a superior anti-swelling coacervate adhesive possessing fast self-healing properties through physical interactions (electrostatic interactions, hydrogen bonding) and chemical crosslinking (Schiff base reaction) was obtained with aldehyde-modified γ-PGA (γ-PGA-CHO), a natural lysozyme (LZM) and an amyloid fiber reduced lysozyme (RLZM). The instant shear adhesion strength and burst pressure tolerance of the adhesive on wet pig intestine reached 50.8 kPa (2.6 times that of CA glue) and 142.5 mmHg (5.9 times that of CA glue), and it maintained an adhesion strength of 37.4 kPa after exposure to the physical environment for 12 h and the swelling rate was only 34.0% underwater. The in vitro and in vivo experiments provided the coacervate adhesive with potential applicability for emergency rescue and wound care scenarios.

Cold atmospheric plasma restores skewed macrophage polarization in triple negative breast cancers via enhancing KAT6A acetylation.

Breast cancer is the most common cancer diagnosed and the second leading cause of death of cancer among women in the world, due to inappropriate diagnosis and choice of therapeutic approach. The molecular profiles of breast cancers may switch among subtypes during treatments, leading to a phenotype such as triple negative breast cancers (TNBCs) that is more difficult to treat. Cold atmospheric plasma (CAP) has been demonstrated by many studies on its efficacy in arresting the malignancies of multiple cancer types including TNBCs that lack surface receptor expression and are thus the most difficult to treat among breast cancers. By analyzing the genetic testing reports of a breast cancer clinical case misdiagnosed with BRCA1 mutation, we characterized the importance of KAT6A in driving disease progression of this patient. Through exploring genes differentially regulated under physical interactions between KAT6A and SMAD3, we proposed the KAT6A/SMAD3/IL6/CD163 molecular axis capable of driving macrophage M2 polarization in the immune microenvironment of breast cancers. Through examining the expression landscapes of KAT6A at both transcriptional and translational levels, we proposed a possible role of KAT6A acetylation in reducing its ability in acetylating SMAD3 and subsequent oncogenic roles. Through analyzing the whole transcriptome and acetylome of TNBC cells in response to CAP treatment, we predicted the efficacy of CAP in resolving TNBCs via increasing KAT6A acetylation, which were validated both in vitro and in vivo. Our study, for the first time, presented the role of CAP in re-polarizing macrophages from the M2 to M1 state in the microenvironment of breast cancers via elevating KAT6A acetylation, and warranted careful interpretation of patients' genetic testing reports by clinicians for the sake of minimizing mortalities due to inappropriate choice of therapeutic modalities.

Tetracycline removal from aqueous media and hospital wastewater using a magnetic composite of mango lignocellulosic kernel biochar/MnFe2O4/Cu@Zn-BDC MOF.

This study explored the use of mango lignocellulosic kernel biochar (MKB) modified with MnFe2O4 magnetic nanoparticles and a Cu@Zn-BDC metal-organic framework (MOF) (MKB/MnFe2O4/Cu@Zn-BDC MOF) for tetracycline (TC) removal from aqueous solutions and hospital wastewater. The modified biochar exhibited strong magnetic properties (19.803 emu/g) and a specific surface area of 30.456 m2/g, facilitating easy separation after adsorption. Using Response Surface Methodology-Central Composite Design (RSM-CCD), the adsorption model demonstrated high accuracy (F-value: 315.510, p < 0.0001, R2 = 0.9959). Thermodynamic analysis indicated that the process was endothermic and spontaneous, driven by physical interactions, with positive enthalpy and negative Gibbs free energy values. The pseudo-second-order kinetic model best described the adsorption, highlighting significant chemical interactions, while the Freundlich isotherm suggested adsorption on heterogeneous surfaces. The maximum TC adsorption capacities for MKB and its magnetic composite were 27.050 mg/g and 42.670 mg/g, respectively. The RL, n, and E parameters confirmed the desirability and physical nature of the interactions (0-1,〉1, and < 8 kJ/mol, respectively). The intraparticle diffusion model indicated multiple mechanisms were involved, and the biochar maintained excellent performance across reuse cycles, making it a highly effective and reusable adsorbent for water and wastewater treatment.

Agricultural relevance of fungal mycelial growth-promoting bacteria: Mutual interaction and application.

Bacterial-fungal interaction (BFI) is found ubiquitously and plays important roles in various environmental settings, thus being responsible for numerous biophysical and chemical processes in nature. In terms of BFI, the capacity of the bacterium to enhance the growth of fungal mycelia is an indication of the roles of the bacterium in mutualistic interaction, since increasing mycelial growth results in higher changes for fungal establishment. In this review, the interaction between mycelial growth-promoting bacterium (MGPB) and its fungal counterpart in agricultural settings and the promotion of mycelial growth as an outcome of mutual interactions in various environmental niches were evaluated. The beneficial relationships included endohyphal interaction, association of bacteria with mushrooms, bacteria-mycorrhizae symbiosis, and geomicrobiology. Furthermore, the mode of interaction between MGPB and their fungal counterparts was also explained. There are two fundamental modes of interaction involved, namely physical interaction and chemical interaction. The first involved endosymbiosis and bacterial attachment, while the latter comprised quorum sensing, volatile metabolites, enzymatic activity, and chemotaxis. Particularly, the growth stimulants secreted by the bacteria, which promote the growth of hyphae, are discussed thoroughly. Moreover, the chance of trade-off metabolites between fungi and their MGPBs as a consequence of mutualistic interaction will also be observed. Finally, the agricultural relevance of BFI, particularly the relation between fungi and MGPBs, will also be provided, including key technologies and future bioprospects for optimum application.