Search Results (334)

As the most unstable crystalline form of calcium carbonate, vaterite is rarely found in nature due to being highly prone to phase transitions. However, its high specific surface area, excellent biocompatibility, and high solubility properties have led to a research boom and the following breakthroughs in the last two decades: (1) From primitive calculations and spectroscopic analyses to modern multidimensional research methods combining calculations and experiments, the crystal structure of vaterite has turned from early identifications in orthorhombic and hexagonal crystal systems to a complex polymorphic structure within the monoclinic crystal system. (2) The formation process of vaterite not only conforms to the classical crystal growth theory but also encompasses the nanoparticle aggregation theory, which incorporates the concepts of oriented nanoparticle assembly and mesoscale transformation. (3) Regardless of the conditions, the formation of vaterite depends on an excess of CO₃²⁻ relative to Ca²⁺, and its stability duration relates to preservation conditions. (4) Vaterite demonstrates significant value in biomedical applications—including bone repair scaffolds, targeted drug carriers, and antibacterial coating materials—leveraging its porous structure, high specific surface area, and exceptional biocompatibility. While it also shows utility in environmental pollutant adsorption and general coating technologies, the current research remains predominantly concentrated on its medical applications. Currently, the rapid transformation of vaterite presents the primary limitation for its industrial application. Future research should prioritize investigating its formation kinetics and stability. Full article

Inelastic electron tunneling spectroscopy (IETS) has emerged as a powerful vibrational spectroscopy technique for molecular electronic junctions, providing unique insights into molecular vibrations and electron–phonon coupling at the nanoscale. In this review, we present a comprehensive overview of IETS in molecular junctions, tracing its development from foundational principles to the latest advances. We begin with the theoretical background, detailing the mechanisms by which inelastic tunneling processes generate vibrational fingerprints of molecules, and highlighting how IETS complements optical spectroscopies by accessing electrically driven vibrational excitations. We then discuss recent progress in experimental techniques and device architectures that have broadened the applicability of IETS. Central focus is given to emerging applications of IETS over the last decade: molecular sensing (identification of chemical bonds and conformational changes in junctions), thermoelectric energy conversion (probing vibrational contributions to molecular thermopower), molecular switches and functional devices (monitoring bias-driven molecular state changes via vibrational signatures), spintronic molecular junctions (detecting spin excitations and spin–vibration interplay), and advanced data analysis approaches such as machine learning for interpreting complex tunneling spectra. Finally, we discuss current challenges, including sensitivity at room temperature, spectral interpretation, and integration into practical devices. This review aims to serve as a thorough reference for researchers in physics, chemistry, and materials science, consolidating state-of-the-art understanding of IETS in molecular junctions and its growing role in molecular-scale device characterization. Full article

The increasing circulation of multi-drug-resistant pathogens, coupled with the sluggish development of new antibiotics, is weakening our capacity to combat human infections, resulting in elevated death tolls. To address this worldwide crisis, antimicrobial peptides (AMPs) are viewed as promising substitutes or adjuvants for combating bacterial infections caused by multidrug-resistant organisms. Here, the antimicrobial activity and structural characterization of a novel 13-amino acid cationic peptide named RKW (RKWILKWLRTWKK-NH2), designed based on known AMPs sequences and the identification of a key tryptophan-rich structural motif, were described. RKW displayed a broad-spectrum and potent antimicrobial and antibiofilm activity against Gram-positive and Gram-negative pathogens, including ESKAPE bacteria and fungi with minimal inhibitory concentrations (MBC) ranging from 5 µM to 20 μM. Structural results by fluorescence and Circular Dichroism (CD) spectroscopy revealed that the peptide was folded into a regular α-helical conformation in a membrane-like environment, remaining stable in a wide range of pH and temperature for at least 48 h of incubation. Furthermore, RKW showed low toxicity in vitro against mammalian fibroblast cells, indicating its potential as a promising candidate for the development of new antimicrobial or antiseptic strategies. Full article

Mercury intrusion porosimetry (MIP) is commonly used to characterize coal pore structures, but conformance effect and compression effect can overestimate pore volume. This study uses MIP data from coal with varying metamorphic degrees in China to compare existing correction methods and propose a new approach based on apparent and true density for pore volume correction under no confining pressure. The study also analyzes the impact of conformance and compression effects on MIP data. Correctly identifying the “actual initial intrusion pressure” and “closure pressure” is essential for accurate data correction. The fractal dimension method offers a more robust theoretical foundation, while the conformance and intrusion pressure identification method is simpler. The stage correction method is reliable but requires repeated MIP tests, adding to the workload. The new method, which corrects both coal matrix and mercury volume compression, provides a simpler and reliable solution. Results show that conformance volume accounts for 9.91–83.26% of the apparent mercury intrusion volume and increases with coal metamorphism. Coal matrix volume compression represents 99.86–99.90% of the corrected total volume, with mercury volume compression being negligible. The corrected pore volume decreases as coal metamorphism increases, indicating the effectiveness and simplicity of the proposed method. Full article

Xinjiang Brown cattle is an elite dual-purpose breed (raised for dairy and beef) developed in China. To elucidate its genomic architecture, we conducted whole-genome resequencing of 169 Xinjiang Brown cattle, followed by structural variation (SV) detection and a genome-wide association study (GWAS). We identified 71,668 SVs, among which deletions were the most prevalent, followed by translocations, inversions, duplications, and insertions. We further identified 1286 high-frequency SVs involving 2016 protein-coding genes. Through functional enrichment analysis of these genes, we revealed associations of genetic variation at genomic positions near genes implicated in immune response and disease resistance (NFKBIZ and PTPRT), growth and development (HDAC4 and MEF2A), and milk production (TP63, FABP4, and MEF2A). GWAS analysis of 31 body conformation traits revealed 58 SVs significantly associated with five traits (chest width, rear udder width, udder depth, rump width, and heel depth) at the genome-wide level. Additionally, nine candidate genes (CLINT1, EBF1, PAM16, GRIP1, CFAP54, SLC22A16, DOK5, ETAA1, and IPMK) were identified as potentially involved in the genetic regulation of body conformation traits. These findings provide novel insights for genetic improvement strategies and indicate that precision breeding could further enhance the production performance of this breed in the future. Full article

Background: Prunus plants are widely distributed across Asia and Europe, yet their intricate phylogenetic relationships pose significant challenges for systematic studies and interspecies identification. Objectives: To clarify the mitochondrial and chloroplast genomes of Prunus salicina var. cordata, and to reveal its evolutionary relationship and historical gene flow with domesticated cherries. Methods: In this study, we assembled, annotated, and analyzed the first mitochondrial and chloroplast genomes of P. salicina var. cordata, a species within the Prunus genus. Results: The mitochondrial genome was found to be 484,858 base pairs in length, exhibiting a typical circular conformation. Phylogenetic analysis revealed a close evolutionary relationship between P. domestica and P. salicina, suggesting historical gene flow between these two species last genomes; mitochondrial genomes; phylogeny analysis. Conclusions: To provide a genomic basis for resolving the phylogenetic controversies within the Li-associated plants, elucidating their evolutionary mechanisms, and formulating breeding strategies. Full article

The transfer of development rights (TDR) is a legal instrument, introduced in 1961, that allows transferring of development rights from a land parcel where restrictions are imposed (sending parcel) to another land parcel (receiving parcel). TDRs aim to ensure environmental and cultural heritage protection with respect to the rights of individual land parcel owners, thus constituting a high impact tool in sustainability and urban planning. Although extensive research has been applied in defining development rights’ transfer zones (RTZ), mainly in the proximity of the sending parcels, limited is the research on defining this “proximity”. This research examines the process of identifying the areas that can host RTZs, using as a case study the implementation of TDR in Greece. Greek TDR legislation was challenged by the Hellenic Council of the State as non-conformant to the principles of rational urban and spatial development, thus requiring the identification of the areas that can host rights’ transfer zones. In order to align with the Council’s decisions, the Ministry of Environment and Energy introduced Law 4759/2020 along with Technical Requirements for the delineation of development rights’ transfer zones. Given that restrictions on the transfer of development rights do not exist in all municipalities in Greece, multi-criteria analysis was used to propose municipal units where studies on development rights’ transfer zones (RTZs) could be conducted, based on the number of sending parcels, geographic and urban planning requirements, and funding limitations. The analysis resulted in 83 municipal units, covering about 75% of the country’s need for development rights’ transfer. The deployment of RTZ studies in the selected areas would benefit the owners of the restricted land parcels (where existing TDR titles are currently inactive or where new ones cannot be issued) and assist urban space management. Full article

This study applies multivariate data analysis to deconvolute the spectral profiles of the Amide III region in the infrared spectra of gingival crevicular fluid (GCF). This reveals the impact of major oral diseases, such as dental caries and periodontal diseases, on the transformation of the secondary structure of GCF proteins. A two-stage analytical approach was employed: first, principal component analysis (PCA) was performed to establish the main factors of variation in the data, followed by pairwise comparisons of the samples based on the results of the Amide III profile deconvolution. The analysis also accounted for comorbidities, such as oncological and gastrointestinal diseases. This approach allowed for the identification of subtle differences in the composition and conformation of the secondary structure of GCF proteins while accounting for the superposition of multiple influencing factors. This methodology was effective in identifying biomarkers of oral diseases in GCF. For the first time, it has been demonstrated that the relative content of the β-sheet-associated component in the spectral profile of the secondary structure element of the protein fraction of GCF serves as a statistically significant marker for dental caries, regardless of the presence or absence of other diseases. Additionally, a significant decrease in the relative content of α-helix structures was observed in GCF from patients with oncological diseases. The changes in the spectral profile of the Amide III band of GCF identified in this study have not been previously detected using molecular spectroscopy, correlated with the secondary structure of proteins, or analyzed using multivariate analysis methods. Full article

Comparative studies using lysophosphatidic acid (LPA) and the synthetic agonist, oleoyl-methoxy glycerophosphothionate (OMPT), in cells expressing the LPA₃ receptor revealed differences in the action of these agents. The possibility that more than one recognition cavity might exist for these ligands in the LPA₃ receptor was considered. We performed agonist docking studies exploring the whole protein to obtain tridimensional details of the ligand–receptor interaction. Functional in cellulo experiments using mutants were also executed. Our work includes blind docking using the unrefined and refined proteins subjected to hot spot predictions. Distinct ligand protonation (charge −1 and −2) states were evaluated. One LPA recognition cavity is located near the lower surface of the receptor close to the cytoplasm (Lower Cavity). OMPT displayed an affinity for an additional identification cavity detected in the transmembrane and extracellular regions (Upper Cavity). Docking targeted to Trp102 favored binding of both ligands in the transmembrane domain near the extracellular areas (Upper Cavity), but the associating amino acids were not identical due to close sub-cavities. A receptor model was generated using AlphaFold3, which properly identified the transmembrane regions of the sequence and co-modeled the lipid environment accordingly. These two models independently generated (with and without the membrane) and adopted essentially the same conformation, validating the data obtained. A DeepSite analysis of the model predicted two main binding pockets, providing additional confidence in the predicted ligand-binding regions and support for the relevance of the docking-based interaction models. In addition, mutagenesis was performed of the amino acids of the two detected cavities. In the in cellulo studies, LPA action was much less affected by the distinct mutations than that of OMPT (which was almost abolished). Therefore, docking and functional data indicate the presence of distinct agonist binding cavities in the LPA₃ receptor. Full article

This study proposes rule-based multi-task deep learning for highly efficient rice lodging identification by introducing prior knowledge to improve the efficiency of disaster investigation using unmanned aerial vehicle (UAV) images. Multi-task learning combines rule-based loss functions and learns the best loss function to train a model conforming to prior knowledge. Rule-based and multi-task learning optimizes the integration of rule-based and deep learning networks and dynamically adjusts the loss function model. Lastly, edge computing is deployed on the edge computing host to improve model efficiency for instant inference. This study inferred fifty-one 4096 × 4096 tagged UAV images taken in 2019 and calculated the confusion matrix and accuracy indices. The recall rate of the modified model in the normal rice category was increased by 13.7%. The affecting factor may be caused by changes in spatial resolution and differences in spectral values in different periods, which can be solved by adding part of the 2019 image transfer training to adjust the learning characteristics. The prior knowledge of a deep learning network can be deployed on edge computing devices to collect high-resolution images by regional routes planning within inferred disaster-damaged farmlands, providing efficient disaster survey tools with high detection accuracy. Full article

Natural products play a crucial role in drug discovery because of their structural diversity and biological activity. However, identifying their molecular targets remains a challenge. Traditional target identification approaches such as affinity-based protein profiling and activity-based protein profiling are limited by the need for chemical modification or reactive groups in natural products. The emergence of label-free techniques offers a powerful alternative for studying drug–target engagement in a physiological context. In particular, the cellular thermal shift assay (CETSA) exploits ligand-induced protein stabilization—a phenomenon where ligand binding enhances a protein’s thermal stability by reducing conformational flexibility—to assess drug binding without requiring chemical modifications. CETSA’s integration with advanced mass spectrometry and high-throughput platforms has dramatically expanded proteome coverage and sensitivity, enabling the simultaneous quantification of thousands of proteins and the identification of low-abundance targets in native cellular environments. This review highlights the application of key CETSA-based methods to target identification in natural products including Western blot-based CETSA, isothermal dose–response CETSA, mass spectrometry-based CETSA, and high-throughput CETSA. Case studies are presented that demonstrate their effectiveness in uncovering the mechanisms of action of different drugs. The current limitations of CETSA-based strategies are also explored, and future improvements to optimize their potential for drug discovery are discussed. Integrating CETSA with complementary approaches can enhance the target identification accuracy and efficiency for natural products and ultimately advance development of therapeutic applications. Full article

Acetylcholinesterase (AChE) is a critical enzyme involved in neurotransmission by hydrolyzing acetylcholine at the synaptic cleft, making it a key target for drug discovery, particularly in the treatment of neurodegenerative disorders such as Alzheimer’s disease. Computational approaches, particularly molecular docking and molecular dynamics (MD) simulations, have become indispensable tools for identifying and optimizing AChE inhibitors by predicting ligand-binding affinities, interaction mechanisms, and conformational dynamics. This review serves as a comprehensive guide for future research on AChE using molecular docking and MD simulations. It compiles and analyzes studies conducted over the past five years, providing a critical evaluation of the most widely used computational tools, including AutoDock, AutoDock Vina, and GROMACS, which have significantly contributed to the advancement of AChE inhibitor screening. Furthermore, we identify PDB ID: 4EY7, the most frequently used AChE crystal structure in docking studies, and highlight Donepezil, a well-established reference molecule widely employed as a control in computational screening for novel inhibitors. By examining these key aspects, this review aims to enhance the accuracy and reliability of virtual screening approaches and guide researchers in selecting the most appropriate computational methodologies. The integration of docking and MD simulations not only improves hit identification and lead optimization but also provides deeper mechanistic insights into AChE–ligand interactions, contributing to the rational design of more effective AChE inhibitors. Full article

Chinese herbal medicines have played a significant role in the development of new and effective drugs, but how to identify the active ingredients from complex extracts of traditional Chinese herbal medicines was a research difficulty. In recent years, few studies have focused on high-efficiency identification of small-molecule inhibitors of Programmed Death Ligand 1 with lower antigenicity and flexible structure tunability. In order to identify small molecule inhibitors of PD-L1 from complex Chinese herbal extracts, this study established a protein–ligand trapping system based on high-performance liquid chromatography coupled with a photo-diode array detector, ion trap/quadrupole time-of-flight tandem mass spectrometry, and a Programmed Death Ligand 1 affinity chromatography unit (ACPD-L1-HPLC-PDA-IT-TOF (Q-TOF)-MS) to rapidly screen and identify small-molecule inhibitors of Programmed Death Ligand 1 from Toddalia asiatica (L.) Lam. Fourteen components were then identified as PD-L1 binders, and surface plasmon resonance (SPR) validation results showed that six of them—magnoflorine (6), nitidine (22), chelerythrine (24), jatrorrhizine (13), toddaculin (68), and toddanol (45)—displayed PD-L1 binding activity. Laser scanning confocal microscopy results demonstrated that these compounds effectively inhibited the binding of PD-1 to PD-L1 in a dose-dependent manner. Additionally, flow cytometry analysis indicated they could promote human lung cancer cell line (A549) apoptosis when co-cultured with Peripheral Blood Mononuclear Cells (PBMCs). The system’s innovation lies in its first integration of dynamic protein–ligand trapping with multi-dimensional validation, coupled with high-throughput screening capacity for structurally diverse natural products. This workflow overcomes traditional phytochemical screening bottlenecks by preserving native protein conformations during affinity capture while maintaining chromatographic resolution, offering a transformative template for accelerating natural product-derived immunotherapeutics through the PD-1/PD-L1 pathway. Full article

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide–protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations. Full article

The evaluation of the geometric conformity of in-layer features in Additive Manufacturing (AM) remains a challenge due to low contrast between the features and the background, textural variations, imaging artifacts, and lighting conditions. This research presents a novel in situ vision-based framework for AM to identify in real-time in-layer features and estimate their shape and printed dimensions and then compare them with the as-processed layer features to evaluate geometrical differences. The framework employs a composite approach to segment features by combining simple thresholding for external features with the Chan–Vese (C–V) active contour model to identify low-contrast internal features. The effect of varying C–V parameters on the segmentation output is also evaluated. The framework was evaluated on a 20.000 mm × 20.000 mm multilayer part with internal features (two circles and a rectangle) printed using Fused Deposition Modeling (FDM). The segmentation performance of the composite method was compared with traditional methods with the results showing the composite method scoring higher in most metrics, including a maximum Jaccard index of 78.34%, effectively segmenting high- and low-contrast features. The improved segmentation enabled the identification of feature geometric differences ranging from 1 to 10 pixels (0.025 mm to 0.250 mm) after printing each layer in situ and in real time. This performance verifies the ability of the framework to detect differences at the pixel level on the evaluation platform. The results demonstrate the potential of the framework to segment features under different contrast and texture conditions, ensure geometric conformity and make decisions on any differences in feature geometry and shape. Full article