Matrix Embedding Research Articles

Polysaccharides' Structures and Functions in Biofilm Architecture of Antimicrobial-Resistant (AMR) Pathogens.

Bacteria and fungi have developed resistance to the existing therapies such as antibiotics and antifungal drugs, and multiple mechanisms are mediating this resistance. Among these, the formation of an extracellular matrix embedding different bacterial cells, called biofilm, is an effective strategy through which bacterial and fungal cells are establishing a relationship in a unique environment. The biofilm provides them the possibility to transfer genes conferring resistance, to prevent them from desiccation and to impede the penetration of antibiotics or antifungal drugs. Biofilms are formed of several constituents including extracellular DNA, proteins and polysaccharides. Depending on the bacteria, different polysaccharides form the biofilm matrix in different microorganisms, some of them involved in the first stage of cells' attachment to surfaces and to each other, and some responsible for giving the biofilm structure resistance and stability. In this review, we describe the structure and the role of different polysaccharides in bacterial and fungal biofilms, we revise the analytical methods to characterize them quantitatively and qualitatively and finally we provide an overview of potential new antimicrobial therapies able to inhibit biofilm formation by targeting exopolysaccharides.

Phase diagram of the square-lattice Hubbard model with Rashba-type antisymmetric spin-orbit coupling

We clarify the ground-state phase diagram of the half-filled square-lattice Hubbard model with Rashba spin-orbit coupling (SOC) characterized by the spin-split energy bands due to broken inversion symmetry. Although the Rashba metals and insulating magnets have been studied well, the intermediate interaction strength of the system remained elusive due to the lack of appropriate theoretical tools to unbiasedly describe the large-scale magnetic structures. We complementarily apply four different methods (sine-square deformed mean-field theory, random phase approximation, Luttinger-Tisza method, and density matrix embedding theory) and succeed in capturing the incommensurate spin-density-wave (SDW) phases with very long spatial periods which were previously overlooked. The transition to the SDW phases from the metallic phase is driven by an unprecedented instability that nests the two parts of the Fermi surface carrying opposite spins. For large SOC, the spiral, stipe, and vortex phases are obtained, when the four Dirac points exist near the Fermi level and their whole linear dispersions nest by a wavelength $\ensuremath{\pi}$, opening a band gap. These two types of transition provide Fermiology that distinguishes the antisymmetric SOC systems, generating a variety of magnetic phases that start from the relatively weak correlation regime and continue to the strongly interacting limit.

Force Sensor Based on FBG Embedded in Silicone Rubber

Force sensing is a key enabler for getting haptic feedback, useful in a variety of applications, especially in the fields of robotics, automation, and health. Indeed, equipping machines, vehicles, robots, and even humans with force sensors provides controlled processes and production, safe and enhanced external interaction, and capability to perform efficient manipulation and precise movements. Aiming to develop an alternative solution to electrical force sensors, in this work a fiber Bragg grating (FBG) is embedded inside a patch made of silicone rubber. Such embedding strategy allows to make the FBG sensitive to the force variations, obtain a flexible patch having a moldable shape, and protect the most fragile areas of the optical fiber. Moreover, due to its high flexibility and stretchability, the sensing patch can be easily employed as portable and wearable device. Besides reporting fabrication process and results of the performed force tests, this work provides a systematic study of the FBG embedding in a silicone matrix. Indeed, for this purpose, three sensing patches having different thicknesses are developed and tested in temperature, strain, and force, finding that the patch thickness influences the sensing performances of the device. The resulting force sensitivity varies in the range from 9.2 to 19.0 pm/N, based on the sensor thickness. Temperature sensitivity, instead, is comparable with respect to bare FBGs, while strain sensitivity is enormously reduced, obtaining a patch able to insulate the FBG from the strain variations.

Structure optimization with stochastic density functional theory.

Linear-scaling techniques for Kohn-Sham density functional theory are essential to describe the ground state properties of extended systems. Still, these techniques often rely on the localization of the density matrix or accurate embedding approaches, limiting their applicability. In contrast, stochastic density functional theory (sDFT) achieves linear- and sub-linear scaling by statistically sampling the ground state density without relying on embedding or imposing localization. In return, ground state observables, such as the forces on the nuclei, fluctuate in sDFT, making optimizing the nuclear structure a highly non-trivial problem. In this work, we combine the most recent noise-reduction schemes for sDFT with stochastic optimization algorithms to perform structure optimization within sDFT. We compare the performance of the stochastic gradient descent approach and its variations (stochastic gradient descent with momentum) with stochastic optimization techniques that rely on the Hessian, such as the stochastic Broyden-Fletcher-Goldfarb-Shanno algorithm. We further provide a detailed assessment of the computational efficiency and its dependence on the optimization parameters of each method for determining the ground state structure of bulk silicon with varying supercell dimensions.

Analysis of Stress Intensity Factor of a Fibre Embedded in a Matrix

The analytical or numerical determination of the stress intensity factor (SIF) in cracked bodies usually assumes the body to be isolated. However, in fibre-reinforced composites, the fibre, which is the main load-carrying component, is embedded in a matrix. To clarify the effect the embedding matrix has on the SIF of the fibre, we propose a 3D computational model of an orthotropic fibre embedded in an isotropic matrix, and compute the SIF using the J-integral method. A parametric analysis based on dimensionless variables explores the effect of the fibre–matrix stiffness ratio as well as the effect of the degree of elastic orthotropy of the fibre. The results show that the SIF is strongly influenced by both factors, and that the matrix reduces the SIF by limiting the crack opening.

Long-Lived Dynamic Room Temperature Phosphorescence from Carbon Dots Based Materials.

As a type of room temperature phosphorescence (RTP) material, carbon dots (CDs) always show short lifetime and low phosphorescence efficiency. To counter these disadvantages, several strategies, such as embedding in rigid matrix, introducing of heteroatom, crosslink-enhanced emission, etc., are well developed. Consequently, lotsof CDs-based RTP materials are obtained. Doping of CDs into various matrix is the dominant method for preparation of long-lived CDs-based RTP materials so far. The desired CDs@matrix composites always display outstanding RTP performances. Meanwhile, matrix-free CDs and carbonized polymer dots-based RTP materials are also widely developed. Amounts of CDs possessing ultra-long lived, multiple colored, and dynamic RTP emission are successfully obtained. Herein, the recent progress achieved in CDs-based RTP materials as well as the corresponding efficient strategies and emission mechanisms are summarized and reviewed in detail. Due to CDs-based RTP materials possess excellent chemical stability, photostability and low biological toxicity, they exhibit great application potential in the fields of anti-counterfeiting, data encryption, and biological monitoring. The application of the CDs-based RTP materials is also introduced in this review. As a promising functional material, development of long wavelength RTP emitting CDs with long lifetime is still challengeable, especially for the red and near-infrared emitting RTP materials.

Simultaneous Laplacian embedding and subspace clustering for incomplete multi-view data

In many real multi-view data, some views often lose some information, resulting in incomplete views. It is a challenging task to extract and fuse valuable information from the multi-view data with incomplete views for improving clustering performance. Incomplete multi-view clustering (IMC) is designed to fully mine patterns in data to reduce the negative impact of missing views. Many previous IMC methods are committed to discovers a consensus representation shared by all views. Nevertheless, this representation could severely deviate from the inherent representation of original data due to the loss of information caused by incomplete views. Besides, most existing spectral clustering-based multi-view subspace clustering independently performs similarity graph learning, Laplacian embedding, and discrete indicator matrix learning. This multi-step strategy might result in sharp performance degradation. In this paper, we propose a novel IMC method, referred to as Simultaneous Laplacian Embedding and Subspace Clustering (SLESC), to address the above issues. Specifically, in the paradigm for data self-representation, the proposed SLESC method learns a similarity graph for each view instead of learning a consensus graph for invoking traditional spectral clustering. Similarity graph learning, Laplacian embedding learning, weighting for each view, and discrete indicator matrix learning are seamlessly incorporated into the unified framework. The joint optimal clustering outcomes are therefore possible. Experimental results on real-world datasets in the IMC task demonstrate the effectiveness of the proposed method compared with state-of-the-art baselines.

Community Discovery Algorithm Based on Multi-Relationship Embedding

Complex systems in the real world often can be modeled as network structures, and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks. Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes. However, many real-world networks consist of multiple types of nodes and edges, and there may be rich semantic information on nodes and edges. The methods for single-layer networks cannot effectively tackle multi-layer information, multi-relationship information, and attribute information. This paper proposes a community discovery algorithm based on multi-relationship embedding. The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder. The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network (GCN) to obtain the final node embedding matrix. This strategy allows capturing of rich structural and attributes information in multi-relational networks. Experiments were conducted on different datasets with baselines, and the results show that the proposed algorithm obtains significant performance improvement in community discovery, node clustering, and similarity search tasks, and compared to the baseline with the best performance, the proposed algorithm achieves an average improvement of 3.1% on Macro-F1 and 4.7% on Micro-F1, which proves the effectiveness of the proposed algorithm.

Macrocyclic luminophores under confinement in a polymeric matrix – induction of large-Stokes shift by inter-unit proton transfer

Macrocyclic tetra(naphthimidazole)resorcin[4]arenes exhibit large Stokes shifts upon embedding in PMMA matrix enabling their applications in luminescent solar concentrator technology.

MLP-JCG: Multi-Layer Perceptron With Joint-Coordinate Gating for Efficient 3D Human Pose Estimation

Various structural relations/dependencies exist among human body joints, which makes it possible to estimate 3D poses from 2D sources. The current research on 3D human pose estimation (3D-HPE for short) mainly focuses on structural information from a specific perspective. However, this information cannot facilitate 2D-to-3D pose lifting. This paper presents a novel and efficient multi-layer perceptron with a joint-coordinate gating (MLP-JCG) model, exploring and utilizing both the local and global structural information to perform 3D pose estimations. Specifically, MLP-JCG contains two independent MLP blocks, i.e., joint-mixing MLP and coordinate-mixing MLP, which solely act on the joint and coordinate axes in modelling their local structural information. For the global structural information, we first explore two kinds of global statistics from the pose matrix embeddings, which are referred to as the dynamics aggregated along the joint/coordinate axis. Then, we propose two kinds of gating units to elementwisely contextualize the features learned from MLP blocks. All the model components are designed based on MLP, making the MLP-JCG easy to implement and train. We conduct experiments on three 3D-HPE benchmarks, and the results demonstrate the superior effectiveness and efficiency of the proposed approach.

Exploiting Field Dependencies for Learning on Categorical Data.

Traditional approaches for learning on categorical data underexploit the dependencies between columns (a.k.a. fields) in a dataset because they rely on the embedding of data points driven alone by the classification/regression loss. In contrast, we propose a novel method for learning on categorical data with the goal of exploiting dependencies between fields. Instead of modelling statistics of features globally (i.e., by the covariance matrix of features), we learn a global field dependency matrix that captures dependencies between fields and then we refine the global field dependency matrix at the instance-wise level with different weights (so-called local dependency modelling) w.r.t. each field to improve the modelling of the field dependencies. Our algorithm exploits the meta-learning paradigm, i.e., the dependency matrices are refined in the inner loop of the meta-learning algorithm without the use of labels, whereas the outer loop intertwines the updates of the embedding matrix (the matrix performing projection) and global dependency matrix in a supervised fashion (with the use of labels). Our method is simple yet it outperforms several state-of-the-art methods on six popular dataset benchmarks. Detailed ablation studies provide additional insights into our method.

The main aspects of the experience economy in tourism industry

The purpose of the study is to identify the influence of experience economy factors (entertainment, education, aesthetics, escapism) and the economic component on preferences in choosing hotels in the domestic tourism industry. The empirical base of the study includes 30,926 reviews from the tripadvisor.ru website for 1,243 hotels in Russia for 2021–2022, divided by consumer ratings. The research methodology includes textual analysis of tourist reviews: counting particular words and phrases in reviews, calculating the correlation of words, finding word relationships using the word2vec embedding matrix, highlighting the most popular topics of discussion based on the Latent Dirichlet allocation (LDA) method and assessing sentiment using a BERT transformer network. The results of this study assess aspects of the basic model of the experience economy (aesthetics, escapism, entertainment and education) and adapt the model for the domestic tourism and hospitality industry, adding relevant aspects (recommendations and economics). Based on the results of a quantitative assessment of aspects of the extended model of the experience economy, it was revealed that the aesthetic aspect is decisive for people when evaluating hotels, and the aspects of entertainment and education, expressed indirectly, although they are of high importance, but not as much as aesthetic parameters. The economic component, primarily the price-quality ratio and the overall assessment of the cost, also stands out in the main topics, however, hotel reviews focus on it to a lesser extent. This is the first time in Russia that such a study is evaluating aspects of the experience economy model for the tourism industry and will allow developing the travel market in Russia.

Generating and Imaging Mouse and Human Epithelial Organoids from Normal and Tumor Mammary Tissue Without Passaging.

Organoids are a reliable method for modeling organ tissue due to their self-organizing properties and retention of function and architecture after propagation from primary tissue or stem cells. This method of organoid generation forgoes single-cell differentiation through multiple passages and instead uses differential centrifugation to isolate mammary epithelial organoids from mechanically and enzymatically dissociated tissues. This protocol provides a streamlined technique for rapidly producing small and large epithelial organoids from both mouse and human mammary tissue in addition to techniques for organoid embedding in collagen and basement extracellular matrix. Furthermore, instructions for in-gel fixation and immunofluorescent staining are provided for the purpose of visualizing organoid morphology and density. These methodologies are suitable for myriad downstream analyses, such as co-culturing with immune cells and ex vivo metastasis modeling via collagen invasion assay. These analyses serve to better elucidate cell-cell behavior and create a more complete understanding of interactions within the tumor microenvironment.

Generating and Imaging Mouse and Human Epithelial Organoids from Normal and Tumor Mammary Tissue Without Passaging

Organoids are a reliable method for modeling organ tissue due to their self-organizing properties and retention of function and architecture after propagation from primary tissue or stem cells. This method of organoid generation forgoes single-cell differentiation through multiple passages and instead uses differential centrifugation to isolate mammary epithelial organoids from mechanically and enzymatically dissociated tissues. This protocol provides a streamlined technique for rapidly producing small and large epithelial organoids from both mouse and human mammary tissue in addition to techniques for organoid embedding in collagen and basement extracellular matrix. Furthermore, instructions for in-gel fixation and immunofluorescent staining are provided for the purpose of visualizing organoid morphology and density. These methodologies are suitable for myriad downstream analyses, such as co-culturing with immune cells and ex vivo metastasis modeling via collagen invasion assay. These analyses serve to better elucidate cell-cell behavior and create a more complete understanding of interactions within the tumor microenvironment.

Efficient Multiconfigurational Quantum Chemistry Approach to Single-Ion Magnets Based on Density Matrix Embedding Theory.

Density matrix embedding theory (DMET) provides a systematic framework to combine low-level (e.g., Hartree-Fock approximation) and high-level correlated quantum chemistry methods to treat strongly correlated systems. In this work, we propose an efficient quantum embedding approach that combines DMET with the complete active space self-consistent field and subsequent state interaction treatment of spin-orbit coupling (CASSI-SO) and apply it to a theoretical description of single-ion magnets (SIMs). We have developed a novel regularized direct inversion of iterative subspace (R-DIIS) technique that ensures restricted open-shell Hartree-Fock converging to a physically correct ground state, which is found to be crucial for the efficacy of subsequent CASSI-SO calculation. The DMET+CASSI-SO approach can produce reliable zero-field splitting parameters in typical 3d-SIMs with dramatically reduced computational cost compared to its all-electron counterpart. This work therefore demonstrates the great potential of DMET-based multiconfigurational approaches for efficient ab initio study of magneto-structural correlations in complex molecular magnetic systems.

Lossless embedding: A visually meaningful image encryption algorithm based on hyperchaos and compressive sensing

A novel visually meaningful image encryption algorithm is proposed based on a hyperchaotic system and compressive sensing (CS), which aims to improve the visual security of steganographic image and decrypted quality. First, a dynamic spiral block scrambling is designed to encrypt the sparse matrix generated by performing discrete wavelet transform (DWT) on the plain image. Then, the encrypted image is compressed and quantified to obtain the noise-like cipher image. Then the cipher image is embedded into the alpha channel of the carrier image in portable network graphics (PNG) format to generate the visually meaningful steganographic image. In our scheme, the hyperchaotic Lorenz system controlled by the hash value of plain image is utilized to construct the scrambling matrix, the measurement matrix and the embedding matrix to achieve higher security. In addition, compared with other existing encryption algorithms, the proposed PNG-based embedding method can blindly extract the cipher image, thus effectively reducing the transmission cost and storage space. Finally, the experimental results indicate that the proposed encryption algorithm has very high visual security.

Analytical study of the bond behavior of fiber reinforced cementitious matrix (FRCM)-substrate joints based on a two-stage nonlinear cohesive material law

Externally bonding fiber-reinforced cementitious matrix (FRCM) composite to the surface of concrete or masonry members is an effective technique to improve the performance of existing structures. In many cases, interfacial debonding between the FRCM fiber textile and embedding matrix governs the capacity of FRCM-strengthened structures. The interfacial debonding process can be studied analytically by assuming a cohesive material law (CML), which represents the relationship between interfacial shear stress and slip. In this study, a two-stage function with an exponential stage and a constant stage is proposed to describe the CML associated with the matrix-fiber interface. The latter stage is characterized by a constant shear stress to account for the friction/interlocking between the matrix and fiber observed in experimental tests. With the assumed CML, the full-range loading response was obtained. Additionally, the interfacial slip, fiber axial strain, and interfacial shear stress were analytically derived. The parameters of the two-stage CMLs for PBO, glass, and carbon FRCM were inversely determined by matching the analytical relationships of peak applied axial stresses associated with different bonded lengths with the experimental ones. Considering the inversely determined CMLs, the predicted load responses and strain profiles showed good agreement with the measurements of direct shear test specimens.

Controlled gel expansion through colloid oscillation.

We model the behavior of a single colloid embedded in a cross-linked polymer gel, immersed in a viscous background fluid. External fields actuate the particle into a periodic motion, which deforms the embedding matrix and creates a local microcavity, containing the particle and any free volume created by its motion. This cavity exists only as long as the particle is actuated and, when present, reduces the local density of the material, leading to swelling. We show that the model exhibits rich resonance features, but is overall characterized by clear scaling laws at low and high driving frequencies, and a pronounced resonance at intermediate frequencies. Our model predictions suggest that both the magnitude and position of the resonance can be varied by varying the material's elastic modulus or cross-linking density, whereas the local viscosity primarily has a dampening effect. Our work implies appreciable free-volume generation is possible by dispersing a collection of colloids in the medium, even at the level of a simple superposition approximation.

Se-xDeepFEFM: Combining Low-Order Feature Refinement and Interaction Intensity Evaluation for Click-Through Rate Prediction

Click-through rate (CTR) prediction can provide considerable economic and social benefits. Few studies have considered the importance of low-order features, usually employing a simple feature interaction method. To address these issues, we propose a novel model called Senet and extreme deep field-embedded factorization machine (Se-xDFEFM) for more effective CTR prediction. We first embed the squeeze-excitation network (Senet) module into Se-xDFEFM to complete low-order feature refinement, which can better filter noisy information. Then, we implement our field-embedded factorization machine (FEFM) to learn the symmetric matrix embeddings for each field pair, along with the single-vector embeddings for each feature, which builds a firm foundation for the subsequent feature interaction. Finally, we design a compressed interaction network (CIN) to realize feature construction with definite order through a vector-wise interaction. We use a deep neural network (DNN) with the CIN to simultaneously implement effective but complementary explicit and implicit feature interactions. Experimental results demonstrate that the Se-xDFEFM model outperforms other state-of-the-art baselines. Our model is effective and robust for CTR prediction. Importantly, our model variants also achieve competitive recommendation performance, demonstrating their scalability.

PH-responsive chitosan-based film containing oregano essential oil and black rice bran anthocyanin for preserving pork and monitoring freshness

This study developed an intelligent and antibacterial packaging film using a chitosan matrix embedding oregano essential oil (OEO) and black rice bran anthocyanin (BRBA). Herein, OEO and BRBA were immobilized into the chitosan matrix through noncovalent bonds and uniformly distributed in the films. The chitosan–OEO–BRBAⅡ film exhibited excellent mechanical, antibacterial, antioxidant, and UV–vis light barrier properties, and sensitive and rapid response to pH/NH3. Furthermore, fresh pork was coated with the film for monitoring the freshness and preservation efficiency at 4 °C for 12 days. The film effectively improved the quality indices of pork, including the sensory index, total viable counts, pH, TVB-N value, and color of pork during the storage at 4 °C. The film reduced the abundance of spoilage bacteria related to stress tolerance, pathogenicity, and biofilm formation in the pork. Their odorous volatiles appeared later and were less than those in the untreated group.