Search Results (89)

Lysosomal storage disorders (LSDs) are inherited metabolic conditions characterized by lysosomal enzyme deficiencies leading to substrate accumulation. As genetic diseases, LSDs can be treated with gene therapies (GT), including the CRISPR/Cas systems. The CRISPR/Cas systems enable precise and programmable genome editing, leading to targeted modifications at specific genomic loci. While the classical CRISPR/Cas9 system has been extensively used to generate LSD disease models and correct disease-associated genetic alterations through homologous recombination (HR), recently described Cas proteins as well as CRISPR/Cas9-derived strategies such as base editing, prime editing, and homology-independent targeted integration (HITI) offer a novel way to develop innovative treatments for LSDs. The direct administration of the CRISPR/Cas9 system remains the primary strategy evaluated in several LSDs; nevertheless, the ex vivo CRISPR/Cas9-based approach has been recently explored, primarily in central nervous system-affecting LSDs. Ex vivo approaches involve genetically modifying, in theory, any patient cells in the laboratory and reintroducing them into the patient to provide a therapeutic effect. This manuscript reviews the molecular aspects of the CRISPR/Cas technology and its implementation in ex vivo strategies for LSDs while discussing novel approaches beyond the classical CRISPR/Cas9 system. Full article

Let Y be a smooth compact n-manifold. We studied smooth embeddings and immersions $\beta :M\to \mathbb{R}\times Y$ of compact n-manifolds M such that $\beta \left(M\right)$ avoids some priory chosen closed poset $\mathrm{\Theta }$ of tangent patterns to the fibers of the obvious projection $\pi :\mathbb{R}\times Y\to Y$. Then, for a fixed Y, we introduced an equivalence relation between such $\beta$’s; creating a crossover between pseudo-isotopies and bordisms. We called this relation quasitopy. In the presented study of quasitopies, the spaces ${\mathcal{P}}_d^{\mathbf{c}\mathrm{\Theta }}$ of real univariate polynomials of degree d with real divisors, whose combinatorial patterns avoid a given closed poset $\mathrm{\Theta }$, play the classical role of Grassmanians. We computed the quasitopy classes ${\mathcal{Q}}_d^{\mathsf{emb}}(Y,\mathbf{c}\mathrm{\Theta })$ of $\mathrm{\Theta }$-constrained embeddings $\beta$ in terms of homotopy/homology theory of spaces Y and ${\mathcal{P}}_d^{\mathbf{c}\mathrm{\Theta }}$. We proved also that the quasitopies of embeddings stabilize, as $d\to \infty$. Full article

Allostery is a fundamental biological phenomenon that occurs when a molecule binds to a protein’s allosteric site, triggering conformational changes that regulate the protein’s activity. However, allostery in antibodies remains largely unexplored, and only a few reports have focused on allostery from antigen-binding fragments (Fab) to crystallizable fragments (Fc). But this study, using anti-phenobarbital antibodies—which are widely applied for detecting the potential health food adulterant phenobarbital—as a model and employing multiple computational methods, is the first to identify a cyclopeptide (cyclo[Link-M-WFRHY-K]) that induces allostery from Fc to Fab in antibody and elucidates the underlying antibody allostery mechanism. The combination of molecular docking and multiple allosteric site prediction algorithms in these methods identified that the cyclopeptide binds to the interface of heavy chain region-1 (CH₁) in antibody Fab and heavy chain region-2 (CH₂) in antibody Fc. Meanwhile, molecular dynamics simulations combined with other analytical methods demonstrated that cyclopeptide induces global conformational shifts in the antibody, which ultimately alter the Fab domain and enhance its antigen-binding activity from Fc to Fab. This result will enable cyclopeptides as a potential Fab-targeted allosteric modulator to provide a new strategy for the regulation of antigen-binding activity and contribute to the construction of novel immunoassays for food safety and other applications using allosteric antibodies as the core technology. Furthermore, graph theory analysis further revealed a common allosteric signaling pathway within the antibody, involving residues Q123, S207, S326, C455, A558, Q778, D838, R975, R1102, P1146, V1200, and K1286, which will be very important for the engineering design of the anti-phenobarbital antibodies and other highly homologous antibodies. Finally, the non-covalent interaction analysis showed that allostery from Fc to Fab primarily involves residue signal transduction driven by hydrogen bonds and hydrophobic interactions. Full article

In this survey, we provide an overview of category theory-derived machine learning from four mainstream perspectives: gradient-based learning, probability-based learning, invariance and equivalence-based learning, and topos-based learning. For the first three topics, we primarily review research in the past five years, updating and expanding on the previous survey by Shiebler et al. The fourth topic, which delves into higher category theory, particularly topos theory, is surveyed for the first time in this paper. In certain machine learning methods, the compositionality of functors plays a vital role, prompting the development of specific categorical frameworks. However, when considering how the global properties of a network reflect in local structures and how geometric properties and semantics are expressed with logic, the topos structure becomes particularly significant and profound. Full article

Sinceits introduction in the 19th century to address geometric problems, topology as a methodology has undergone a series of evolutions, encompassing branches of geometric topology, point-set topology (analytic topology), algebraic topology, and differential topology, gradually permeating into various interdisciplinary applied fields. Starting from disciplines with typical geometric characteristics such as geography, physics, biology, and computer science, topology has found its way to economic fields in the 20th century. Given that the introduction of topology to economics is relatively new and presents features of being fragmented and non-systematic, this review aimed to provide scholars with a systematic evolution map to refine the characteristics of topology as a methodology applied in economics and finance, thereby aiding future potential interdisciplinary developments in these fields. By collecting abundant literature indexed in SCOPUS/WoS and other famous databases, with a qualitative analysis to classify and summarize it, we found that topological methods were introduced to modern economics when dealing with dynamic optimization, functional analysis, and convex programming problems, including famous applications such as uncovering equilibrium with fixed-point theorems in Walrasian economics. Topology can help uncover and refine the topological properties of these function space transformations, thus finding unchangeable features. Meanwhile, in contemporary economics, topology is being used for high-dimension reduction, complex network construction, and structural data mining, combined with techniques of machine learning, and applied to high-dimensional time series and structure analysis in financial markets. The most famous practical applications include the use of topological data analysis (TDA) and topological machine learning (TML) for different applied problems. Full article

It has been approximately 2000 years since the medicinal homologous theory, which primarily holds that food has the same therapeutic value as medicine in order to improve the health of both humans and animals. In recent years, this theory has also been proposed to be used in poultry breeding. Ellagic acid (EA), a natural compound primarily extracted from medicinal homologous foods such as raspberries and pomegranates, is reported to have incomparable advantages in improving the production performance and disease resistance of poultry due to its pharmacological properties, which regulate the processes of redox homeostasis, inflammatory response, and cell apoptotic death. However, the application and research of EA in poultry production are still in the initial stage, and the potential mechanisms of its biological functions affecting animal health have not been clearly identified, which requires more attention worldwide. This mini-review collects the latest 10-year achievements of research on the effects of EA on poultry health, aiming to promote the practical application of EA in maintaining animal health and formulating corresponding targeted strategies. Full article

Medicinal food varieties developed according to the theory of medical and edible homologues are effective at preventing and treating chronic diseases and in health care. As of 2022, 110 types of traditional Chinese medicines from the same source of medicine and food have been published by the National Health Commission. Inflammation is the immune system’s first response to injury, infection, and stress. Chronic inflammation is closely related to many diseases such as atherosclerosis and cancer. Therefore, timely intervention for inflammation is the mainstay treatment for other complex diseases. However, some traditional anti-inflammatory drugs on the market are commonly associated with a number of adverse effects, which seriously affect the health and safety of patients. Therefore, the in-depth development of new safe, harmless, and effective anti-inflammatory drugs has become a hot topic of research and an urgent clinical need. Polysaccharides, one of the main active ingredients of medical and edible homologous traditional Chinese medicines (MEHTCMs), have been confirmed by a large number of studies to exert anti-inflammatory effects through multiple targets and are considered potential natural anti-inflammatory drugs. In addition, the structure of medical and edible homologous traditional Chinese medicines’ polysaccharides (MEHTCMPs) may be the key factor determining their anti-inflammatory activity, which makes the underlying the anti-inflammatory effects of polysaccharides and their structure–efficacy relationship hot topics of domestic and international research. However, due to the limitations of the current analytical techniques and tools, the structures have not been fully elucidated and the structure–efficacy relationship is relatively ambiguous, which are some of the difficulties in the process of developing and utilizing MEHTCMPs as novel anti-inflammatory drugs in the future. For this reason, this paper summarizes the potential anti-inflammatory mechanisms of MEHTCMPs, such as the regulation of the Toll-like receptor-related signaling pathway, MAPK signaling pathway, JAK-STAT signaling pathway, NLRP3 signaling pathway, PI3K-AKT signaling pathway, PPAR-γ signaling pathway, Nrf2-HO-1 signaling pathway, and the regulation of intestinal flora, and it systematically analyzes and evaluates the relationships between the anti-inflammatory activity of MEHTCMPs and their structures. Full article

Methods used in topological data analysis naturally capture higher-order interactions in point cloud data embedded in a metric space. This methodology was recently extended to data living in an information space, by which we mean a space measured with an information theoretical distance. One such setting is a finite collection of discrete probability distributions embedded in the probability simplex measured with the relative entropy (Kullback–Leibler divergence). More generally, one can work with a Bregman divergence parameterized by a different notion of entropy. While theoretical algorithms exist for this setup, there is a paucity of implementations for exploring and comparing geometric-topological properties of various information spaces. The interest of this work is therefore twofold. First, we propose the first robust algorithms and software for geometric and topological data analysis in information space. Perhaps surprisingly, despite working with Bregman divergences, our design reuses robust libraries for the Euclidean case. Second, using the new software, we take the first steps towards understanding the geometric-topological structure of these spaces. In particular, we compare them with the more familiar spaces equipped with the Euclidean and Fisher metrics. Full article

Thermoelectric materials have drawn much attention over the last two decades due to the increase in global energy demand. However, designing efficient thermoelectrics reveals itself as a tough task for their properties (Seebeck coefficient, electrical conductivity, thermal conductivity) are mutually opposed. Hence, most recently, new design approaches have appeared, among which high-throughput methods have been implemented either experimentally or computationally. In this work, a high-throughput computer program has been designed to generate over 4000 structures based on a small set of complex layered chalcogenide compounds taken from the mA^IVB^VI nA₂^VB₃^VI homologous series, where A^IV is Ge, A^V is Sb and B^VI is Te. The computer-generated structures have been investigated using density-functional theory methods, and the electronic and transport properties have been calculated. It has been found, using the quantum theory of atoms in molecules and crystals, that a wide variety of bond types constitutes the bonding network of the structures. All the structures are found to have negative formation energies. Among the obtained final structures, 43 are found with a wide band gap energy (>0.25 eV), 358 with semi-conductor/metal characteristics, and 731 with metallic characteristics. The transport properties calculations, using the Boltzmann equation, reveal that two p-type and 86 n-type structures are potentially promising compounds for thermoelectric applications. Full article

Recently, Gorenstein dimensions relative to a semidualizing module have been the subject of numerous studies with interesting extensions of the classical homological dimensions. Although all these studies share the same direction, a common basis, and similar final goals, there is no common framework encompassing them as parts of a whole, progressing, on different fronts, towards the same end. We provide this general and global framework in the context of abelian categories, standardizing terminology and notation: we establish a general context by defining Gorenstein categories relative to two classes of objects ($(\mathcal{X},\mathcal{Y})$-Gorenstein categories, denoted $\mathcal{G}(\mathcal{X},\mathcal{Y})$), and carry out a study of the homological dimensions associated with them. We prove, under some mild standard conditions, the corresponding version of the Comparison Lemma that ensures the consistency of a homological-dimension theory. We show that Ext functors can be used as tools to compute these $\mathcal{G}(\mathcal{X},\mathcal{Y})$-dimensions, and we compare the dimensions obtained using the classes $G\left(\mathcal{X}\right)$ with those computed using $\mathcal{G}(\mathcal{X},\mathcal{Y})$. We also initiate a research of the global dimensions obtained with these classes $\mathcal{G}(\mathcal{X},\mathcal{Y})$ and find conditions for them to be finite. Finally, we show that these classes of Gorenstein objects are closely and interestingly related to the Foxby classes induced by a pair of functors. Namely, we prove that the Auslander and Bass classes are indeed $\mathcal{G}(\mathcal{X},\mathcal{Y})$ categories for some specific classes $\mathcal{X}$ and $\mathcal{Y}$. Full article

We demonstrate the equivalence of two different conjectures in the literature for the holographic entanglement negativity in AdS₃/CFT₂, modulo certain constants. These proposals involve certain algebraic sums of bulk geodesics homologous to specific combinations of subsystems, and the entanglement wedge cross section (EWCS) backreacted by a cosmic brane for the conical defect geometry in the bulk gravitational path integral. It is observed that the former conjectures reproduce the field theory replica technique results in the large central charge limit whereas the latter involves constants related to the Markov gap. In this context, we establish an alternative construction for the EWCS of a single interval in a CFT₂ at a finite temperature to resolve an issue for the latter proposal involving thermal entropy elimination for holographic entanglement negativity. Our construction for the EWCS correctly reproduces the corresponding field theory results modulo the Markov gap constant in the large central charge limit. Full article

Virtual colonoscopy plays an important role in polyp detection of colorectal cancer. Noise in the colon data acquisition process can result in topological errors during surface reconstruction. Topological denoising can be employed to remove these errors on surfaces for subsequent geometry processing, such as surface simplification and parameterization. Many methods have been proposed for this task. However, many existing methods suffer from failure in computation of all the non-trivial loops, due to high genus or complex topological structures. In this paper, we propose a novel robust topological denoising method for surfaces based on homotopy theory. The proposed method was evaluated on two datasets of colon meshes. We compared our method with the State-of-the-Art persistent-homology-based method. Our method can successfully compute the loops on all colon data for topological denoising, whereas the persistent homology method fails on some colon data. Moreover, our method detects all loops with shorter lengths than those detected by the persistent homology method. Our experimental results show that the proposed method is effective and robust in topological denoising, and that it has the potential for practical application to virtual colonoscopy. Full article

The RAS gene family is one of the most frequently mutated oncogenes in human cancers. In KRAS, mutations of G12D and G12C are common. Here, 52 iridoids were selected and docked against 8AFB (KRAS G12C receptor) using Sotorasib as the standard. As per the docking interaction data, 6-O-trans-p-coumaroyl-8-O-acetylshanzhiside methyl ester (dock score: −9.9 kcal/mol), 6′-O-trans-para-coumaroyl geniposidic acid (dock score: −9.6 kcal/mol), 6-O-trans-cinnamoyl-secologanoside (dock score: −9.5 kcal/mol), Loganic acid 6′-O-beta-d-glucoside (dock score: −9.5 kcal/mol), 10-O-succinoylgeniposide (dock score: −9.4), Loganic acid (dock score: −9.4 kcal/mol), and Amphicoside (dock score: −9.2 kcal/mol) showed higher dock scores than standard Sotorasib (dock score: −9.1 kcal/mol). These common amino acid residues between iridoids and complexed ligands confirmed that all the iridoids perfectly docked within the receptor’s active site. The 100 ns MD simulation data showed that RMSD, RMSF, radius of gyration, and SASA values were within range, with greater numbers of hydrogen bond donors and acceptors. MM/PBSA analysis showed maximum binding energy values of −7309 kJ/mol for 6-O-trans-p-coumaroyl-8-O-acetylshanzhiside methyl ester. FMO analysis showed that 6-O-trans-p-coumaroyl-8-O-acetylshanzhiside methyl ester was the most likely chemically reactive molecule. MEP analysis data highlighted the possible electrophilic and nucleophilic attack regions of the best-docked iridoids. Of all the best-docked iridoids, Loganic acid passed Lipinski, Pfizer, and GSK filters with a similar toxicity profile to Sotorasib. Thus, if we consider these iridoids to be KRAS G12C inhibitors, they will be a boon to mankind. Full article

The mesomorphic stability and optical activity of new group-based benzotrifluoride liquid crystals, (E)-4-(((4-(trifluoromethyl) phenyl) imino) methyl) phenyl 4-(alkyloxy)benzoate, or In, were investigated. The end of the molecules connected to the benzotrifluoride moiety and the end of the phenylazo benzoate moiety have terminal alkoxy groups which can range in chain length from 6 to 12 carbons. The synthesized compounds’ molecular structures were verified using FT-IR, ¹H NMR, mass spectroscopy, and elemental analysis. Mesomorphic characteristics were verified using differential scanning calorimetry (DSC) and a polarized optical microscope (POM). All of the homologous series that have been developed display great thermal stability across a broad temperature range. Density functional theory (DFT) determined the examined compounds’ geometrical and thermal properties. The findings showed that every compound is entirely planar. Additionally, by using the DFT approach, it was possible to link the experimentally found values of the investigated compounds’ investigated compounds’ mesophase thermal stability, mesophase temperature ranges, and mesophase type to the predicted quantum chemical parameters. Full article

The application of solid mechanics theory for material behavior faces the discrete nature of modern or biological material. Despite the developed methods of homogenization, there are deviations between simulated and experiments results. The reason is homogenization, which mathematically involves a type of interpolation. The situation gets worse for complex structured materials. On the other hand, a topological approach can help in such analysis, but such an approach has computational costs. At the same time, increasing modern computational capabilities remove this barrier. This study is focused on building a method to analyze material structure in a topological sense. The orientation distribution function was used to describe the structure of the material. The plane case was investigated. Quadratic and biquadratic forms of interpolant were investigated. The persistent homology approach was used for topology analysis. For this purpose, a persistence diagram for quadratic and biquadratic forms was found and analyzed. In this study, it is shown how scaling the origin point cloud influences H₁ points in the persistence diagram. It was assumed that the topology of the biquadratic form can be understood as a superposition of quadratic forms. Quantitative estimates are given for ellipticity and H₁ points. A dataset of micro photos was processed using the proposed method. Furthermore, the supply criteria for the interpolation choice in quadratic or biquadratic forms was formulated. Full article