Human protein-protein interaction networks: A topological comparison review.

Protein-Protein Interaction Networks aim to model the interactome, providing a powerful tool for understanding the complex relationships governing cellular processes. These networks have numerous applications, including functional enrichment, discovering cancer driver genes, identifying drug targets, and more. Various databases make protein-protein networks available for many species, including Homo sapiens. This work topologically compares four Homo sapiens networks using a coarse-to-fine approach, comparing global characteristics, sub-network topology, specific nodes centrality, and interaction significance. Results show that the four human protein networks share many common protein-encoding genes and some global measures, but significantly differ in the interactions and neighbourhood. Small sub-networks from cancer pathways performed better than the whole networks, indicating an improved topological consistency in functional pathways. The centrality analysis shows that the same genes play different roles in different networks. We discuss how studies and analyses that rely on protein-protein networks for humans should consider their similarities and distinctions.

A quest for cytosolic sequons and their functions.

Evolution shapes protein sequences for their functions. Here, we studied the moonlighting functions of the N-linked sequon NXS/T, where X is not P, in human nucleocytosolic proteins. By comparing membrane and secreted proteins in which sequons are well known for N-glycosylation, we discovered that cyto-sequons can participate in nucleic acid binding, particularly in zinc finger proteins. Our global studies further discovered that sequon occurrence is largely proportional to protein length. The contribution of sequons to protein functions, including both N-glycosylation and nucleic acid binding, can be regulated through their density as well as the biased usage between NXS and NXT. In proteins where other PTMs or structural features are rich, such as phosphorylation, transmembrane ɑ-helices, and disulfide bridges, sequon occurrence is scarce. The information acquired here should help understand the relationship between protein sequence and function and assist future protein design and engineering.

Water masses influence the variation of microbial communities in the Yangtze River Estuary and its adjacent waters.

The Yangtze River estuary (YRE) are strongly influenced by the Kuroshio and terrigenous input from rivers, leading to the formation of distinct water masses, however, there remains a limited understanding of the full extent of this influence. Here the variation of water masses and bacterial communities of 58 seawater samples from the YRE and its adjacent waters were investigated. Our findings suggested that there were 5 water masses in the studied area: Black stream (BS), coastal water in the East China Sea (CW), nearshore mixed water (NM), mixed water in the middle and deep layers of the East China Sea (MM), and deep water blocks in the middle of the East China Sea (DM). The CW mass harbors the highest alpha diversity across all layers, whereas the NM mass exhibits higher diversity in the surface layer but lower in the middle layers. Proteobacteria was the most abundant taxa in all water masses, apart from that, in the surface layer masses, Cyanobacterium, Bacteroidota, and Actinobacteriota were the highest proportion in CW, while Bacteroidota and Actinobacteriota were the highest proportion in NM and BS; in the middle layer, Bacteroidota and Actinobacteriota were dominant phylum in CW and BS masses, but Cyanobacterium was main phylum in NM mass; in the bottom layer, Bacteroidota and Actinobacteriota were the dominant phylum in CW, while Marininimicrobia was the dominated phylum in DM and MM masses. Network analysis suggests water masses have obvious influence on community topological characteristics, moreover, community assembly across masses also differ greatly. Taken together, these results emphasized the significant impact of water masses on the bacterial composition, topological characteristics and assembly process, which may provide a theoretical foundation for predicting alterations in microbial communities within estuarine ecosystems under the influence of water masses.

Synthesis, topology, molecular docking and dynamics studies of o-phenylenediamine derivative.

The N, N'-(1,2-phenylene) bis (1- (4- chlorophenyl) methanimine) (CS4) was synthesized and characterized by infrared (IR), absorption (UV-vis) and NMR (1H and 13C) spectral analyses. The structural parameters, vibrational frequencies, potential energy and the distribution analysis (PED) were calculated by using DFT with the basis set of B3LYP/cc-pVDZ and these spectral values were compared to the experimental values. HOMO and LUMO studied were performed in order to understand the stability and biological activity of the compound. The most reactive sites on the compound were investigated by utilizing MEP energy surface and Fukui function descriptor with the natural population analysis (NPA) of the charges. The study of the natural bond orbitals (NBO) reveals the delocalization of the intramolecular interaction of the charges in the compound. Additionally, topological investigations (ELF, LOL), determination of thermodynamic parameters and noncovalent interaction (NCI) study by using topology (RDG) analysis were also carried out. Finally, the molecular docking and molecular dynamics simulations was carried out by examining against glycosylphosphatidylinositol phospholipase D inhibitor receptor for distinct protein targets (3MZG).Communicated by Ramaswamy H. Sarma.

Topology Regulated Background Extraction (TRBE) method for eye fundus images.

One of the initial steps in the preprocessing of digital fundoscopy images is the identification of pixels containing relevant information. This can be achieved through different approaches, one of them is implementing background extraction, reducing the set of pixels to be analyzed later in the process. In this work, we present a background extraction method for digital fundoscopy images based on computational topology. By interpreting binarized images as cubical complexes and extracting their homological groups in 1 and 2 dimensions we identify a subset of luminescence values that can be used to binarize the original grayscale image, obtaining a mask to achieve background extraction. This method is robust to noise and suboptimal image quality, facilitating the analytical pipeline in the context of computer aided diagnosis approaches. This method facilitates the segmentation of the background of a digital fundoscopy image, which allows further methods to focus on pixels with relevant information (eye fundus). This tool is best suited to be implemented in the preprocessing stages of the analytical pipeline by computational ophthalmology specialists.•It is robust to noise and low-quality images.•Output provides an ideal scenario for down-the-line analysis by facilitating only relevant pixels in a digital fundoscopy.

Ab Initio Density Functional Theory Calculation: Americium Hydrolysis Mechanism.

The hydrolysis mechanism of americium was calculated using density functional theory, and the detailed microscopic reaction mechanism was obtained. The results show that americium reacts with water along the octet state to produce oxides and H2, and that this reaction is exothermic. The interaction between Am and O atoms gradually changes from initially electrostatic interaction to covalent interaction, and continues to strengthen. During the reaction process, Am atoms always lose electrons, the 5f orbital is obviously involved, and there is df orbital hybridization. This study provides the necessary theoretical data support for the theoretical and experimental study of the actinide system.

Topology Optimization Method of Stamping Structures Based on the Directional Density Field.

The stamping process produces thin-walled structures that, in general, have uniform wall thickness and no enclosed cavity. However, it is difficult to satisfy the above geometric requirements with the current density-based topology optimization method, since configuring the related geometric constraints is challenging. In order to solve this problem, a topology optimization method for stamping structures based on a directional density field is proposed. Specifically, the directional density field is developed to enable the adding and removing of materials only along the stamping direction, so as to avoid internal voids and concave features. The geometric control for uniform wall thickness is realized by tuning the truncation threshold of the Heaviside projection that processes the directional density field into the 0-1 binary field. At the same time, a calibrated filter radius of the truncation thresholds will facilitate the drawing angle control of the stamping ribs. The effectiveness of the established method has been verified by a number of numerical case studies. Results show that the proposed method can perform topology optimization for stamping structures with tunable uniform thickness and drawing angle control of the ribs. No internal voids or undercuts appear in the results. The results also disclose that a constant truncation threshold increment does not guarantee uniform wall thickness, and varying the threshold increments through surface offset and polynomial fitting is necessary.

A Three-branch Jointed Feature and Topology Decoder guided by game-theoretic interactions for temporomandibular joint segmentation.

Segmentation of the temporomandibular joint (TMJ) disc and condyle from magnetic resonance imaging (MRI) is a crucial task in TMJ internal derangement research. The automatic segmentation of the disc structure presents challenges due to its intricate and variable shapes, low contrast, and unclear boundaries. Existing TMJ segmentation methods often overlook spatial and channel information in features and neglect overall topological considerations, with few studies exploring the interaction between segmentation and topology preservation. To address these challenges, we propose a Three-Branch Jointed Feature and Topology Decoder (TFTD) for the segmentation of TMJ disc and condyle in MRI. This structure effectively preserves the topological information of the disc structure and enhances features. We introduce a cross-dimensional spatial and channel attention mechanism (SCIA) to enhance features. This mechanism captures spatial, channel, and cross-dimensional information of the decoded features, leading to improved segmentation performance. Moreover, we explore the interaction between topology preservation and segmentation from the perspective of game theory. Based on this interaction, we design the Joint Loss Function (JLF) to fully leverage the features of segmentation, topology preservation, and joint interaction branches. Results on the TMJ MRI dataset demonstrate the superior performance of our TFTD compared to existing methods.

Physical-Chemical Coupling Coassembly Approach to Branched Magnetic Mesoporous Nanochains with Adjustable Surface Roughness.

Self-assembly processes triggered by physical or chemical driving forces have been applied to fabricate hierarchical materials with subtle nanostructures. However, various physicochemical processes often interfere with each other, and their precise control has remained a great challenge. Here, in this paper, a rational synthesis of 1D magnetite-chain and mesoporous-silica-nanorod (Fe3O4&mSiO2) branched magnetic nanochains via a physical-chemical coupling coassembly approach is reported. Magnetic-field-induced assembly of magnetite Fe3O4 nanoparticles and isotropic/anisotropic assembly of mesoporous silica are coupled to obtain the delicate 1D branched magnetic mesoporous nanochains. The nanochains with a length of 2-3 µm in length are composed of aligned Fe3O4@mSiO2 nanospheres with a diameter of 150 nm and sticked-out 300 nm long mSiO2 branches. By properly coordinating the multiple assembly processes, the density and length of mSiO2 branches can well be adjusted. Because of the unique rough surface and length in correspondence to bacteria, the designed 1D Fe3O4&mSiO2 branched magnetic nanochains show strong bacterial adhesion and pressuring ability, performing bacterial inhibition over 60% at a low concentration (15 µg mL-1). This cooperative coassembly strategy deepens the understanding of the micro-nanoscale assembly process and lays a foundation for the preparation of the assembly with adjustable surface structures and the subsequent construction of complex multilevel structures.

The functional assay identified authentic interactions between CAPA peptides and the CAPA receptor isoforms in Bemisia tabaci (Gennadius).

CAPA neuropeptides regulate the diuresis/ antidiuresis process in insects by activating specific cognate receptor, CAPAr. In this study, we characterized the CAPAr gene (BtabCAPAr) in the whitefly, Bemisia tabaci Asia II 1. The two alternatively spliced isoforms of BtabCAPAr gene, BtabCAPAr-1 and BtabCAPAr-2, having six and five exons, respectively, were identified. The BtabCAPAr gene expression was highest in adult whitefly as compared to gene expression in egg, nymphal and pupal stages. Among the three putative CAPA peptides, CAPA-PVK1 and CAPA-PVK2 strongly activated the BtabCAPAr-1 with very low EC50 values of 0.067 nM and 0.053 nM, respectively, in heterologous calcium mobilization assays. None of the peptide activated the alternatively spliced isoform BtabCAPAr-2 that has lost the transmembrane segments 3 and 4. Significant levels of mortality were observed when whiteflies were fed with CAPA-PVK1 at 1.0 µM (50.0%), CAPA-PVK2 at 100.0 nM (43.8%) and CAPA-tryptoPK 1.0 µM (40.0%) at the 96 h after the treatment. This study provides valuable information to design biostable peptides to develop a class of insecticides.

Topology Optimization Design Method for Acoustic Imaging Array of Power Equipment.

Acoustic imaging technology has the advantages of non-contact and intuitive positioning. It is suitable for the rapid positioning of defects such as the mechanical loosening, discharge, and DC bias of power equipment. However, the existing research lacks the optimization design of microphone array topology. The acoustic frequency domain characteristics of typical power equipment are elaborately sorted out. After that, the cut-off frequencies of acoustic imaging instruments are determined, to meet the needs of the full bandwidth test requirements. Through a simulation calculation, the circular array is demonstrated to be the optimal shape. And the design parameters affect the imaging performance of the array to varying degrees, indicating that it is difficult to obtain the optimal array topology by an exhaustive method. Aimed at the complex working conditions of power equipment, a topology optimization design method of an acoustic imaging array for power equipment is proposed, and the global optimal solution of microphone array topology is obtained. Compared with the original array, the imaging performance of the improved LF and HF array is promoted by 54% and 49%, respectively. Combined with the simulation analysis and laboratory test, it is verified that the improved array can not only accurately locate the single sound source but also accurately identify the main sound source from the interference of the contiguous sound source.

Relative Localization and Circumnavigation of a UGV0 Based on Mixed Measurements of Multi-UAVs by Employing Intelligent Sensors.

Relative localization (RL) and circumnavigation is a highly challenging problem that is crucial for the safe flight of multi-UAVs (multiple unmanned aerial vehicles). Most methods depend on some external infrastructure for positioning. However, in some complex environments such as forests, it is difficult to set up such infrastructures. In this paper, an approach to infrastructure-free RL estimations of multi-UAVs is investigated for circumnavigating a slowly drifting UGV0 (unmanned ground vehicle 0), where UGV0 serves as the RL and circumnavigation target. Firstly, a discrete-time direct RL estimator is proposed to ascertain the coordinates of each UAV relative to the UGV0 based on intelligent sensing. Secondly, an RL fusion estimation method is proposed to obtain the final estimate of UGV0. Thirdly, an integrated estimation control scheme is also proposed for the application of the RL fusion estimation method to circumnavigation. The convergence and the performance are analyzed. The simulation results validate the effectiveness of the proposed algorithm for RL fusion estimations and of the integrated scheme.

Content-illumination coupling guided low-light image enhancement network.

Current low-light enhancement algorithms fail to suppress noise when enhancing brightness, and may introduces structural distortion and color distortion caused by halos or artifacts. This paper proposes a content-illumination coupling guided low-light image enhancement network (CICGNet), it develops a truss topology based on Retinex as backbone to decompose low-light image component in an end-to-end way. The preservation of content features and the enhancement of illumination features are carried out along with depth and width direction of the truss topology. Each submodule uses the same resolution input and output to avoid the introduction of noise. Illumination component prevents misestimation of global and local illumination by using pre- and post-activation features at different depth levels, this way could avoid possible halos and artifacts. The network progressively enhances the illumination component and maintains the content component stage-by-stage. The proposed algorithm demonstrates better performance compared with advanced attention-based low-light enhancement algorithms and state-of-the-art image restoration algorithms. We also perform extensive ablation studies and demonstrate the impact of low-light enhancement algorithm on the downstream task of computer vision. Code is available at: https://github.com/Ruini94/CICGNet .

Differences of individual gray matter networks between MCI patients who converted to AD within 3 Years and nonconverters.

Objective: Here we aimed to explore the differences in individual gray matter (GM) networks at baseline in mild cognitive impairment patients who converted to Alzheimer's disease (AD) within 3 years (MCI-C) and nonconverters (MCI-NC). Materials and methods: Data from 461 MCI patients (180 MCI-C and 281 MCI-NC) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject, a GM network was constructed using 3D-T1 imaging and the Kullback-Leibler divergence method. Gradient and topological analyses of individual GM networks were performed, and partial correlations were calculated to evaluate relationships among network properties, cognitive function, and apolipoprotein E (APOE) €4 alleles. Subsequently, a support vector machine (SVM) model was constructed to discriminate the MCI-C and MCI-NC patients at baseline. Results: The gradient analysis revealed that the principal gradient score distribution was more compressed in the MCI-C group than in the MCI-NC group, with scores for the left lingual gyrus, right fusiform gyrus and left middle temporal gyrus being increased in the MCI-C group (p < 0.05, FDR corrected). The topological analysis showed significant differences in nodal efficiency in four nodes between the two groups. Furthermore, the regional gradient scores or nodal efficiency were found to be significantly related to the neuropsychological test scores, and the left middle temporal gyrus gradient scores were positively associated with the number of APOE €4 alleles (r = 0.192, p = 0.002). Ultimately, the SVM model achieved a balanced accuracy of 79.4% in classifying MCI-C and MCI-NC patients (p < 0.001). Conclusion: The whole-brain GM network hierarchy in the MCI-C group was more compressed than that in the MCI-NC group, suggesting more serious cognitive impairments in the MCI-C group. The left middle temporal gyrus gradient scores were related to both cognitive function and APOE €4 alleles, thus serving as potential biomarkers distinguishing MCI-C from MCI-NC at baseline.

Entangled Mesh Hydrogels with Macroporous Topologies via Cryogelation for Rapid Atmospheric Water Harvesting.

Sorption-based atmospheric water harvesting (SAWH) is a promising technology to alleviate freshwater scarcity. Recently, hygroscopic salt-hydrogel composites (HSHCs) have emerged as attractive candidates with their high water uptake, versatile designability, and scale-up fabrication. However, achieving high-performance SAWH applications for HSHCs has been challenging because of their sluggish kinetics, attributed to their limited mass transport properties. Herein, a universal network engineering of hydrogels using a cryogelation method is presented, significantly improving the SAWH kinetics of HSHCs. As a result of the entangled mesh confinements formed during cryogelation, a stable macroporous topology is attained and maintained within the obtained entangled-mesh hydrogels (EMHs), leading to significantly enhanced mass transport properties compared to conventional dense hydrogels (CDHs). With it, corresponding hygroscopic EMHs (HEMHs) simultaneously exhibit faster moisture sorption and solar-driven water desorption. Consequently, a rapid-cycling HEMHs-based harvester delivers a practical freshwater production of 2.85 Lwater kgsorbents -1 day-1 via continuous eight sorption/desorption cycles, outperforming other state-of-the-art hydrogel-based sorbents. Significantly, the generalizability of this strategy has been validated by extending it to other hydrogels used in HSHCs. Overall, this work offers a new approach to efficiently address long-standing challenges of sluggish kinetics in current HSHCs, promoting them toward the next-generation SAWH applications. This article is protected by copyright. All rights reserved.

Topological Insights into Nanostar Dendrimers by Computing the Augmented Zagreb Index.

BACKGROUND: The field of nanobiotechnology uses precise nanofabrication techniques to advance our understanding and control of biological systems. Due to their remarkable properties, dendrimers, which are hyperbranched macromolecular structures with distinct and well-defined architectures, have emerged as pivotal entities within this field. They are gaining increasing attention for their potential to catalyze a paradigm shift in medical therapeutics, biotechnological applications, and advanced material sciences. OBJECTIVE: This paper focuses on a novel analytical expression and determines the precise value of the augmented Zagreb index, a topological descriptor, for eight classes of nanostar dendrimers. METHODS: The Zagreb index is a topological invariant to predict molecular behaviour and reactivity. In this paper, we have explored its application in characterizing the branching of nanostar dendrimers through computational modelling and mathematical rigor. RESULTS: Our research has measured the augmented Zagreb index for nanostar dendrimers, which fall into eight distinct classes. The results better explain the relationship between the dendrimers' topology and chemical properties. This correlation has implications for their structural stability and reactivity, potentially leading to new applications. CONCLUSION: Developing the augmented Zagreb index for nanostar dendrimers is a significant breakthrough in nanobiotechnology. Based on the correlation between the calculated topological index and the corresponding molecular attributes, our analytical approach has opened up new possibilities for designing and synthesizing dendrimers tailored to specific functions in medical and material science applications. This precise topological quantification could significantly enhance the utility and functionalization of dendrimers in cutting-edge nanotechnological applications.

Greater white matter degeneration and lower structural connectivity in non-amnestic vs. amnestic Alzheimer's disease.

Introduction: Multimodal evidence indicates Alzheimer's disease (AD) is characterized by early white matter (WM) changes that precede overt cognitive impairment. WM changes have overwhelmingly been investigated in typical, amnestic mild cognitive impairment and AD; fewer studies have addressed WM change in atypical, non-amnestic syndromes. We hypothesized each non-amnestic AD syndrome would exhibit WM differences from amnestic and other non-amnestic syndromes. Materials and methods: Participants included 45 cognitively normal (CN) individuals; 41 amnestic AD patients; and 67 patients with non-amnestic AD syndromes including logopenic-variant primary progressive aphasia (lvPPA, n = 32), posterior cortical atrophy (PCA, n = 17), behavioral variant AD (bvAD, n = 10), and corticobasal syndrome (CBS, n = 8). All had T1-weighted MRI and 30-direction diffusion-weighted imaging (DWI). We performed whole-brain deterministic tractography between 148 cortical and subcortical regions; connection strength was quantified by tractwise mean generalized fractional anisotropy. Regression models assessed effects of group and phenotype as well as associations with grey matter volume. Topological analyses assessed differences in persistent homology (numbers of graph components and cycles). Additionally, we tested associations of topological metrics with global cognition, disease duration, and DWI microstructural metrics. Results: Both amnestic and non-amnestic patients exhibited lower WM connection strength than CN participants in corpus callosum, cingulum, and inferior and superior longitudinal fasciculi. Overall, non-amnestic patients had more WM disease than amnestic patients. LvPPA patients had left-lateralized WM degeneration; PCA patients had reductions in connections to bilateral posterior parietal, occipital, and temporal areas. Topological analysis showed the non-amnestic but not the amnestic group had more connected components than controls, indicating persistently lower connectivity. Longer disease duration and cognitive impairment were associated with more connected components and fewer cycles in individuals' brain graphs. Discussion: We have previously reported syndromic differences in GM degeneration and tau accumulation between AD syndromes; here we find corresponding differences in WM tracts connecting syndrome-specific epicenters. Determining the reasons for selective WM degeneration in non-amnestic AD is a research priority that will require integration of knowledge from neuroimaging, biomarker, autopsy, and functional genetic studies. Furthermore, longitudinal studies to determine the chronology of WM vs. GM degeneration will be key to assessing evidence for WM-mediated tau spread.

Experimental and computational approaches for membrane protein insertion and topology determination.

Membrane proteins play pivotal roles in a wide array of cellular processes and constitute approximately a quarter of the protein-coding genes across all organisms. Despite their ubiquity and biological significance, our understanding of these proteins remains notably less comprehensive compared to their soluble counterparts. This disparity in knowledge can be attributed, in part, to the inherent challenges associated with employing specialized techniques for the investigation of membrane protein insertion and topology. This review will center on a discussion of molecular biology methodologies and computational prediction tools designed to elucidate the insertion and topology of helical membrane proteins.

Regulating the Topology of Covalent Organic Frameworks for Boosting Overall H2O2 Photogeneration.

Photocatalytic oxygen reduction reactions and water oxidation reactions are extremely promising green approaches for massive H2O2 production. Nonetheless, constructing effective photocatalysts for H2O2 generation is critical and still challenging. Since the network topology has significant impacts on the electronic properties of two dimensional (2D) polymers, herein, for the first time, we regulated the H2O2 photosynthetic activity of 2D covalent organic frameworks (COFs) by topology. Through designing the linking sites of the monomers, we synthesized a pair of novel COFs with similar chemical components on the backbones but distinct topologies. Without sacrificial agents, TBD-COF with cpt topology exhibited superior H2O2 photoproduction performance (6085 and 5448 µmol g-1 h-1 in O2 and air) than TBC-COF with hcb topology through the O2-O2•--H2O2, O2-O2•--O21-H2O2, and H2O-H2O2 three paths. Further experimental and theoretical investigations confirmed that during the H2O2 photosynthetic process, the charge carrier separation efficiency, O2•- generation and conversion, and the energy barrier of the rate determination steps in the three channels, related to the formation of *OOH, *O21, and *OH, can be well tuned by the topology of COFs. The current study enlightens the fabrication of high-performance photocatalysts for H2O2 production by topological structure modulation.

Traffic coordination by reducing jamming attackers in VANET using probabilistic Manhattan Grid Topology for automobile applications.

In recent years Intelligent Transportation System (ITS) has been growing interest in the development of vehicular communication technology. The traffic in India shows considerable fluctuations owing to the static and dynamic characteristics of road vehicles in VANET (Vehicular Adhoc Network). These vehicles take up a convenient side lane position on the road, disregarding lane discipline. They utilize the opposing lane to overtake slower-moving vehicles, even when there are oncoming vehicles approaching. The primary objective of this study is to minimize injuries resulting from vehicle interactions in mixed traffic conditions on undivided roads. This is achieved through the implementation of the Modified Manhattan grid topology, which primarily serves to guide drivers in the correct path when navigating undivided roads. Furthermore, the Fuzzy C-Means algorithm (FCM) is applied to detect potential jamming attackers, while the Modified Fisheye State Routing (MFSR) Algorithm is employed to minimize the amount of information exchanged among vehicles. Subsequently, the Particle Swarm Optimization (PSO) algorithm is developed to enhance the accuracy of determining the coordinates of jamming attackers within individual clusters. The effectiveness of the outcomes is affirmed through the utilization of the Fuzzy C-Means algorithm, showcasing a notable 30% reduction in the number of attackers, along with the attainment of a 70% accuracy rate in this research endeavor.