Variety Of Topologies Research Articles

Self-assembly and condensation of intermolecular poly(UG) RNA quadruplexes.

Poly(UG) or 'pUG' dinucleotide repeats are highly abundant sequences in eukaryotic RNAs. In Caenorhabditis elegans, pUGs are added to RNA 3' ends to direct gene silencing within Mutator foci, a germ granule condensate. Here, we show that pUG RNAs efficiently self-assemble into gel condensates through quadruplex (G4) interactions. Short pUG sequences form right-handed intermolecular G4s (pUG G4s), while longer pUGs form left-handed intramolecular G4s (pUG folds). We determined a 1.05 Å crystal structure of an intermolecular pUG G4, which reveals an eight stranded G4 dimer involving 48 nucleotides, 7 different G and U quartet conformations, 7 coordinated potassium ions, 8 sodium ions and a buried water molecule. A comparison of the intermolecular pUG G4 and intramolecular pUG fold structures provides insights into the molecular basis for G4 handedness and illustrates how a simple dinucleotide repeat sequence can form complex structures with diverse topologies.

Transient voltage stability assessment and margin calculation based on disturbance signal energy feature learning

With the increasing variation of the network topology and the high complexity of the processing measurement data, the transient voltage stability assessment of the new power system is facing significant challenges in low accuracy and high time costs. To address the shortcomings of the existing method and apply it to online assessment, this paper proposes an assessment method based on feature learning for disturbance signal energy (DSE) from bus voltages. Firstly, the relationship between DSE and system transient voltage stability is established, and the calculation of DSE from bus voltage time series is detailed. Subsequently, a transient voltage stability assessment method based on the ID3 Decision Tree algorithm and DSE is proposed. Finally, by employing the Support Vector Machine (SVM) to construct the optimal boundary in the feature space formed by the key buses, the transient voltage stability margin (TVSM) for specific scenarios is proposed. Simulation results on the IEEE 39-bus system demonstrate that the proposed method can rapidly and accurately assess the transient voltage stability of the system and calculate the stability margin, providing intuitive and interpretable results with high engineering application value.

Insights into the Molecular Structure, Stability, and Biological Significance of Non-Canonical DNA Forms, with a Focus on G-Quadruplexes and i-Motifs

This article provides a comprehensive examination of non-canonical DNA structures, particularly focusing on G-quadruplexes (G4s) and i-motifs. G-quadruplexes, four-stranded structures formed by guanine-rich sequences, are stabilized by Hoogsteen hydrogen bonds and monovalent cations like potassium. These structures exhibit diverse topologies and are implicated in critical genomic regions such as telomeres and promoter regions of oncogenes, playing significant roles in gene expression regulation, genome stability, and cellular aging. I-motifs, formed by cytosine-rich sequences under acidic conditions and stabilized by hemiprotonated cytosine–cytosine (C:C+) base pairs, also contribute to gene regulation despite being less prevalent than G4s. This review highlights the factors influencing the stability and dynamics of these structures, including sequence composition, ionic conditions, and environmental pH. Molecular dynamics simulations and high-resolution structural techniques have been pivotal in advancing our understanding of their folding and unfolding mechanisms. Additionally, the article discusses the therapeutic potential of small molecules designed to selectively bind and stabilize G4s and i-motifs, with promising implications for cancer treatment. Furthermore, the structural properties of these DNA forms are explored for applications in nanotechnology and molecular devices. Despite significant progress, challenges remain in observing these structures in vivo and fully elucidating their biological functions. The review underscores the importance of continued research to uncover new insights into the genomic roles of G4s and i-motifs and their potential applications in medicine and technology. This ongoing research promises exciting developments in both basic science and applied fields, emphasizing the relevance and future prospects of these intriguing DNA structures.

Development of functionally graded structure for hip endoprosthesis stem based on lattice-type metamaterial

The paper reports on design and studies of a promising configuration of a hip endoprosthesis stem. The femoral component of the endoprosthesis is constructed based on a metamaterial produced by additive technologies, representing a periodic porous structure of variable topology. The central goal of the design procedure was to numerically determine the spatially graded relative density of the lattice structure within the implant volume by solving the optimization problem. The first stage of development comprised finite element modeling of a system with a solid implant attached, i.e., the optimization space, for seven different loading scenarios. These scenarios were constructed to represent the most typical cases of human motor activity. The second step was to determine the optimal spatial distribution of relative density of the lattice structure in the implant volume, obtained by solving the topological optimization problem under the given loading. After the optimization problem was solved, the distribution of the relative density of the lattice structure was obtained, serving as a basis for constructing a geometric model of a functionally graded porous scaffold for implantation. This result allows achieving an optimal ratio of stiffness and mass properties under the given loading conditions. The approach was verified by direct finite element modeling of the constructed lattice geometry of the implant under specific loading scenarios. Analyzing the stress fields in the volume of the endoprosthesis, we concluded that there were no critical regions in the developed structure, so the approach taken to develop the metamaterial-based endoprosthesis can be deemed correct.

Scaling Laws for Protein Folding under Confinement.

Spatial confinement significantly affects protein folding. Without the confinement provided by chaperones, many proteins cannot fold correctly. However, the quantitative effect of confinement on protein folding remains elusive. In this study, we observed scaling laws between the variation in folding transition temperature and the size of confinement, (Tf - Tfbulk)/Tfbulk ∼ L-ν. The scaling exponent v is significantly influenced by both the protein's topology and folding cooperativity. Specifically, for a given protein, v can decrease as the folding cooperativity of the model increases, primarily due to the heightened sensitivity of the unfolded state energy to changes in cage size. For proteins with diverse topologies, variations in topological complexity influence scaling exponents in multiple ways. Notably, v exhibits a clear positive correlation with contact order and the proportion of nonlocal contacts, as this complexity significantly enhances the sensitivity of entropy loss in the unfolded state. Furthermore, we developed a novel scaling argument yielding 5/3 ≤ ν ≤ 10/3, consistent with the simulation results.

Photochemical Control of Network Topology in PEG Hydrogels.

Hydrogels are often synthesized through photoinitiated step-, chain-, and mixed-mode polymerizations, generating diverse network topologies and resultant material properties that depend on the underlying network connectivity. While many photocrosslinking reactions are available, few afford controllable connectivity of the hydrogel network. Herein, a versatile photochemical strategy is introduced for tuning the structure of poly(ethylene glycol) (PEG) hydrogels using macromolecular monomers functionalized with maleimide and styrene moieties. Hydrogels are prepared along a gradient of topologies by varying the ratio of step-growth (maleimide dimerization) to chain-growth (maleimide-styrene alternating copolymerization) network-forming reactions. The initial PEG content and final network physical properties (e.g., modulus, swelling, diffusivity) are tailored in an independent manner, highlighting configurable gel mechanics and reactivity. These photochemical reactions allow high-fidelity photopatterning and 3D printing and are compatible with 2D and 3D cell culture. Ultimately, this photopolymer chemistry allows facile control over network connectivity to achieve adjustable material properties for broad applications.

Autonomous Sensing Architected Materials

AbstractIntegrating autonomous sensing materials into future applications necessitates developing advanced multiscale multiphysics predictive models. This study introduces an experimentally informed predictive framework for autonomous sensing architected materials, combining theoretical and computational methodologies. By incorporating stress‐dependent electrical resistivity through anisotropic piezoresistive constitutive effects, alongside considering material, geometric, and contact nonlinearities, the proposed multiscale model captures the architecture‐dependent piezoresistive responses of lattice composites produced via additive manufacturing of polyetherimide (PEI)/carbon nanotube (CNT) nanoengineered feedstock. The PEI/CNT composite exhibits exceptional strength (105 MPa), stiffness (3368 MPa), and strain sensitivity (gauge factor ≈13), translating into remarkable piezoresistive characteristics for the PEI/CNT lattice composites, surpassing existing works (gauge factor ≈3 to 11). This multiscale finite element model accurately predicts both macroscopic piezoresistive responses and the influence of architectural and topological variations on electric current paths, validated via infrared thermography analysis. Additionally, an Ashby chart for the gauge factor of PEI/CNT lattice composites suggests their prediction through a scaling law similar to mechanical properties, underscoring the tunable strain and damage sensitivity of these materials. The combined experimental, theoretical, and numerical findings offer critical insights into optimizing piezoresistive composites through architected design, with profound implications for smart orthopedics, structural health monitoring, sensors, batteries, and other multifunctional applications.

The influence of G-tract and loop length on the topological variability of putative five and six G-quartet DNA structures in the human genome

Local variation of DNA structure and its dynamic nature play an essential role in the regulation of important biological processes. One of the most prominent noncanonical structures are G-quadruplexes, which form in vivo within guanine-rich regions and have been demonstrated to be involved in the regulation of transcription, translation and telomere maintenance. We provide an analysis of G-quadruplex formation in sequences with five and six guanine residues long G-tracts, which have emerged from the investigation of the gapless human genome and are associated with genes related to cancer and neurodegenerative diseases. We systematically explored the effect of G-tract and loop elongations by means of NMR and CD spectroscopy and polyacrylamide electrophoresis. Despite both types of elongation leading up to structural polymorphism, we successfully determined the topologies of four out of eight examined sequences, one of which contributes to a very scarce selection of currently known intramolecular four G-quartet structures in potassium solutions. We demonstrate that examined sequences are incompatible with five or six G-quartet structures with propeller loops, although the compatibility with other loop types cannot be factored out. Lastly, we propose a novel approach towards specific G-quadruplex targeting that could be implemented in structures with more than four G-quartets.

Superelliptic quantum rings under orthogonally applied electric and magnetic fields: Electronic and thermal properties

A systematic study was undertaken to analyze the electronic and thermal properties of a single electron within a superelliptic quantum ring under orthogonal magnetic and electric fields. The corresponding Schrödinger equation for this system was numerically solved using the finite element method within the effective mass approximation. The influence of geometric effects associated with the morphology of the inner and outer contours of the superellipse, as well as the impact of external fields on electronic and thermal properties, were analyzed. The flexibility of the superellipse shape parameters considered in this study allows the consideration of different types of rings and the derivation of several electronic properties previously published in the literature as particular cases of our model. It was observed that thermal properties are highly sensitive to external fields and the geometrical potential linked to the topological variations experienced by the superellipse during the growth of the quantum rings.

Multi-Graph Assessment of Temporal and Extratemporal Lobe Epilepsy in Resting-State fMRI

Epilepsy is a common neurological disorder that affects millions of people worldwide, disrupting brain networks and causing recurrent seizures. In this regard, investigating the distinctive characteristics of brain connectivity is crucial to understanding the underlying neural processes of epilepsy. However, the various graph-theory frameworks and different estimation measures may yield significant variability among the results of different studies. On this premise, this study investigates the brain network topological variations between patients with temporal lobe epilepsy (TLE) and extratemporal lobe epilepsy (ETLE) using both directed and undirected network connectivity methods as well as different graph-theory metrics. Our results reveal distinct topological differences in connectivity graphs between the two epilepsy groups, with TLE patients displaying more disassortative graphs at lower density levels compared to ETLE patients. Moreover, we highlight the variations in the hub regions across different network metrics, underscoring the importance of considering various centrality measures for a comprehensive understanding of brain network dynamics in epilepsy. Our findings suggest that the differences in brain network organization between TLE and ETLE patients could be attributed to the unique characteristics of each epilepsy type, offering insights into potential biomarkers for type-specific epilepsy diagnosis and treatment.

Quality of Service-Aware Multi-Objective Enhanced Differential Evolution Optimization for Time Slotted Channel Hopping Scheduling in Heterogeneous Internet of Things Sensor Networks.

The emergence of the Internet of Things (IoT) has attracted significant attention in industrial environments. These applications necessitate meeting stringent latency and reliability standards. To address this, the IEEE 802.15.4e standard introduces a novel Medium Access Control (MAC) protocol called Time Slotted Channel Hopping (TSCH). Designing a centralized scheduling system that simultaneously achieves the required Quality of Service (QoS) is challenging due to the multi-objective optimization nature of the problem. This paper introduces a novel optimization algorithm, QoS-aware Multi-objective enhanced Differential Evolution optimization (QMDE), designed to handle the QoS metrics, such as delay and packet loss, across multiple services in heterogeneous networks while also achieving the anticipated service throughput. Through co-simulation between TSCH-SIM and Matlab, R2023a we conducted multiple simulations across diverse sensor network topologies and industrial QoS scenarios. The evaluation results illustrate that an optimal schedule generated by QMDE can effectively fulfill the QoS requirements of closed-loop supervisory control and condition monitoring industrial services in sensor networks from 16 to 100 nodes. Through extensive simulations and comparative evaluations against the Traffic-Aware Scheduling Algorithm (TASA), this study reveals the superior performance of QMDE, achieving significant enhancements in both Packet Delivery Ratio (PDR) and delay metrics.

Tide level prediction during typhoons based on variable topology in graph convolution recurrent neural networks

Accurate tide level prediction is crucial for storm surge prevention and mitigation. In this study, we propose a regional multi-station tide-level prediction model (RMSTL) based on a graph convolution recurrent network that utilizes spatial and temporal information from historical tide levels and meteorological data to predict tide levels at multiple stations. Six stations in Changjiang Estuary, China, were selected for this case study. We explored the change in the accuracy of forecasting the tide-level during typhoons under a distinct topology of the tidal station network and the weights of the stations. The results showed that: (1) the prediction accuracy of RMSTL model behaved better than that of the other three widely used models; (2) the RMSTL model can be made more accurate by adding the weights of the edges or the number of edges of the topology; the value of RMSE decreased from 0.369 to 0.278, and the value of MAE decreased from 0.260 to 0.189; (3) when the dataset was limited, increasing the weights of the edges had a slightly better effect than increasing the weights of the edges in terms of improving the accuracy of the prediction results. The proposed method opens new avenues for forecasting storm surges.

A new understanding of coronary curvature and haemodynamic impact on the course of plaque onset and progression

The strong link between atherosclerosis and luminal biomechanical stresses is well established. Yet, this understanding has not translated into preventative coronary diagnostic imaging, particularly due to the under-explored role of coronary anatomy and haemodynamics in plaque onset, which we aim to address with this work. The left coronary trees of 20 non-stenosed (%diameter stenosis [%DS] = 0), 12 moderately (0 < %DS < 70) and 7 severely (%DS ≥ 70) stenosed cases were dissected into bifurcating and non-bifurcating segments for whole-tree and segment-specific comparisons, correlating nine three-dimensional coronary anatomical features, topological shear variation index (TSVI) and luminal areas subject to low time-average endothelial shear stress (%LowTAESS), high oscillatory shear index (%HighOSI) and high relative residence time (%HighRRT). We found that TSVI is the only metric consistently differing between non-stenosed and stenosed cases across the whole tree, bifurcating and non-bifurcating segments ( p < 0.002, AUC = 0.876), whereas average curvature and %HighOSI differed only for the whole trees ( p < 0.024) and non-bifurcating segments ( p < 0.027), with AUC > 0.711. Coronary trees with moderate or severe stenoses differed only in %LowTAESS ( p = 0.009) and %HighRRT ( p = 0.012). This suggests TSVI, curvature and %HighOSI are potential factors driving plaque onset, with greater predictive performance than the previously recognized %LowTAESS and %HighRRT, which appears to play a role in plaque progression.

Concurrent topology optimization of sandwich structures with multi-configuration and variable-diameter lattice infill

The superior stiffness-to-weight and strength-to-weight mechanical advantages of sandwich structures can be fully exploited through concurrent design of entire topology, infill configuration and density, where the high-performance yet complicated structure can be fabricated through additive manufacturing. However, the emerging design challenges are concurrent design updating related to sandwich topology, infill configuration and density, which is a design problem with continuous and discrete variables mathematically. In this paper, a concurrent topology optimization is proposed for sandwich structures with multi-configuration and variable-diameter lattice infill. Three design variable fields are employed to describe the fundamental topology considering sandwich structural topology, infill configuration and density simultaneously. Corresponding material interpolation model is developed by combining DSP-based shell-infill description and multi-response latent-variable surrogate model based effective material property calculation. Two-stage design model is formulated as a rough design model considering all design variables followed by a refined design model with only infill density variables, which is developed to strictly satisfy the material allowance constraint due to the mapping between discrete infill configuration variables and continuous latent configuration variables. Corresponding sensitivities of compliance and constraint with respect to the structural topology, infill configuration and density variables are derived, and the method of moving asymptotes (MMA) is employed to solve the design model efficiently. Several numerical examples are presented to systematically demonstrate the effectiveness of the proposed approach.

A sliding mode based foot-end trajectory consensus control method with variable topology for legged motion of heavy-duty robot

Rational foot-end trajectory planning and control are of great significance for stable-legged walking of heavy-duty multi-legged robots. To achieve a fast, active, and compliant response of the leg actuator to disturbances for improvement of the stability and flexibility of the heavy-duty legged robot system during continuous walking on rough roads, a legged consensus control method (LCC) is proposed. Firstly, the LCC includes a foot-end trajectory planner model for designing the trajectory during the swing phase to ensure that the robot’s feet are always in a safe workspace during legged motion with continuously variable direction. Secondly, LCC constructs a consensus control method for encoding foot-end position and velocity consensus error based on variable topology networks. Six legs are treated as six intelligent agents and divided into two fully connected networks: the swing phase and stance phase, to achieve smooth and consistent motion that satisfies the geometric constraints of the robot. The foot-end agent can switch between swing and stance groups according to the state of the contact with the environment accompanied by the amendment topology, to enhance the robustness of the robot system through fast compliance control of the foot-end kinematics state. Then, the sliding mode control method based on consensus velocity and position error is deduced in LCC. The sliding mode surface is designed to make the three control variables realize stable movement with a consistent state of foot-end in three X,Y,Z-axis respectively, thereby enhancing the stability of foot-end state and fuselage posture. Finally, simulation and experiments have verified that the proposed LCC can assist legged-robot perform relatively steady legged motion with continuously variable direction on various rugged roads. The body attitude Root Mean Square Error (RMSE) is quickly reduced by 81.0% compared with independent PI control. The LCC algorithm code is publicly available at https://github.com/bjmyX/LCC_code.

Stress-related discrete variable topology optimization with handling non-physical stress concentrations

The accuracy of stress calculation with a fixed mesh significantly affects the stress-based topology optimization, due to potential non-physical stress concentrations in voxel-based topology descriptions. This paper proposes a novel problem-independent machine learning enhanced high-precision stress calculation method (PIML-HPSCM) to address this challenge. As an immersed analysis method, PIML-HPSCM combines the high efficiency of fixed mesh with the accuracy of body-fitted mesh, without complex integration schemes of other immersed methods. PIML-HPSCM utilizes the extended multiscale finite element method to depict the material heterogeneity within high-resolution boundary elements. The accurate stress field can then be calculated conveniently by establishing stress evaluation matrices of high-resolution boundary elements. Moreover, the PIML is independent of problem settings and is applicable for various problems with the same governing equation type. Invoking the offline-trained neural network online can enhance stress calculation efficiency by 10–20 times. The stress-based discrete variable topology optimization, which naturally avoids singular stress phenomenon, is efficiently addressed by the sequential approximate integer programming method with PIML-HPSCM. Results from 2D and 3D examples demonstrate that the stresses calculated by PIML-HPSCM are consistent with those by body-fitted mesh, and optimized designs effectively eliminate stress concentrations of initial designs and have uniform stress distributions.

Counting U(N)⊗r⊗O(N)⊗q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$U(N)^{\\otimes r}\\otimes O(N)^{\\otimes q}$$\\end{document} invariants and tensor model observables

U(N)⊗r⊗O(N)⊗q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$U(N)^{\\otimes r} \\otimes O(N)^{\\otimes q}$$\\end{document} invariants are constructed by contractions of complex tensors of order r+q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r+q$$\\end{document}, also denoted (r, q). These tensors transform under r fundamental representations of the unitary group U(N) and q fundamental representations of the orthogonal group O(N). Therefore, U(N)⊗r⊗O(N)⊗q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$U(N)^{\\otimes r} \\otimes O(N)^{\\otimes q}$$\\end{document} invariants are tensor model observables endowed with a tensor field of order (r, q). We enumerate these observables using group theoretic formulae, for tensor fields of arbitrary order (r, q). Inspecting lower-order cases reveals that, at order (1, 1), the number of invariants corresponds to a number of 2- or 4-ary necklaces that exhibit pattern avoidance, offering insights into enumerative combinatorics. For a general order (r, q), the counting can be interpreted as the partition function of a topological quantum field theory (TQFT) with the symmetric group serving as gauge group. We identify the 2-complex pertaining to the enumeration of the invariants, which in turn defines the TQFT, and establish a correspondence with countings associated with covers of diverse topologies. For r>1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r>1$$\\end{document}, the number of invariants matches the number of (q-dependent) weighted equivalence classes of branched covers of the 2-sphere with r branched points. At r=1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r=1$$\\end{document}, the counting maps to the enumeration of branched covers of the 2-sphere with q+3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$q+3$$\\end{document} branched points. The formalism unveils a wide array of novel integer sequences that have not been previously documented. We also provide various codes for running computational experiments.

Scalable rapid framework for evaluating network worst robustness with machine learning

Robustness is pivotal for comprehending, designing, optimizing, and rehabilitating networks, with simulation attacks being the prevailing evaluation method. Simulation attacks are often time-consuming or even impractical; however, a more crucial yet persistently overlooked drawback is that any attack strategy merely provides a potential paradigm of disintegration. The key concern is: in the worst-case scenario or facing the most severe attacks, what is the limit of robustness, referred to as “Worst Robustness”, for a given system? Understanding a system’s worst robustness is imperative for grasping its reliability limits, evaluating protective capabilities, and determining associated design and security maintenance costs. To address these challenges, we introduce the concept of Most Destruction Attack (MDA), which is based on the idea of knowledge stacking. MDA is employed to assess the worst robustness of networks, followed by the application of an adapted CNN algorithm to expedite the prediction of worst robustness. We establish the logical validity of MDA and highlight the exceptional performance of the adapted CNN algorithm in predicting the worst robustness across diverse network topologies. This Worst Robustness Evaluation (WRE) framework is scalable, accommodating various attack strategies, whether existing or prospective, and enhancing predictive capabilities with more powerful machine learning algorithms.

Innovative Tools for DNA Topology Probing in Human Cells Reveal a Build-Up of Positive Supercoils Following Replication Stress at Telomeres and at the FRA3B Fragile Site.

Linear unconstrained DNA cannot harbor supercoils since these supercoils can diffuse and be eliminated by free rotation of the DNA strands at the end of the molecule. Mammalian telomeres, despite constituting the ends of linear chromosomes, can hold supercoils and be subjected to topological stress. While negative supercoiling was previously observed, thus proving the existence of telomeric topological constraints, positive supercoils were never probed due to the lack of an appropriate tool. Indeed, the few tools available currently could only investigate unwound (Trioxsalen) or overwound (GapR) DNA topology (variations in twist) but not the variations in writhe (supercoils and plectonemes). To address this question, we have designed innovative tools aimed at analyzing both positive and negative DNA writhe in cells. Using them, we could observe the build-up of positive supercoils following replication stress and inhibition of Topoisomerase 2 on telomeres. TRF2 depletion caused both telomere relaxation and an increase in positive supercoils while the inhibition of Histone Deacetylase I and II by TSA only caused telomere relaxation. Moving outside telomeres, we also observed a build-up of positive supercoils on the FRA3B fragile site following replication stress, suggesting a topological model of DNA fragility for this site.

Harmonics mitigation in distribution networks comprising smart online electric vehicles chargers based on equal sharing algorithm

This study focuses on analyzing the distribution network's performance due to the scheduled integration of electric vehicles (EVs). In this context, an online equal sharing scheduled algorithm is utilized to monitor and manage the charging process of EVs inside the charging stations (CSs). By this way, the planned charging demand may aid in minimizing the power stresses at the point of common coupling (PCC). Afterwards, the penetration of CSs into practical distribution networks is investigated and resolved from power quality perspectives. Meanwhile, voltage and current harmonic distortions of 3.3 % and 8.3 %, respectively resulted at the PCC due to installing these harmonic injection sources. It is worth mentioning that diverse topologies of passive filters are interrogated in this research for harmonics mitigation purposes. Single tuned passive filters demonstrate their superiority over other architectures in harmonics suppression with the simplest and most cost-effective solution. Additionally, the overloading stresses over the installed package substation of about 7 % are also alleviated permitting the CS to charge with full capacity. Finally, this work presents a forward step in the fields of EVs smart charging and harmonics mitigation in practical distribution gids.