Loop Structure Research Articles

Thylakoid-based green preparation of porous microneedles for antibiotic residues detection in food samples

BackgroundAntibiotic residues in food chain have raised concerns regarding their toxicity and involvement in antimicrobial resistance. However, most existing antibiotic biosensors are primarily applicable to liquid food samples. Given the complex matrix characteristics of foods, there is an urgent need for the development of effective antibiotic detection platforms that exhibit high universality and flexibility. Porous microneedles (PMN) are microdevice structures with needle-like shapes and microscale pores throughout their composition, which facilitate rapid sampling. Consequently, the integration of PMN with biosensors holds significant promise for the detection of antibiotic residues in complex food samples. ResultsIn this study, hydrogel-forming PMN are fabricated by leveraging the oxygen-production capacity of thylakoid to generate bubbles and form porous structures. These PMN are then integrated with a fluorescence aptasensor for the quantification of the antibiotic netilmicin. The aptasensor consists of a netilmicin (NET) aptamer with stem loop and hairpin structure, which facilitated the binding of SYBR Green I to produce a fluorescent signal. In the presence of NET, the complete binding between NET and the aptamer results in a reduction of fluorescence intensity, thereby generating a detectable signal change for the detection of NET. Utilizing capillary action accelerate fluid extraction (2.9 times faster than nonporous microneedles) and a large specific surface area (5.1072 m2/g) conducive to aptasensor adsorb, the PMN achieve efficient capture and quantification of antibiotic with limits of detection and quantitation of 5.99 nM and 19.8 nM, respectively. SignificancePorous microneedles with tunable porosity and desirable mechanical properties are successfully fabricated. The integration of PMN with aptasensor enable the efficient detection of netilmicin in fish, milk and river water samples, demonstrating high recovery rates. The PMN represent potential tools for the convenient and rapid detection of antibiotic residues within complex food matrices, thereby enhancing food safety monitoring.

Circular supply chains as complex adaptive ecosystems: A simulation-based approach

Circular Supply Chains (CSCs) are self-sustained ecosystems designed to pursue Circular Economy principles. Compared to linear supply chains, they involve new classes of stakeholders performing circular activities and multiple resource streams (e.g. virgin materials, by-products, end-of-life products, waste) moving forward and back the original and different supply chains. Therefore, CSCs are characterized by increasing complexity that call for proper coordination mechanisms. In this regard, we propose a theoretical framework, based on the theory of Complex Adaptive Systems, capturing the static and dynamic dimensions of CSC complexity which characterize the archetype of a CSC and then we develop a novel agent-based model to simulate the effect of competition and collaboration mechanisms on CSC performance across different CSC archetypes. The results of numerical simulations suggest which mechanism to adopt for the coordination of certain CSC archetypes: while competition is always detrimental, local collaboration is effective for CSCs with closed loop structures, and global collaboration is instead required in presence of open and hybrid loop structures.

Beta bursts in the parkinsonian cortico-basal ganglia network form spatially discrete ensembles

Defining spatial synchronisation of pathological beta oscillations is important, given that many theories linking them to parkinsonian symptoms propose a reduction in the dimensionality of the coding space within and/or across cortico-basal ganglia structures. Such spatial synchronisation could arise from a single process, with widespread entrainment of neurons to the same oscillation. Alternatively, the partially segregated structure of cortico-basal ganglia loops could provide a substrate for multiple ensembles that are independently synchronized at beta frequencies. Addressing this question requires an analytical approach that identifies groups of signals with a statistical tendency for beta synchronisation, which is unachievable using standard pairwise measures. Here, we utilized such an approach on multichannel recordings of background unit activity (BUA) in the external globus pallidus (GP) and subthalamic nucleus (STN) in parkinsonian rats. We employed an adapted version of a principle and independent component analysis-based method commonly used to define assemblies of single neurons (i.e., neurons that are synchronized over short timescales). This analysis enabled us to define whether changes in the power of beta oscillations in local ensembles of neurons (i.e., the BUA recorded from single contacts) consistently covaried over time, forming a “beta ensemble”. Multiple beta ensembles were often present in single recordings and could span brain structures. Membership of a beta ensemble predicted significantly higher levels of short latency (<5 ms) synchrony in the raw BUA signal and phase synchronisation with cortical beta oscillations, suggesting that they comprised clusters of neurons that are functionally connected at multiple levels, despite sometimes being non-contiguous in space. Overall, these findings suggest that beta oscillations do not comprise of a single synchronisation process, but rather multiple independent activities that can bind both spatially contiguous and non-contiguous pools of neurons within and across structures. As previously proposed, such ensembles provide a substrate for beta oscillations to constrain the coding space of cortico-basal ganglia circuits.

A one-step fabrication method of flexible magnetostrictive fiber ribbon based on Fe50Co50 alloy and its characteristics study

Magnetostrictive materials are widely used in sensors, actuators and micro-nano electronics because of their excellent performance. However, traditional rigid magnetostrictive materials are difficult to adapt to the flexible and deformable structure of flexible electronic devices. In order to solve the deformation, sensing and control problems in the field of flexible electronics, a one-step new method of is proposed to deposit Fe50Co50 fiber ribbons with reciprocated and loop structures on flexible substrate. Helmholtz coil is designed to test the magnetostrictive positive effect ability of Fe50Co50 ribbon, and its magnetostrictive strain curve is analyzed. Furthermore, frequency domain characteristics of Fe50Co50 ribbon are tested, and the influence of externally driving magnetic field on elastic layer materials and resonance output characteristics is analyzed. The experimental results show that Fe50Co50 ribbon exhibits good low-frequency magnetic field characteristics. And free end displacement of Fe50Co50 ribbon based on loop structure, under 12 mT alternating magnetic field and 134 mT bias magnetic field, reaches a peak-to-peak value of 870 μm for first-order resonance, and 320 μm for second-order resonance. This verifies that Fe50Co50 ribbon possesses the expected properties that magnetostrictive materials should have, and innovatively breaks through the technical bottleneck of manufacturing flexible micro-nano magnetostrictive energy exchange components.

Research Progress of Circular RNA CircHIPK3 in Non-small Cell Lung Cancer

Lung cancer ranks among the most prevalent and deadliest malignancies worldwide. Despite significant strides in targeted therapies and immunotherapy for lung cancer, curing the disease remains a highly prioritized issue. Circular RNAs (circRNAs), recently discovered RNA molecules characterized by covalently closed loop structures, possess features such as structural stability, sequence conservation, and disease-specific expression. Cutting-edge medical research has linked circRNA dysregulation to the progression of various cancers. Among these, circular RNA HIPK3 (circHIPK3), an oncogenic gene primarily derived from the second exon of the HIPK3 gene, has emerged as a focal point of investigation. Increasing evidences suggest that circHIPK3 is involved in the development of non-small cell lung cancer (NSCLC) and other malignancies. Aberrant expression of circHIPK3 is closely associated with the disease mechanisms, diagnosis, treatment, and prognosis of NSCLC. This review discusses the latest research advancements on circHIPK3 in NSCLC, aiming to promote precise diagnosis and treatment of lung cancer. .

Sarbecovirus programmed ribosome frameshift RNA element folding studied by NMR spectroscopy and comparative analyses.

The programmed ribosomal frameshift (PRF) region is found in the RNA genome of all coronaviruses and shifts the ribosome reading frame through formation of a three-stem pseudoknot structure, allowing the translation of essential viral proteins. Using NMR spectroscopy, comparative sequence analyses and functional assays we show that, in the absence of the ribosome, a 123-nucleotide sequence encompassing the PRF element of SARS-CoV-2 adopts a well-defined two-stem loop structure that is conserved in all SARS-like coronaviruses. In this conformation, the attenuator hairpin and slippery site nucleotides are exposed in the first stem-loop and two pseudoknot stems are present in the second stem-loop, separated by an 8-nucleotide bulge. Formation of the third pseudoknot stem depends on pairing between bulge nucleotides and base-paired nucleotides of the upstream stem-loop, as shown by a PRF construct where residues of the upstream stem were removed, which formed the pseudoknot structure and had increased frameshifting activity in a dual-luciferase assay. The base-pair switch driving PRF pseudoknot folding was found to be conserved in several human non-SARS coronaviruses. The collective results suggest that the frameshifting pseudoknot structure of these viruses only forms transiently in the presence of the translating ribosome. These findings clarify the frameshifting mechanism in coronaviruses and can have a beneficial impact on antiviral drug discovery.

A pH-sensitive motif in an outer membrane protein activates bacterial membrane vesicle production

Outer membrane vesicles (OMVs) produced by Gram-negative bacteria have key roles in cell envelope homeostasis, secretion, interbacterial communication, and pathogenesis. The facultative intracellular pathogen Salmonella Typhimurium increases OMV production inside the acidic vacuoles of host cells by changing expression of its outer membrane proteins and modifying the composition of lipid A. However, the molecular mechanisms that translate pH changes into OMV production are not completely understood. Here, we show that the outer membrane protein PagC promotes OMV production through pH-dependent interactions between its extracellular loops and surrounding lipopolysaccharide (LPS). Structural comparisons and mutational studies indicate that a pH-responsive amino acid motif in PagC extracellular loops, containing PagC-specific histidine residues, is crucial for OMV formation. Molecular dynamics simulations suggest that protonation of histidine residues leads to changes in the structure and flexibility of PagC extracellular loops and their interactions with the surrounding LPS, altering membrane curvature. Consistent with that hypothesis, mimicking acidic pH by mutating those histidine residues to lysine increases OMV production. Thus, our findings reveal a mechanism for sensing and responding to environmental pH and for control of membrane dynamics by outer membrane proteins.

Optimization of isolation valve placement to improve reliability and resilience of water distribution systems

Optimization of isolation valve layout is critical to improve post-failure response restoration of water distribution systems (WDSs) when pipes are damaged. Many studies have been done to optimize isolation valves to improve reliability of WDSs, but less attention has been given to improving the seismic resilience. This paper proposes a multi-objective optimization model for isolation valves placement considering reliability and seismic resilience simultaneously. The newly developed model employs topology-based reliability and resilience indicators to improve efficiency of optimization algorithm by identification of accidental segments and unintended isolation segments using graph theory without hydraulic simulation. The optimization model was applied to two networks with different loop structures. Subsequently, searching trajectory guided by constraints is investigated and performance of isolation valve placements under different valve densities are assessed including topological structure of isolation valves, criticality of segments and isolation valves, and seismic resilience. The results showed that optimal layout of isolation valves obtained through optimization can improve the reliability and resilience of the system. Our novel approach can benefit water utilities that frequently encounter optimal placement of isolation valves to improve the reliability and resilience of WDSs.

Mechanism of allosteric inhibition of human p97/VCP ATPase and its disease mutant by triazole inhibitors

Human p97 ATPase is crucial in various cellular processes, making it a target for inhibitors to treat cancers, neurological, and infectious diseases. Triazole allosteric p97 inhibitors have been demonstrated to match the efficacy of CB-5083, an ATP-competitive inhibitor, in cellular models. However, the mechanism is not well understood. This study systematically investigates the structures of new triazole inhibitors bound to both wild-type and disease mutant forms of p97 and measures their effects on function. These inhibitors bind at the interface of the D1 and D2 domains of each p97 subunit, shifting surrounding helices and altering the loop structures near the C-terminal α2 G helix to modulate domain-domain communications. A key structural moiety of the inhibitor affects the rotameric conformations of interacting side chains, indirectly modulating the N-terminal domain conformation in p97 R155H mutant. The differential effects of inhibitor binding to wild-type and mutant p97 provide insights into drug design with enhanced specificity, particularly for oncology applications.

Phylodynamic and Epistatic Analysis of Coxsackievirus A24 and Its Variant.

Coxsackievirus A24 (CV-A24) is a human enterovirus that causes acute flaccid paralysis. However, a Coxsackievirus A24 variant (CV-A24v) is the most common cause of eye infections. The causes of these variable pathogenicity and tissue tropism remain unclear. To elucidate the phylodynamics of CV-A24 and CV-A24v, we analyzed a dataset of 66 strains using Bayesian phylodynamic approach, along with detailed sequence variation and epistatic analyses. Six CV-A24 strains available in GenBank and 60 CV-A24v strains, including 11 Taiwanese strains, were included in this study. The results revealed striking differences between CV-A24 and CV-A24v exhibiting long terminal branches in the phylogenetic tree, respectively. CV-A24v presented distinct ladder-like clustering, indicating immune escape mechanisms. Notably, 10 genetic recombination events in the 3D regions were identified. Furthermore, 11 missense mutation signatures were detected to differentiate CV-A24 and CV-A24v; among these mutations, the F810Y substitution may significantly affect the secondary structure of the GH loop of VP1 and subsequently affect the epitopes of the capsid proteins. In conclusion, this study provides critical insights into the evolutionary dynamics and epidemiological characteristics of CV-A24 and CV-A24v, and highlights the differences in viral evolution and tissue tropism.

A neural basis for learning sequential memory in brain loop structures.

Behaviors often involve a sequence of events, and learning and reproducing it is essential for sequential memory. Brain loop structures refer to loop-shaped inter-regional connection structures in the brain such as cortico-basal ganglia-thalamic and cortico-cerebellar loops. They are thought to play a crucial role in supporting sequential memory, but it is unclear what properties of the loop structure are important and why. In this study, we investigated conditions necessary for the learning of sequential memory in brain loop structures via computational modeling. We assumed that sequential memory emerges due to delayed information transmission in loop structures and presented a basic neural activity model and validated our theoretical considerations with spiking neural network simulations. Based on this model, we described the factors for the learning of sequential memory: first, the information transmission delay should decrease as the size of the loop structure increases; and second, the likelihood of the learning of sequential memory increases as the size of the loop structure increases and soon saturates. Combining these factors, we showed that moderate-sized brain loop structures are advantageous for the learning of sequential memory due to the physiological restrictions of information transmission delay. Our results will help us better understand the relationship between sequential memory and brain loop structures.

An ensemble approach for circular RNA-disease association prediction using variational autoencoder and genetic algorithm.

Circular RNAs (circRNAs) are endogenous non-coding RNAs with a covalently closed loop structure. They have many biological functions, mainly regulatory ones. They have been proven to modulate protein-coding genes in the human genome. CircRNAs are linked to various diseases like Alzheimer's disease, diabetes, atherosclerosis, Parkinson's disease and cancer. Identifying the associations between circular RNAs and diseases is essential for disease diagnosis, prevention, and treatment. The proposed model, based on the variational autoencoder and genetic algorithm circular RNA disease association (VAGA-CDA), predicts novel circRNA-disease associations. First, the experimentally verified circRNA-disease associations are augmented with the synthetic minority oversampling technique (SMOTE) and regenerated using a variational autoencoder, and feature selection is applied to these vectors by a genetic algorithm (GA). The variational autoencoder effectively extracts features from the augmented samples. The optimized feature selection of the genetic algorithm effectively carried out dimensionality reduction. The sophisticated feature vectors extracted are then given to a Random Forest classifier to predict new circRNA-disease associations. The proposed model yields an AUC value of 0.9644 and 0.9628 under 5-fold and 10-fold cross-validations, respectively. The results of the case studies indicate the robustness of the proposed model.

Multi-pose dress simulation of fully-fashioned knitted skirt based on loop structure

To realize the three-dimensional structure simulation and dress simulation of the fully-fashioned knitted skirt, based on the study of the structural characteristics of the fully-fashioned knitted skirt, the mathematical model of the knitted skirt pattern, the loop geometry mathematical model and the loop mesh model were established by the method of mathematical modelling. The spatial transformation relationship of each pair of triangles in the plate model and the three-dimensional surface model was calculated by using the spatial matrix. To draw a loop on the surface model's triangular patch, spatial transformation was used to calculate the grid point positions and obtain three-dimensional coordinates of the loop-type value points. Based on C# programming language, the human body model in the database was matched with the knitted skirt surface model, and the three-dimensional graphics library Open-GL and loop drawing method were used to realize the simulation of the knitted skirt dress based on the loop structure. The simulation method was tested by the dressing effects of hip skirts and umbrella skirts with different postures. The results showed that the spatial transformation could realize the fast transformation and calculate the coordinates of the loop-type value points. The C# programming language and Open-GL technologies realized the visual simulation of the fully-fashioned knitted skirt and the multi-pose dressing simulation based on the loop structure.

The complete pathway for co-transcriptional mRNA maturation within a large protein of a non-segmented negative-strand RNA virus.

Non-segmented negative-strand (NNS) RNA viruses, such as rabies, Nipahand Ebola, produce 5'-capped and 3'-polyadenylated mRNAs resembling higher eukaryotic mRNAs. Here, we developed a transcription elongation-coupled pre-mRNA capping system for vesicular stomatitis virus (VSV, a prototypic NNS RNA virus). Using this system, we demonstrate that the single-polypeptide RNA-dependent RNA polymerase (RdRp) large protein (L) catalyzes all pre-mRNA modifications co-transcriptionally in the following order: (i) 5'-capping (polyribonucleotidylation of GDP) to form a GpppA cap core structure, (ii) 2'-O-methylation of GpppA into GpppAm, (iii) guanine-N7-methylation of GpppAm into m7GpppAm (cap 1), (iv) 3'-polyadenylation to yield a poly(A) tail. The GDP polyribonucleotidyltransferase (PRNTase) domain of L generated capped pre-mRNAs of 18 nucleotides or longer via the formation of covalent enzyme-pre-mRNA intermediates. The single methyltransferase domain of L sequentially methylated the cap structure only when pre-mRNAs of 40 nucleotides or longer were associated with elongation complexes. These results suggest that the formation of pre-mRNA closed loop structures in elongation complexes via the RdRp and PRNTase domains followed by the RdRp and MTase domains on the same polypeptide is required for the cap 1 formation during transcription. Taken together, our findings indicate that NNS RNA virus L acts as an all-in-one viral mRNA assembly machinery.

Band-Pass and Band-Stop Filter Frequency Selective Surface with Harmonic Suppression

This study investigated a frequency-selective surface (FSS) that is used to suppress harmonics affecting the performance and accuracy of radar systems. One side of the FSS features a metal grid structure, and when converted into an equivalent circuit model, it exhibits the characteristics of a band-pass filter with an L and C parallel structure. The other side of the FSS features a metallic loop structure and when represented as an equivalent circuit model, it exhibits the characteristics of a band-stop filter with an L and C series structure. The reflection coefficient (S11) and transmission coefficient (S21) of the FSS designed based on theory are compared using a CST studio suite and Keysight’s Advanced Design System. In addition, the transmission coefficients are verified through actual measurements, wherein the measured transmission coefficient is −0.1 dB at 3.0 GHz and approximately −50 dB at the harmonic frequency of 6.0 GHz. The designed FSS is attached to an actual radar system, and the 2D radiation pattern and maximum gain are measured during steering in boresight, azimuth (30∘) and elevation (30∘) directions. At 3.0 GHz, the maximum gain in boresight is 17.25 dB without the FSS and 17.12 dB with the FSS. At 6.0 GHz, the maximum gain is 12.79 dB without the FSS and 2.69 dB with the FSS. At 3.0 GHz, the maximum gain during azimuth steering is 16.13 dB without the FSS and 16.68 dB with the FSS, and the maximum gain during elevation steering is 15.74 dB without the FSS and 15.90 dB with the FSS.

Combination of 3D printing and knitting: the loop stitch design and its effects on tensile strength and elongation at break

ABSTRACT This study explored the creation of 3D printing knit-based structures. Furthermore, the study investigated the relationship between the mechanical performance of these structures and the changes in loop stitch design and 3D printing material. Effects on the mechanical properties of the 3D printing loop structure were evaluated with respect to loop stitch, loop head, loop leg and loop foot. This study analysed the mean differences between the values of independent variables, namely, loop stitch and print material and the values of two dependent variables (i.e. tensile strength and elongation at break) using a multivariate analysis of variance. The results revealed that the mechanical properties of the loop structure vary significantly depending on the loop stitch shape and printing material. Tensile strength is mainly affected by material, whereas elongation property is significantly affected by loop stitch shape.

Stretching multistate flexible chains and loops.

Polymer loop structure commonly appears in biological phenomena, such as DNA looping and DNA denaturation. When a chain forms a loop, its elastic behavior differs from that of an open chain due to the loss of entropy. In the case of reversible loop formation, interesting behavior emerges related to the multistate nature of the conformations. In this study, we model a multistate reversible loop as a looping Gaussian chain, which can bind (close) reversibly at one or several points to form a loop, or a zipping Gaussian loop, which can zip reversibly to form a double-stranded chain. For each model, we calculate the force-extension relations in the fixed-extension (Helmholtz) and the fixed-force (Gibbs) statistical ensembles. Unlike the single Gaussian chain or loop, the multilevel systems demonstrate qualitatively distinct tensile elasticity and ensemble inequivalence. In addition, we investigate a Gaussian necklace consisting of reversible alternating blocks of the zipped chain and loop and obtain the force-temperature phase diagram. The phase diagram implies a force-induced phase transition from a completely looped (denatured) state to a mixed (bound) state.

Accurately Modeling RNA Stem-Loops in an Implicit Solvent Environment.

Ribonucleic acid (RNA) molecules can adopt a variety of secondary and tertiary structures in solution, with stem-loops being one of the more common motifs. Here, we present a systematic analysis of 15 RNA stem-loop sequences simulated with molecular dynamics simulations in an implicit solvent environment. Analysis of RNA cluster ensembles showed that the stem-loop structures can generally adopt the A-form RNA in the stem region. Loop structures are more sensitive, and experimental structures could only be reproduced with modification of CH···O interactions in the force field, combined with an implicit solvent nonpolar correction to better model base stacking interactions. Accurately modeling RNA with current atomistic physics-based models remains challenging, but the RNA systems studied herein may provide a useful benchmark set for testing other RNA modeling methods in the future.

Full-Dimensional Proportional-Derivative Control Technique for Turing Pattern and Bifurcation of Delayed Reaction-Diffusion Bidirectional Ring Neural Networks

Abstract In neural networks, the states of neural networks often exhibit significant spatio-temporal heterogeneity due to the diffusion effect of electrons and differences in the concentration of neurotransmitters. One of the macroscopic reflections of this time-spatial inhomogeneity is Turing pattern. However, most current research in reaction-diffusion neural networks has focused only on one-dimensional location information, and the remaining results considering two-dimensional location information are still limited to the case of two neurons. In this paper, we conduct the dynamic analysis and optimal control of a delayed reaction-diffusion neural network model with bidirectional loop structure. First, several mathematical descriptions are given for the proposed neural network model and the full-dimensional partial differential proportional-derivative (PD) controller is introduced. Second, by analyzing the characteristic equation, the conditions for Hopf bifurcation and Turing instability of the controlled network model are obtained. Furthermore, the amplitude equation of the controlled neural network is obtained based on the multiscale analysis method. Subsequently, we determine the key parameters affecting the formation of Turing pattern depending on the amplitude equation. Finally, multiple sets of computer simulations are carried out to support our theoretical results. It is found that the diffusion coefficients and time delays have significant effects on spatio-temporal dynamics of neural networks. Moreover, after reasonable parameter proportioning, the full-dimensional PD control method can alleviate the spatial heterogeneity caused by diffusion projects and time delays.

Meso-macro deformation investigation for biaxial knitted textile based pouch motor

A detailed knowledge of the deformation behavior of pouch motor contracting actuators (PMCA) improves their performance, and researchers are always looking for materials and structures that can increase the actuator’s performance by better controlling these deformations. In this article, a novel PMCA is fabricated using three-dimensional (3D) knitting technology, and its constitutive material is spectra as a non-stretch yarn. However, the final fabric (loop structure) is stretchable in the course direction and non-stretchable in the wale direction (anisotropic structure). The advantage of using this technique is that the contraction in the transverse direction that existed in the previous actuators becomes the expansion improving the actuator performance by about 21% compared to prior counterparts. The available commercial software Abaqus2021 is used for simulation, and its results are verified with the experimental results. A comparative investigation between the presented PMCA and the previous silicone-based counterpart is performed. A meso-macro deformation study is developed experimentally and numerically to justify the improvement of the presented PMCA.