Flexibility Gradient Research Articles

Skin-Inspired, Highly Sensitive, Broad-Range-Response and Ultra-Strong Gradient Ionogels Prepared by Electron Beam Irradiation.

Skin, characterized by its distinctive gradient structure and interwoven fibers, possesses remarkable mechanical properties and highly sensitive attributes, enabling it to detect an extensive range of stimuli. Inspired by these inherent qualities, a pioneering approach involving the crosslinking of macromolecules through in situ electron beam irradiation (EBI) is proposed to fabricate gradient ionogels. Such a design offers remarkable mechanical properties, including excellent tensile properties (>1000%), exceptional toughness (100MJ m-3), fatigue resistance, a broad temperature range (-65-200°C), and a distinctive gradient modulus change. Moreover, the ionogel sensor exhibits an ultra-fast response time (60ms) comparable to skin, an incredibly low detection limit (1kPa), and an exceptionally wide detection range (1kPa-1MPa). The exceptional gradient ionogel material holds tremendous promise for applications in the field of smart sensors, presenting a distinct strategy for fabricating flexible gradient materials.

The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition.

Long-tail distribution is widely spread in real-world applications. Due to the extremely small ratio of instances, tail categories often show inferior accuracy. In this paper, we find such performance bottleneck is mainly caused by the imbalanced gradients, which can be categorized into two parts: (1) positive part, deriving from the samples of the same category, and (2) negative part, contributed by other categories. Based on comprehensive experiments, it is also observed that the gradient ratio of accumulated positives to negatives is a good indicator to measure how balanced a category is trained. Inspired by this, we come up with a gradient-driven training mechanism to tackle the long-tail problem: re-balancing the positive/negative gradients dynamically according to current accumulative gradients, with a unified goal of achieving balance gradient ratios. Taking advantage of the simple and flexible gradient mechanism, we introduce a new family of gradient-driven loss functions, namely equalization losses. We conduct extensive experiments on a wide spectrum of visual tasks, including two-stage/single-stage long-tailed object detection (LVIS), long-tailed image classification (ImageNet-LT, Places-LT, iNaturalist), and long-tailed semantic segmentation (ADE20K). Our method consistently outperforms the baseline models, demonstrating the effectiveness and generalization ability of the proposed equalization losses. Code will be released at https://github.com/ModelTC/United-Perception.

A flexible gradient lateral flow immunochromatographic assay for qualitative, semi-quantitative, and quantitative determination of serum amyloid A

Serum amyloid A (SAA) is an acute-phase protein produced in response to inflammatory proteins during infections, inflammation, trauma, surgery, cancer, and other conditions. Early and accurate detection of SAA is necessary for diagnosis and monitoring of disease progression. To meet this need, we developed a gradient lateral flow immunoassay test strip using Au nanoparticles as signal reporters. The test strip has three test (T1, T2, and T3) lines with progressively decreasing concentrations of SAA antibody, enabling the determination of high, medium, and low concentrations of SAA in serum. The test strip results were analyzed using three distinct readout methods, each with different sensitivity, accuracy, and precision for SAA concentration measurements. Qualitative judgment is based on the color of the T1 line. Semi-quantitative assessment of SAA concentration is determined by the number of colored T-lines. Specifically, color development in T1 line alone indicates a concentration range of 10–50 μg/mL, while T1 and T2 lines together indicate a range of 50–100 μg/mL, and development in all three lines (T1, T2, and T3) indicates a concentration of >100 μg/mL. Quantitative analysis was performed using either smartphone imaging or image scanning with ImageJ software. By using a five-parameter logistic function, we found a strong correlation (R2 = 0.998) between the ratio of signal intensities of (T1 + T2 + T3) to the control (C) line and SAA concentrations ranging from 5 to 1000 μg/mL. At lower concentrations (0–100 μg/mL), we observed a proportional relationship between the value of (T1 + T2 + T3)/C and SAA concentration. The limit of detection for SAA was 9.33 ng/mL (or 6.53 μg/mL of SAA in undiluted serum samples) for the smartphone method and 3.06 ng/mL (or 2.14 μg/mL of SAA in undiluted serum samples) for the scanner method. The gradient test strip was highly consistent with a commercially available SAA immunochromatographic test strip when tested with real human serum samples. Passing-Bablok regression indicated that results obtained using the smartphone app and scanner methods of the gradient test strip were comparable to those obtained using the commercial test strip. The gradient test strip is flexible and adaptable, providing solutions for qualitative, semi-quantitative, and quantitative SAA measurements.

A High‐Capacity Gradient Zn Powder Anode for Flexible Zn‐Ion Batteries

AbstractZn powder‐based anodes are promising for flexible Zn‐ion batteries with large‐scale production, but the drawbacks such as dendrite growth and side reactions strictly hinder their wide application. Herein, a free‐standing Zn powder‐based anode with gradient particle size and porosity is facilely constructed for flexible Zn‐ion batteries. The gradient design not only optimizes the electric field distribution and the Zn‐ion flux but also induces ideal bottom‐up deposition and top‐down stripping behaviors of Zn, thus suppressing dendrite growth. As a result, the flexible gradient Zn powder anode can be stably cycled for 1250 h at 1 mA cm−2/1 mAh cm−2, and even at high current/capacity of 5 mA cm−2/5 mAh cm−2, it still achieves a long lifespan of 130 h, which outperforms its non‐gradient counterparts and most previous results from Zn powder‐based anodes. The gradient strategy is expected to inspire the extensive utilization of Zn powder‐based anodes for flexible energy storage devices.

Brownian ratchet for directional nanoparticle transport by repetitive stretch-relaxation of DNA.

Brownian motion subject to a periodic and asymmetric potential can be biased by external, nonequilibrium fluctuations, leading to directional movement of Brownian particles. Sequence-dependent flexibility variation along double-stranded DNA has been proposed as a tool to develop periodic and asymmetric potentials for DNA binding of cationic nanoparticles with sizes below tens of nanometers. Here, we propose that repetitive stretching and relaxation of a long, double-stranded DNA molecule with periodic flexibility gradient can induce nonequilibrium fluctuations that tune the amplitude of asymmetric potentials for DNA-nanoparticle binding to result in directional transport of nanometer-sized particles along DNA. Realization of the proposed Brownian ratchet was proven by Brownian dynamics simulations of coarse-grained models of a single, long DNA molecule with flexibility variation and a cationic nanoparticle.

Biomimetic Water-Repelling Surfaces with Robustly Flexible Structures.

Biomimetic liquid-repelling surfaces have been the subject of considerable scientific research and technological application. To design such surfaces, a flexibility-based oscillation strategy has been shown to resolve the problem of liquid-surface positioning encountered by the previous, rigidity-based asymmetry strategy; however, its usage is limited by weak mechanical robustness and confined repellency enhancement. Here, we design a flexible surface comprising mesoscale heads and microscale spring sets, in analogy to the mushroomlike geometry discovered on springtail cuticles, and then realize this through three-dimensional projection microstereolithography. Such a surface exhibits strong mechanical robustness against ubiquitous normal and shear compression and even endures tribological friction. Simultaneously, the surface elevates water repellency for impacting droplets by enhancing impalement resistance and reducing contact time, partially reaching an improvement of ∼80% via structural tilting movements. This is the first demonstration of flexible interfacial structures to robustly endure tribological friction as well as to promote water repellency, approaching real-world applications of water repelling. Also, a flexibility gradient is created on the surface to directionally manipulate droplets, paving the way for droplet transport.

Flexible Gradient Poly(ether-ester) from the Copolymerization of Epoxides and ε-Caprolactone Mediated by a Hetero-bimetallic Complex

Flexible Gradient Poly(ether-ester) from the Copolymerization of Epoxides and ε-Caprolactone Mediated by a Hetero-bimetallic Complex

A flexible gradient method for unstructured‐grid solvers

AbstractIn this short note, we introduce a flexible explicit gradient method for efficient and robust unstructured‐grid computations. As shown, the method serves as a memory‐efficient alternative to the multiple least‐squares gradients needed by conflicting requirements for inviscid, viscous, and turbulence‐model source terms (e.g., vorticity), in practical computational‐fluid‐dynamics solvers. The flexible method computes the gradient of a numerical solution in a cell by averaging the face gradients defined by the alpha‐damped formula with a parameter based on a single set of least‐squares gradients. We show that a small gives less‐accurate gradients similar to unweighted least‐squares gradients, and a large produces accurate gradients similar to weighted least‐squares gradients. Therefore, it allows us to obtain two types of gradients by changing the value of without storing more than one set of least‐squares coefficients. Moreover, the method yields fourth‐order accurate gradients with on a regular grid. The method is demonstrated for two‐dimensional inviscid and viscous flows on unstructured grids.

A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk

To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptive P-value thresholding (AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS association P values play the role of the primary data for AdaPT; single-nucleotide polymorphisms (SNPs) are selected because they are gene expression quantitative trait loci (eQTLs). This natural pairing of SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from genetically correlated bipolar disorder, the effect size of SNP genotypes on gene expression, and gene-gene coexpression, captured by subnetwork (module) membership. In all, 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations using gene expression information from the developing human prefrontal cortex. We interpret these results in light of recent theories about the polygenic nature of SCZ. Importantly, our entire process for identifying enrichment and creating features with independent complementary data sources can be implemented in many different high-throughput settings to ultimately improve power.

Discriminative and Uncorrelated Feature Selection With Constrained Spectral Analysis in Unsupervised Learning.

The existing unsupervised feature extraction methods frequently explore low-redundant features by an uncorrelated constraint. However, the constrained models might incur trivial solutions, due to the singularity of scatter matrix triggered by high-dimensional data. In this paper, we propose a regularized regression model with a generalized uncorrelated constraint for feature selection, which leads to three merits: 1) exploring the low-redundant and discriminative features; 2) avoiding the trivial solutions and 3) simplifying the optimization. Besides that, the local cluster structure is achieved via a novel constrained spectral analysis for the unsupervised learning, where Must-Links and Cannot-Links are transformed into a intrinsic graph and a penalty graph respectively, rather than incorporated into a mixed affinity graph. Accordingly, a discriminative and uncorrelated feature selection with constrained spectral analysis (DUCFS) is proposed with adopting σ -norm regularization for interpolating between F-norm and l2,1 -norm. Due to the flexible gradient and global differentiability, our model converges fast. Extensive experiments on benchmark datasets among several state-of-the-art approaches verify the effectiveness of the proposed method.

Preparation and characterization of SEBS-g-MAH-filled flexible polyurethane foam composites with gradient-changing structure

This study constructs a flexible gradient foaming structure by changes the maleic anhydride-grafted styrene-ethylene/butylene-styrene (SEBS-g-MAH) content on the basis of a two-step foaming technique. The effects of SEBS-g-MAH content and its ratio in the first and second foaming steps on microstructure, compression, quasi-static burst, dynamic cushion, and thermal performance of flexible polyurethane foam composites are investigated. Results show that, SEBS-g-MAH content affects microstructure, compressive stress, burst strength, dynamic cushion and thermal performance. Gradient foaming improves compressive stress and quasi-static bursting strength by 3.81 and 1.56 times, respectively. The absorbed energy under dynamic impact of gradient foaming can be increased by 87% relative to that in one-step foaming. This study provides a new way approach to SEBS application, and serves as a new reference for designing of energy-absorbing foam composites by filler addition.

Analysis of natural frequency for bioinspired functional gradient plates

Biological materials-fish scales exhibit ultra-flexibility due to its functionally graded materials. Inspired by the hierarchical gradient structure of fish scales, a new flexible gradient model that can adequately describe the characteristics of the bioinspired hierarchical structures is proposed in this work. To assess the flexibility of the proposed gradient model, a combination of extended finite element method (XFEM) and stabilized discrete shear gap (DSG) is established to analyze the vibration of bioinspired gradient plates with/without cracks. The DSG technique is employed to eliminate the shear locking phenomenon, while the XFEM is used for a mesh-independent modelling of crack. The combined approach is applicable to both moderately thick and thin plates, and is insensitive to mesh distortion. Functionally gradient plates take two types: power law function (Type I) and bioinspired hierarchical mode (Type II). For Type I, the natural frequencies decrease by increasing the gradient factor, i.e., the exponent of the power law. When the gradient factor is larger than one, the improvement of the plate stiffness by material gradient is restricted. For Type II, the natural frequencies are mostly independent of the step smoothing factor, yet quite sensitive to the number of step layers, providing an additional degree of freedom in tailoring the material properties. In addition, the natural frequencies of the bioinspired gradient plate are lower than that of the homogeneous ceramic plate. By using Type II, the stiffness of the plate can be reduced effectively, making the plate prone to deformation, which coincides with the flexible scale design. Therefore, the present study provides an incisive method and instructive guideline for a new era of artificially designed flexible materials inspired by natural (or biological) materials and structures.

Based on vector flexible weighted morphological image edge detection

The classic gray-scale morphological operator has been widely used in image filtering, edge detection, image enhancement and image segmentation, etc. Morphology theory in color image processing was still in the stage of exploration in the field of application, the application of mathematical morphology in image processing is good or not is the key to the morphological gradient operator and structural elements. In view of the two key factors the vector flexible weighted morphological algorithm is put forward to detect image edge. Firstly build the weighted structural elements, if the pixel is the edge of the image to give greater weighted value, otherwise give small weighted value. Secondly reference gray-scale morphological duality theorem, the vector flexible gradient operator meets the duality theorem of color image. Vector flexible gradient operator is proposed to apply weighted structural elements to corrosion and dilation gradient operation to extract image edge. The experimental results show that the proposed vector flexible weighted morphological operators edge detection algorithm can effectively extract image edges, keep small image edge and can remove the image noise, verify the effectiveness of the proposed algorithm.

Femtoliter Gradient Elution System for Liquid Chromatography Utilizing Extended Nanofluidics.

A gradient system was developed for the separation of proteins on a femtoliter scale utilizing nanofluidic channels. In the history of chromatography, miniaturization of the separation column has been important for efficient separation and downsizing of instruments. Previously, our group developed a small and highly efficient chromatography system utilizing nanofluidic channels, although a flexible design of the gradient was difficult and separation of proteins was not achieved. Here, we propose a flexible gradient system using standard HPLC pumps and an auxiliary mixer with a simple sample injection system. In contrast to our previous sample injection system using pressure balance, the system enables a femtoliter-scale sample injection which is compatible with gradient elution using HPLC pumps. The system was carefully designed, verified for sample injection and gradient elution, and finally applied to the separation of proteins from model and real samples. This femtoliter-scale, efficient separation system will contribute to omics studies at the single-cell level.

3D printing and intraoperative neuronavigation tailoring for skull base reconstruction after extended endoscopic endonasal surgery: proof of concept.

Endoscopic endonasal approaches are increasingly performed for the surgical treatment of multiple skull base pathologies. Preventing postoperative CSF leaks remains a major challenge, particularly in extended approaches. In this study, the authors assessed the potential use of modern multimaterial 3D printing and neuronavigation to help model these extended defects and develop specifically tailored prostheses for reconstructive purposes. Extended endoscopic endonasal skull base approaches were performed on 3 human cadaveric heads. Pre-Preprocedure and intraprocedure CT scans were completed and were used to segment and design extended and tailored skull base models. Multimaterial models with different core/edge interfaces were 3D printed for implantation trials. A novel application of the intraoperative landmark acquisition method was used to transfer the navigation, helping to tailor the extended models. Prostheses were created based on preoperative and intraoperative CT scans. The navigation transfer offered sufficiently accurate data to tailor the preprinted extended skull base defect prostheses. Successful implantation of the skull base prostheses was achieved in all specimens. The progressive flexibility gradient of the models’ edges offered the best compromise for easy intranasal maneuverability, anchoring, and structural stability. Prostheses printed based on intraprocedure CT scans were accurate in shape but slightly undersized. Preoperative 3D printing of patient-specific skull base models is achievable for extended endoscopic endonasal surgery. The careful spatial modeling and the use of a flexibility gradient in the design helped achieve the most stable reconstruction. Neuronavigation can help tailor preprinted prostheses.

Directional rolling of positively charged nanoparticles along a flexibility gradient on long DNA molecules.

Directing the motion of molecules/colloids in any specific direction is of great interest in many applications of chemistry, physics, and biological sciences, where regulated positioning or transportation of materials is highly desired. Using Brownian dynamics simulations of coarse-grained models of a long, double-stranded DNA molecule and positively charged nanoparticles, we observed that the motion of a single nanoparticle bound to and wrapped by the DNA molecule can be directed along a gradient of DNA local flexibility. The flexibility gradient is constructed along a 0.8 kilobase-pair DNA molecule such that local persistence length decreases gradually from 50 nm to 40 nm, mimicking a gradual change in sequence-dependent flexibility. Nanoparticles roll over a long DNA molecule from less flexible regions towards more flexible ones as a result of the decreasing energetic cost of DNA bending and wrapping. In addition, the rolling becomes slightly accelerated as the positive charge of nanoparticles decreases due to a lower free energy barrier of DNA detachment from charged nanoparticle for processive rolling. This study suggests that the variation in DNA local flexibility can be utilized in constructing and manipulating supramolecular assemblies of DNA molecules and nanoparticles in structural DNA nanotechnology.

Traction reinforcement in prehensile feet of harvestmen (Arachnida, Opiliones).

Prehensile and gripping organs are recurring structures in different organisms that enhance friction by the reinforcement and redirection of normal forces. The relationship between organ structure and biomechanical performance is poorly understood, despite a broad relevance for microhabitat choice, movement ecology and biomimetics. Here, we present the first study of the biomechanics of prehensile feet in long-legged harvestmen. These arachnids exhibit the strongest sub-division of legs among arthropods, permitting extreme hyperflexion (i.e. curling up the foot tip). We found that despite the lack of adhesive foot pads, these moderately sized arthropods are able to scale vertical smooth surfaces, if the surface is curved. Comparison of three species of harvestmen differing in leg morphology shows that traction reinforcement by foot wrapping depends on the degree of leg sub-division, not leg length. Differences are explained by adaptation to different microhabitats on trees. The exponential increase of foot section length from distal to proximal introduces a gradient of flexibility that permits adaptation to a wide range of surface curvature while maintaining integrity at strong flexion. A pulley system of the claw depressor tendon ensures the controlled flexion of the high number of adesmatic joints in the harvestman foot. These results contribute to the general understanding of foot function in arthropods and showcase an interesting model for the biomimetic engineering of novel transportation systems and surgical probes.

Thermal stress analysis on 2D C/SiC composites of thin-walled structure of anti-heat head cone

The thermal protection performance of C/SiC composites can be improved by coating the thermal barrier coating on the surface of C/SiC composites.The wall heat flux density distribution of the 2D C/SiC composite thermal insulation structure at the front end of the hyper-sonic vehicle was calculated by steady state heat transfer at different Mach numbers in the atmosphere. In the thermal stress calculation, an effective finite element numerical model is established by the method of partitioning and based on the flexible gradient density method. The model was treated as heterogeneous materials which consists of C/SiC composite region, the coating zone and transition zone, and the results show that the maximum thermal stress appears near the end of the head cone.

Alpha-synuclein and familial variants affect the chain order and the thermotropic phase behavior of anionic lipid vesicles

Alpha-synuclein (aSN) is a presynaptic protein with a pathological role in Parkinson's disease (PD). The mutants A30P, E46K and A53T are involved in PD early-onset forms. aSN is natively unfolded but can self-assemble to oligomers and fibrils and binds anionic membranes in a helical conformation. We study the influence of wild-type (wt) aSN and familial variants on the chain order and thermotropic phase behavior of anionic dimyristoylphosphatidylglycerol (DMPG) bilayers by using electron spin resonance and calorimetry, respectively. The alpha-helical conformation of the proteins in the membrane-bound state is assessed by circular dichroism thermal scans. wt and mutated aSN upon binding to fluid DMPG vesicles progressively increase chain order. Lipid:protein molar binding stoichiometries correspond to 50 for A30P, 35–36 for aSN and A53T, 30 for E46K. The temperature range over which the variants assume the α-helical fold correlates directly with the density of proteins on vesicle surfaces. All variants preserve the characteristic chain flexibility gradient and impart motional restriction in the lipid chain. This is evident at the first CH2 segments and is markedly reduced at the chain termini, disappearing completely for A30P. The proteins slightly reduce DMPG main transition temperature, revealing preferential affinity for the fluid phase, and broaden the transition, promoting gel-fluid phase coexistence. The overall results are consistent with protein surface association in which the degree of binding correlates with the degree of folding and perturbation of the membrane bilayer. However, the degree of binding of monomer to membrane does not correlate directly with aSN toxicity in vivo.

Structural analysis of nanosystems: Solid Sorbitan esters Nanoparticles (SSN) as a case study

Innovative approaches in nanotechnology can provide drug delivery systems with a high potential in different fields. To avoid trial and error assays as a main driving force governing new designs and, furthermore, to develop successful nanosystem optimization strategies, it is of the greatest importance to develop specific characterisation techniques beyond conventional determinations of size, zeta potential and morphology. However, the application of techniques able to determine some key characteristics, such as nanostructure (i.e., solid structure vs vesicular), and the way in which the reorganization of components takes place on these structures has been scarcely explored. The present work has been devoted to provide some insights about the potential offered by some NMR techniques to those scientists working on nanotechnological approaches. For this purpose, we selected our nanosystems based on sorbitan monooleate as a case study. We used 1H NMR methods, including a recently proposed method relying in the well-known Saturation Transfer Difference (STD) experiment for the observation of ‘invisible signals’ in large aggregates (Invisible State STD or ISSTD). Overall, these techniques revealed the presence in these nanosystems of a gradient of flexibility from an internal rigid core towards a more flexible region located on their surface, as well as the absence of water content in both regions. Such structure, corresponding to a solid nanostructure rather than a vesicular one, can explain some of the interesting properties previously observed for these innovative nanosystems, such as their high stability, and allows us to refer to these nanosystems with the term “Solid Sorbitan esters Nanoparticles” (SSN). On the basis of the valuable information provided by the mentioned characterisation techniques, it is our understanding that they could facilitate the future design of new drug delivery nanosystems as well as the improvement of existing ones and/or the development of new applications for classical drug delivery concepts.