Step Gradient Research Articles

Optimizing lane reversals in transportation networks to reduce traffic congestion: A global optimization approach

This paper studies how to reduce the overall travel time of commuters in a transportation network by reversing the direction of some lanes in the network using a macroscopic network-wide perspective. Similar to the Network Design Problem, the lane reversal problem has been shown to be NP-hard given the dependence of the users’ route selection on the lane direction decision. Herein, we propose and compare three efficient methods to solve the routing and lane reversal problem jointly. First, we introduce an alternating method that decouples the routing and lane assignment problems. Second, we propose a Frank–Wolfe method that jointly takes gradient steps to adjust both the lane assignment and routing decisions. Third, we propose a convex approximation method that uses a threshold-based approach to convexify the joint routing and lane reversal objective. The convex approximation method is advantageous since it finds a global optimum solution for the approximated problem and it enables the possibility to include linear constraints. Using this method, we extend the main formulation to be able to limit a maximum number of reversed lanes, as well as to incorporate multiple origin–destination (OD) patterns. We test the proposed methods in a case study using the transportation network of Eastern Massachusetts where our results indicate an overall reduction in travel times of 4.7% by selecting the best 15 reversals. Moreover, using a small test network, we investigate the performance of the lane reversal strategies as a function of the OD demand symmetry. As expected, we observe that when the OD demand is very asymmetric (e.g., for a single OD pair, evacuations, large events), the reduction in travel times is larger than the symmetric case, reaching travel time reductions of 60%.

Newton acceleration on manifolds identified by proximal gradient methods

Proximal methods are known to identify the underlying substructure of nonsmooth optimization problems. Even more, in many interesting situations, the output of a proximity operator comes with its structure at no additional cost, and convergence is improved once it matches the structure of a minimizer. However, it is impossible in general to know whether the current structure is final or not; such highly valuable information has to be exploited adaptively. To do so, we place ourselves in the case where a proximal gradient method can identify manifolds of differentiability of the nonsmooth objective. Leveraging this manifold identification, we show that Riemannian Newton-like methods can be intertwined with the proximal gradient steps to drastically boost the convergence. We prove the superlinear convergence of the algorithm when solving some nondegenerated nonsmooth nonconvex optimization problems. We provide numerical illustrations on optimization problems regularized by \(\ell _1\)-norm or trace-norm.

Modulating mechanical performances of metallic amorphous materials through phase gradient

Increasing strength is usually at the cost of sacrificing ductility in structural materials. The tradeoff becomes even conspicuous in a category of metallic amorphous materials, the so-called metallic glasses (MGs) featured without any atomic-scale translational symmetry. Therefore, there remains little room for simultaneous optimization of strength and ductility in MGs through tailoring the morphology and kinetics of structural imperfections. Here we propose an alternative strategy for modulating the mechanical properties of MGs through introducing proper content of compositional or phase gradient inspired by the mechanistic strain gradient theory. We design two types of CuZr-based phase gradient metallic glasses (PGMGs) with different compositional concentration gradient directions in either continuous or stepped gradient form. Extensive molecular dynamics simulations demonstrate that phase gradient raises the concentration of mechanically stable icosahedral and icosahedron-like Voronoi polyhedra and, thus, increases the strength of MGs. In terms of plastic deformation, free volume mismatch between phases invalidates the autocatalytic activation mechanism of shear transformation zones, resulting in greater resistance to shear band propagation. The phase gradient also encourages branches of shear band and nucleation of multiple shear bands, which mechanism delocalizes deformation and postpones failure. These mechanisms lead to an improvement in the overall ductility of the PGMGs. The present strategy sheds light on evading the long-standing strength-ductility tradeoff in amorphous metals through extrinsic chemical and geometrical modulation that can be handled by appropriate thermal processing and fabrication technique.

A rapid and quantitative reversed-phase HPLC-DAD/ELSD method for lipids involved in nanoparticle formulations

Lipid nanoparticles (LNPs) have shown great success as drug delivery systems, especially for mRNA vaccines, as those developed during the Covid-19 pandemics. Lipid analysis is critical to monitor the formulation process and control the quality of LNPs. The present study is focused on the development and validation of a high-performance liquid chromatography – diode array detector –evaporative light scattering detector (HPLC-DAD/ELSD) based method for the simultaneous quantification of 7 lipids, illustrating the main components of LNPs: ionizable lipids, the neutral co-lipid cholesterol, phospholipids, hydrophilic polymer-lipids for colloidal stability (e.g., a PEGylated lipid). In particular, this study focuses on two innovative synthetic lipids: a switchable cationic lipid (CSL3) which has demonstrated in vitro and in vivo siRNA transfection abilities, and the palmitic acid-grafted-poly(ethyloxazoline)5000 (PolyEtOx), used as an alternative polymer to address allergic reactions attributed to PEGylated lipids. The HPLC separation was achieved on a Poroshell C18 column at 50 °C using a step gradient of a mobile phase composed of water/methanol mixtures with 0.1% (v/v) trifluoroacetic acid (TFA). This method was validated following ICH Q2(R1) & (R2) guidelines in terms of linearity (R² ≥ 0.997), precision (relative standard deviation on peak areas < 5% for intermediate repeatability), accuracy (recoveries between 92.9% and 108.5%), and sensitivity. Indeed, low detection and quantitation limits were determined (between 0.02 and 0.04 µg and between 0.04 and 0.10 µg, respectively). Due to its high selectivity, this method allowed the analysis of lipid degradation products produced through degradation studies in basic, acidic, and oxidative conditions. Moreover, the method was successfully applied to the analysis of several liposome formulations at two key steps of the development process. Consequently, the reported HPLC method offers fast, versatile, selective and quantitative analysis of lipids, essential for development optimization, chemical characterization, and stability testing of LNP formulations.

Separation and purification of highly infectious enterovirus A71 particles using a strong anion-exchange column

Virions produced from cell culture is the primary source for production of formalin-inactivated whole virus vaccines for enteroviruses. EV-A71 particles produced from culture system comprise two major types, the immature/empty (E)-particle and the mature/full (F)-particle, which both exhibit low isoelectric point (pI) values but have distinct differences in infectivity and immunogenicity. Although EV-A71 particles can conventionally be separated into E-particle and F-particle using sucrose gradient ultracentrifugation, this procedure is cumbersome and difficult to put into practice for vaccine production. Methods based on ion-exchange chromatography have been exploited to improve the purification efficacy; however, none of them are capable of separating the E- and F-particles efficiently. In this study, we aimed to develop an approach to isolate and purify the highly immunogenic mature EV-A71 particles. By applying a step gradient elution procedure, we successfully isolated the viral structure protein VP0-cleaved particles of EV-A71 from a mixture of cultured viral solution using the Q-membrane anion-exchange chromatography. The elution started with 0.1x phosphate buffered saline (PBS) solution while increasing the percentage of 1x PBS containing 1M NaCl in sequential steps. By this procedure, the VP0-cleaved mature particles and VP0-uncleaved immature particles of EV-A71 could be separated into different fractions in Q-membrane with gradually increased NaCl concentration in elution buffer. The purified VP0-cleaved particles were shown to have characteristics equivalent to those of the highly infectious F-particles of EV-A71. The overall recovery rate for the mature EV-A71 particles by Q-membrane is 56% and its purity was shown to be equivalent to those isolated by the sucrose gradient ultracentrifugation. Our approach provides a simple and efficient purification method for recovering mature, highly infectious virus particles from the EV-A71 culture bulk.

Isolation of mitochondria-derived mitovesicles and subpopulations of microvesicles and exosomes from brain tissues.

Extracellular vesicles (EVs) are nanoscale vesicles secreted into the extracellular space by all cell types, including neurons and astrocytes in the brain. EVs play pivotal roles in physiological and pathophysiological processes such as waste removal, cell-to-cell communication and transport of either protective or pathogenic material into the extracellular space. Here we describe a detailed protocol for the reliable and consistent isolation of EVs from both murine and human brains, intended for anyone with basic laboratory experience and performed in a total time of 27 h. The method includes a mild extracellular matrix digestion of the brain tissue, a series of filtration and centrifugation steps to purify EVs and an iodixanol-based high-resolution density step gradient that fractionates different EV populations, including mitovesicles, a newly identified type of EV of mitochondrial origin. We also report detailed downstream protocols for the characterization and analysis of brain EV preparations using nanotrack analysis, electron microscopy and western blotting, as well as for measuring mitovesicular ATP kinetics. Furthermore, we compared this novel iodixanol-based high-resolution density step gradient to the previously described sucrose-based gradient. Although the yield of total EVs recovered was similar, the iodixanol-based gradient better separated distinct EV species as compared with the sucrose-based gradient, including subpopulations of microvesicles, exosomes and mitovesicles. This technique allows quantitative, highly reproducible analyses of brain EV subtypes under normal physiological processes and pathological brain conditions, including neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease.

Evaluation of lentiviral vector stability and development of ion exchange purification processes.

In this manuscript, we employ parallel batch stability and chromatographic screens in concert with linear and step gradient experiments to develop a high yield, HCP clearance anion exchange capture process for lentiviral vector (LVV) purification. An initial broad resin screen is carried out to determine anion exchange-based resins that exhibit high recovery of LVV. LVV stability is then evaluated and conditions are established where the vector exhibits good stability, namely phosphate buffer at pH 6.5-7.5, with low to moderate salt concentrations. A subsequent high-throughput batch screen is then carried out with a subset of resins selected from the first screen under stable conditions to identify optimal wash and elution steps to further improve product yield and protein clearance. Linear gradient experiments are also conducted in mini-column format to refine the operating conditions and final step gradient processes are established that exhibit greater than 70% yield of infectious LVV while also achieving up to 2.89 log reduction values (LRV) of HCPs during the process. The large set of stability and chromatographic data provided in this work represent an important contribution to knowledge in the field about the chromatographic efficacy of a wide range of resins for LVV bioprocessing under stable conditions.

High-precision Multichannel Solar Image Registration Using Image Intensity

Solar images observed in different channels with different instruments are crucial to the study of solar activity. However, the images have different fields of view, causing them to be misaligned. It is essential to accurately register the images for studying solar activity from multiple perspectives. Image registration is described as an optimizing problem from an image to be registered to a reference image. In this paper, we proposed a novel coarse-to-fine solar image registration method to register the multichannel solar images. In the coarse registration step, we used the regular step gradient descent algorithm as an optimizer to maximize the normalized cross correlation metric. The fine registration step uses the Powell–Brent algorithms as an optimizer and brings the Mattes mutual information similarity metric to the minimum. We selected five pairs of images with different resolutions, rotation angles, and shifts to compare and evaluate our results to those obtained by scale-invariant feature transform and phase correlation. The images are observed by the 1.6 m Goode Solar Telescope at Big Bear Solar Observatory and the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Furthermore, we used the mutual information and registration time criteria to quantify the registration results. The results prove that the proposed method not only reaches better registration precision but also has better robustness. Meanwhile, we want to highlight that the method can also work well for the time-series solar image registration.

Continuous remeshing for inverse rendering

AbstractWe present a novel method for joint optimization and remeshing and apply it to inverse rendering. Rapid advances in differentiable rendering during the last years paved the way for fast inverse rendering of complex scenes. But serious problems with gradient‐based optimization of triangle meshes remain. Applying gradient steps to the vertices can lead to mesh defects, such as flipped triangles, crumpled regions, and self‐intersections. Choosing a good vertex count is crucial for the optimization quality and performance but is usually done by hand. Moreover, meshes with fixed triangulation struggle to adapt to complex geometry. Our novel method tackles all these problems by applying an adaptive remeshing step in each single iteration of the optimization loop. By immediately collapsing suspicious triangles, we avoid and heal mesh defects. We use a closed‐loop‐controlled location‐dependent edge length. We compare our solution to state‐of‐the‐art methods and find that it is faster and more accurate. It produces finer meshes with fewer defects, requires less parameter tuning and can reconstruct more complex objects.

Topology Optimisation under Uncertainties with Neural Networks

Topology optimisation is a mathematical approach relevant to different engineering problems where the distribution of material in a defined domain is distributed in some optimal way, subject to a predefined cost function representing desired (e.g., mechanical) properties and constraints. The computation of such an optimal distribution depends on the numerical solution of some physical model (in our case linear elasticity) and robustness is achieved by introducing uncertainties into the model data, namely the forces acting on the structure and variations of the material stiffness, rendering the task high-dimensional and computationally expensive. To alleviate this computational burden, we develop two neural network architectures (NN) that are capable of predicting the gradient step of the optimisation procedure. Since state-of-the-art methods use adaptive mesh refinement, the neural networks are designed to use a sufficiently fine reference mesh such that only one training phase of the neural network suffices. As a first architecture, a convolutional neural network is adapted to the task. To include sequential information of the optimisation process, a recurrent neural network is constructed as a second architecture. A common 2D bridge benchmark is used to illustrate the performance of the proposed architectures. It is observed that the NN prediction of the gradient step clearly outperforms the classical optimisation method, in particular since larger iteration steps become viable.

Analysis of chitosan molecular weight profile by electrophoresis in a porosity step gradient polyacrylamide gel

Chitosan is biocompatible and biodegradable natural biopolymer widely applied in various fields of biology, medicine, and pharmacy, however, its effects significantly depend on the degree of polymerization (DP) and the degree of deacetylation (DDA) of polymer chains. Evaluation of the chitosan chain diversity by DP requires the use of a highly expensive method of high-performance size exclusion chromatography. The aim of our study was to determine the molecular weight profile of chitosan specimens by the use of electrophoresis in a porosity step gradient polyacrylamide gel and to evaluate the efficacy of this method in monitoring the purification of chitosan fragments and its derivatives. Two types of step gradient porosity gels were used: 1) gels of layers with acrylamide concentration 2.5, 3.5, 5.0, 10.0, 15.0, 20.0 % w/v for native chitosan or its high molecular fragments; 2) gels of layers with acrylamide concentration 2.5, 5.0, 10.0, 15.0, 20.0, 25.0 % w/v for low molecular chitosan fragments. The main amount of molecules from the chitosan pool was localized in the type 1 gel in the region of 550-40 kDa and distributed among three bands, which in different samples differed significantly in percentage. Electrophoresis of chitosan fragments fractionated by gel permeation chromatography provided a clear separation of medium molecular weight fragments (50–400 kDa) in type 1 gel and of low molecular weight fragments (3–40 kDa) in type 2 gel. Thus the method of chitosan electrophoresis in a step-gradient porosity of polyacrylamide gel was developed which permits to characterize the molecular weight profile of chitosan specimens polymer chains and is effective in monitoring the isolation of chitosan fragments by gel penetration chromatography of molecular weights from 3 to 400 kDa.

Approximate Wasserstein attraction flows for dynamic mass transport over networks

This paper presents a Wasserstein attraction approach for solving dynamic mass transport problems over networks. In the transport problem over networks, we start with a distribution over the set of nodes that needs to be “transported” to a target distribution accounting for the network topology. We exploit the specific structure of the problem, characterized by the computation of implicit gradient steps, and formulate an approach based on discretized flows. As a result, our proposed algorithm relies on the iterative computation of constrained Wasserstein barycenters. We show how the proposed method finds approximate solutions to the network transport problem, taking into account the topology of the network, the capacity of the communication channels, and the capacity of the individual nodes. Finally, we show the performance of this approach applied to large-scale water transportation networks.

Stepping up to the Canadian opioid crisis: a longitudinal analysis of the correlation between socioeconomic status and population rates of opioid-related mortality, hospitalization and emergency department visits (2000-2017).

High levels of income inequality and increased opioid-related harm across Canada bring into question the role of socioeconomic status (SES) in the opioid epidemic. Only a few studies have examined this association, and most of those have analyzed this issue on a provincial level. This study examined the association between opioid-related health outcomes and SES, and investigated rate ratios over time. Administrative databases were used to identify opioid-related mortality, hospitalization and emergency department visits between 2000 and 2017. Patient's postal code was linked to the quintile of median household income at the forward sortation area level. Crude rates and age- and sex-adjusted rates in each quintile were calculated, as well as the adjusted rate ratio of average annual rates between the lowest and highest quintiles. The significance of the time trend of rate ratios for all outcomes was examined using linear regression. A stepped gradient of opioid-related outcomes across all income quintiles emerged from these data. For mortality, hospitalization and emergency department visits, the average annual rate ratio between lowest quintile and highest quintile was 3.8, 4.3 and 4.9, respectively. These ratios were generally stable and consistent over the study period, albeit the opioid-related mortality SES gap decreased gradually (p < 0.01). Area income quintile was found to be highly associated with opioid outcomes. Psychosocial factors (stress, unemployment, housing insecurity) that are typically concentrated in low SES areas may play a significant role in the opioid epidemic. Health policies should address these factors in order to provide effective solutions.

Task-Sequencing Meta Learning for Intelligent Few-Shot Fault Diagnosis With Limited Data

Recently, deep learning-based intelligent fault diagnosis methods have been developed rapidly, which rely on massive data to train the diagnosis model. However, it is usually difficult to collect sufficient failure data in practical industrial production, thus limits the application of intelligent diagnosis methods. To address the few-shot fault diagnosis problem, a task-sequencing meta-learning method is proposed in this article. First, the meta-learning model is trained over a series of learning tasks to obtain knowledge about how to diagnosis. Thus, the learned knowledge can help adapt and generalize with a few examples when dealing with new tasks that have never been encountered. Then, considering the difference and connection between different failures and diagnosis tasks, a task-sequencing algorithm is proposed to sort meta training tasks from easy to difficult, which followed the way human acquire knowledge. After evaluating the difficulty of each task, the proposed method learns simple tasks first and generalizes the learned knowledge to complex tasks. Better knowledge adaptability is obtained by gradually increasing the task difficulty. Finally, utilizing gradient-based meta learning, the initialization parameters are trained by a small number of gradient steps. The effectiveness of the proposed method is validated by a practice rolling bearing dataset and a power system dataset. The experiment results illustrate that the proposed method can identify new categories with only several samples. In addition, it also shows advantages in fault diagnosis when the categories are fine-grained according to the working conditions. Therefore, the proposed method is suitable for solving the few-shot problem in practice and complicated fault diagnosis.

Design and Bulk Sensitivity Analysis of a Silicon Nitride Photonic Biosensor for Cancer Cell Detection

Bulk sensitivity is an important parameter to validate the efficiency of the photonic waveguide sensor. Due to recent advancements in point-of-care silicon photonic biosensing, the focus is to identify the effective way to improve sensitivity. Integrating polydimethylsiloxane (PDMS) microfluidic channel in sensor architecture decreases the sensitivity due to leakage of molecules at edges. The silicon nitride (SiN4) Mach–Zehnder interferometer utilizes the refractive index of different cancer cells (1.39–1.401) to determine the bulk sensitivity. The proposed gradient step rib-slot structure of 970 nm wide and 400 nm thickness is designed to hold the liquid sample without any PDMS material. This novel waveguide exhibits high waveguide bulk sensitivity S w , bulk and device bulk sensitivity S d compared with the gradient rib waveguide. We achieved a waveguide bulk sensitivity S w , bulk of 2.0699 RIU/RIU and device sensitivity S d of 568 nm/RIU through finite-difference time-domain (FDTD) analysis.

Development and Validation of a Novel LC-MS/MS Method for the Simultaneous Determination of Abemaciclib, Palbociclib, Ribociclib, Anastrozole, Letrozole, and Fulvestrant in Plasma Samples: A Prerequisite for Personalized Breast Cancer Treatment.

Palbociclib, ribociclib and abemaciclib were recently approved as chemotherapeutic agents and are currently in the post-marketing surveillance phase. They are used in combination with aromatase inhibitors anastrozole and letrozole or antiestrogen fulvestrant for HR+, HER2− breast cancer treatment. Here, a novel bioanalytical LC-ESI-MS/MS method was developed for the quantitation of these six drugs in human plasma. The samples were prepared by simple protein precipitation followed by solvent evaporation. A Kinetex biphenyl column (150 × 4.6 mm, 2.6 µm) used for chromatographic analysis adequately resolved even the closely eluting aromatase inhibitors’ peaks. The mobile phase consisted of 0.1% formic acid in water and in ACN, in a linear gradient. An additional gradient step was added to eliminate the observed carry-over. The proposed method was fully validated in the relevant linear ranges covering the expected plasma concentrations of all six drugs (correlation coefficients between 0.9996 and 0.9931). The intra-day method precision (CV) ranged from 3.1% to 15%, while intra-day accuracy (%bias) was between −1.5% and 15.0%. The inter-day precision ranged from 1.6% to 14.9%, with accuracy between −14.3% and 14.6%, which is in accordance with the EMA and ICH guidelines on bioanalytical method validation. The method was successfully applied to samples from patients treated for HR+, HER2− breast cancer.

Differentially Private Accelerated Optimization Algorithms

We present two classes of differentially private optimization algorithms derived from the well-known accelerated first-order methods. The first algorithm is inspired by Polyak's heavy ball method and employs a smoothing approach to decrease the accumulated noise on the gradient steps required for differential privacy. The second class of algorithms are based on Nesterov's accelerated gradient method and its recent multi-stage variant. We propose a noise dividing mechanism for the iterations of Nesterov's method in order to improve the error behavior of the algorithm. The convergence rate analyses are provided for both the heavy ball and the Nesterov's accelerated gradient method with the help of the dynamical system analysis techniques. Finally, we conclude with our numerical experiments showing that the presented algorithms have advantages over the well-known differentially private algorithms.

A fast proximal iteratively reweighted nuclear norm algorithm for nonconvex low-rank matrix minimization problems

In this paper, we propose a fast proximal iteratively reweighted nuclear norm algorithm with extrapolation for solving a class of nonconvex low-rank matrix minimization problems. The proposed method incorporates two different extrapolation steps with respect to the previous iterations into the backward proximal step and the forward gradient step of the classic proximal iteratively reweighted method. We prove that the proposed method generates a convergent subsequence under general parameter constraints, and that any limit point is a stationary point of the problem. Furthermore, we prove that if the objective function satisfies the Kurdyka-Łojasiewicz property, the algorithm is globally convergent to a stationary point of the considered problem. Finally, we perform numerical experiments on a practical matrix completion problem with both synthetic and real data, the results of which demonstrate the efficiency and superior performance of the proposed algorithm.

The development of a generic analysis method for natural and synthetic dyes by ultra-high-pressure liquid chromatography with photo-diode-array detection and triethylamine as an ion-pairing agent

In cultural heritage the characterization of organic colorants is a challenging task. Currently, different chromatographic techniques are used to analyze natural and synthetic dyes separately, since the classes differ significantly in chemical properties and, therefore, chromatographic behavior. To save time, costs and sample material, we developed a method suitable for a wide variety of organic colorants using ultra-high-performance liquid chromatography coupled to a photo-diode-array detector. Gradient elution was performed with a mobile phase consisting of water and methanol with 5 mM triethylamine added as an ion-pairing agent at a pH of 3. Both linear and step gradients were optimized using the ‘Program for Interpretive Optimization of Two-dimensional Resolution’ (PIOTR) Pirok et al. [22]. Two optimized linear gradients and two step gradients were evaluated experimentally. The method was applied on a complex dye mixture containing nearly 130 natural- and synthetic-dye reference compounds. More than 100 of these compounds could be identified in a single experiment. The feasibility of the method was demonstrated by analyzing samples of several precious objects that were found in the Texel shipwreck Vos et al. [2] and of two embroideries of Emile Bernard, the results of which are described in this paper.

Effects of UV/H2O2 degradation and step gradient ethanol precipitation on Sargassum fusiforme polysaccharides: Physicochemical characterization and protective effects against intestinal epithelial injury

In this study, the degraded purified fraction from Sargassum fusiforme polysaccharides (SFP), named DSFP, was produced by the treatment of ultraviolet/hydrogen peroxide (UV/H2O2) degradation and step gradient ethanol precipitation. Results showed that the treatment significantly reduced the molecular weight of polysaccharides, from 282.83 kDa to 18.54 kDa, and influenced their surface morphology and roughness. SFP and DSFP were typical sulfated polysaccharides, mainly composed of fucose, galacturonic acid, glucuronic acid, galactose, and mannose. Both SFP and DSFP increased cell migration during intestinal epithelial wound healing and stimulated the cell cycle progression by promoting the transition from G0/G1 to S phase in the rat intestine epithelium cells (IEC-6). But DSFP had a stronger positive effect on wound healing and cell migration than SFP. It reinforced the intestinal barrier function and attenuated lipopolysaccharides-induced intestinal inflammation. DSFP significantly downregulated the expression of Toll-like receptor 4, tumor necrosis factor-α, interleukin-6, interleukin-1β, and inducible nitric oxide synthase by 53.14%, 92.41%, 66.01%, 68.24%, and 78.09%, respectively, and upregulated that of interleukin-10 by 2.48 folds when compared to the model. Therefore, the treatment (UV/H2O2 degradation and step gradient ethanol precipitation) could effectively improve the protective effects against intestinal epithelial injury.