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Background: Increasing evidence indicates the importance of CD8+ T cells in autoimmune attack against CNS myelin and axon in multiple sclerosis (MS). Previous research has also discovered that myelin-reactive T cells have memory phenotype functions in MS patients. However, limited evidence is available regarding the role of CD8+ memory T cell subsets in MS. This study aimed to explore potential antigen-specific memory T cell-related biomarkers and their association with disease activity. Methods: The myelin oligodendrocyte glycoprotein (MOG)-specific CD8+ memory T cell subsets and their related cytokines (perforin, granzyme B, interferon (IFN)-γ) and negative co-stimulatory molecules (programmed cell death protein 1 (PD-1), T- cell Ig and mucin domain 3 (Tim-3)) were analyzed by flow cytometry and real-time PCR in peripheral blood of patients with relapsing-remitting MS. Results: We found that MS patients had elevated frequency of MOG-specific CD8+ T cells, MOG-specific central memory T cells (TCM), MOG-specific CD8+ effector memory T cells (TEM), and MOG-specific CD8+ terminally differentiated cells (TEMRA); elevated granzyme B expression on MOG-specific CD8+ TCM; and, on MOG-specific CD8+ TEM, elevated granzyme B and reduced PD-1 expression. The Expanded Disability Status Scale score (EDSS) in MS patients was correlated with the frequency of MOG-specific CD8+ TCM, granzyme B expression in CD8+ TCM, and granzyme B and perforin expression on CD8+ TEM, but with reduced PD-1 expression on CD8+ TEM. Conclusion: The dysregulation of antigen-specific CD8+ memory T cell subsets, along with the abnormal expression of their related cytokines and negative co-stimulatory molecules, may reflect an excessive or persistent inflammatory response induced during early stages of the illness. Our findings strongly suggest positive regulatory roles for memory T cell populations in MS pathogenesis, probably via molecular mimicry to trigger or promote abnormal peripheral immune responses. Furthermore, downregulated PD-1 expression may stimulate a positive feedback effect, promoting MS-related inflammatory responses via the interaction of PD-1 ligands. Therefore, these parameters are potential serological biomarkers for predicting disease development in MS.
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Esclerose Múltipla , Humanos , Linfócitos T CD8-Positivos , Granzimas , Receptor de Morte Celular Programada 1 , Células T de Memória , Perforina , Glicoproteína Mielina-Oligodendrócito , CitocinasRESUMO
The self-sufficient cytochrome P450 BM3 mutant (A74G/F87V/D168H/L188Q) can serve as a biocatalyst for whole-cell catalysis process of indigo. Nevertheless, the bioconversion yield of indigo is generally low under normal cultivation conditions (37 °C, 250 rpm). In this study, a recombinant E. coli BL21(DE3) strain was constructed to co-express the P450 BM3 mutant gene and GroEL/ES genes to investigate whether GroEL/ES can promote the indigo bioconversion yield in E. coli. The results revealed that the GroEL/ES system could significantly increase the indigo bioconversion yield, and the indigo bioconversion yield of the strain co-expressing P450 BM3 mutant and GroEL/ES was about 21-fold that of the strain only expressing the P450 BM3 mutant. In addition, the P450 BM3 enzyme content and in vitro indigo bioconversion yield were determined to explore the underlying mechanism for the improvement of indigo bioconversion yield. The results revealed that GroEL/ES did not increase indigo bioconversion yield by increasing the content of P450 BM3 enzyme and its enzymatic transformation efficiency. Moreover, GroEL/ES could improve the intracellular nicotinamide adenine dinucleotide phosphate (NADPH)/NADP+ ratio. Given that NADPH is an important coenzyme in the catalytic process of indigo, the underlying mechanism for the improvement of indigo bioconversion yield is probably related to an increase in the intracellular NADPH/NADP+ ratio.
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Attributing to material identification ability powered by a large number of spectral bands, hyperspectral videos (HSVs) have great potential for object tracking. Most hyperspectral trackers employ manually designed features rather than deeply learned features to describe objects due to limited available HSVs for training, leaving a huge gap to improve the tracking performance. In this paper, we propose an end-to-end deep ensemble network (SEE-Net) to address this challenge. Specifically, we first establish a spectral self-expressive model to learn the band correlation, indicating the importance of a single band in forming hyperspectral data. We parameterize the optimization of the model with a spectral self-expressive module to learn the nonlinear mapping from input hyperspectral frames to band importance. In this way, the prior knowledge of bands is transformed into a learnable network architecture, which has high computational efficiency and can fast adapt to the changes of target appearance because of no iterative optimization. The band importance is further exploited from two aspects. On the one hand, according to the band importance, each frame of HSVs is divided into several three-channel false-color images which are then used for deep feature extraction and location. On the other hand, based on the band importance, the importance of each false-color image is computed, which is then used to assemble the tracking results from individual false-color images. In this way, the unreliable tracking caused by false-color images of low importance can be suppressed to a large extent. Extensive experimental results show that SEE-Net performs favorably against the state-of-the-art approaches. The source code will be available at https://github.com/hscv/SEE-Net.
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Testate amoebae, a polyphyletic protist group inhabiting a wide variety of extant ecosystems, have evolved as far back as early Neoproterozoic. However, their fossil record is discontinuous and biased toward empty shells. Here, we report an arcellinid testate amoeba species, Cangwuella ampulliformis gen. nov., sp. nov., from a shallow-marine community in the Early Devonian of Guangxi, southwestern China. With the aid of scanning electron microscopy and X-ray micro-tomography, we find that the shell of our testate amoeba contains some acetabuliform structures. Although such configuration does not match exactly with the known internal structures in extant testate amoebae, our fossils highlight the potential of exploring the ecological relationships between fossil testate amoebae and their associated organisms, and increase our knowledge on the diversity of testate amoebae in Early Devonian environments.
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Wide-bandgap (WBG) perovskites have great potential for inclusion in efficient tandem solar cells, but large open-circuit voltage losses have limited device performance to date. Here, we show that a high-quality WBG perovskite, FA0.83Cs0.17Pb(I0.8Br0.2)3, with enlarged grain sizes and improved crystallinity can be achieved by incorporating lead chloride (PbCl2) into a lead acetate (PbAc2)-based precursor. The improved film quality resulted in the suppression of nonradiative recombination and a reduction in defect density. Efficient WBG perovskite solar cells (1.66 eV) with an efficiency of 19.3% and a high Voc of 1.22 V were fabricated using a facile one-step spin-coating method without the need for an antisolvent. Notably, the unencapsulated devices retained 90% of their initial power conversion efficiency after storage in a dry box (10% humidity) for 800 h.
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Brain network provides important insights for the diagnosis of many brain disorders, and how to effectively model the brain structure has become one of the core issues in the domain of brain imaging analysis. Recently, various computational methods have been proposed to estimate the causal relationship (i.e., effective connectivity) between brain regions. Compared with traditional correlation-based methods, effective connectivity can provide the direction of information flow, which may provide additional information for the diagnosis of brain diseases. However, existing methods either ignore the fact that there is a temporal-lag in the information transmission across brain regions, or simply set the temporal-lag value between all brain regions to a fixed value. To overcome these issues, we design an effective temporal-lag neural network (termed ETLN) to simultaneously infer the causal relationships and the temporal-lag values between brain regions, which can be trained in an end-to-end manner. In addition, we also introduce three mechanisms to better guide the modeling of brain networks. The evaluation results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method.
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In cloud manufacturing, the dynamic characteristics of manufacturing services increase the complexity of supply and demand matching. A peer effect between service demanders and a synergy effect between service providers affect the final matching result. This paper proposes a two-sided matching model of service providers and demanders considering the peer and synergy effects. First, a dynamic evaluation index system is proposed, and the index weight of service providers and demanders is determined based on the fuzzy analytical hierarchy process. Second, a two-sided matching model is established based on peer and synergy effects. Finally, the proposed method is validated by the collaborative manufacturing of hydraulic cylinders. The results reveal that the model effectively matches service demanders with service providers, improving the overall satisfaction of both parties.
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Near-term quantum computers will be limited in the number of qubits on which they can process information as well as the depth of the circuits that they can coherently carry out. To date, experimental demonstrations of algorithms such as the Variational Quantum Eigensolver (VQE) have been limited to small molecules using minimal basis sets for this reason. In this work we propose incorporating an orbital optimization scheme into quantum eigensolvers wherein a parametrized partial unitary transformation is applied to the basis functions set in order to reduce the number of qubits required for a given problem. The optimal transformation is found by minimizing the ground state energy with respect to this partial unitary matrix. Through numerical simulations of small molecules up to 16 spin orbitals, we demonstrate that this method has the ability to greatly extend the capabilities of near-term quantum computers with regard to the electronic structure problem. We find that VQE paired with orbital optimization consistently achieves lower ground state energies than traditional VQE when using the same number of qubits and even frequently achieves lower ground state energies than VQE methods using more qubits.
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INTRODUCTION: Follicular carcinoma originating from struma ovarii is a clinically rare low-grade malignant tumor. The pathological diagnosis of ovarian thyroid follicular carcinoma is predominantly based on the infiltrative growth and vascular involvement of tumor cell nests of different sizes in the ovarian parenchyma. PATIENT CONCERNS: Here we present a case of this malignancy in which the bilateral ovaries, right oviduct wall, myometrial surface, omentum, and bladder reflex were extensively involved Microscopically, the thyroid follicles in this case showed infiltrative growth of nodules of different sizes in the ovarian stroma. DIAGNOSIS: The epithelial layer of the follicles was atypical, but with no nuclear features of papillary thyroid carcinoma such as nuclear groove and nuclear pseudoinclusions. Immunohistochemistry showed positive expression of thyroglobulin, thyroid transcription factor-1, and cytokeratin19, with a Ki-67 index of 5% +. Immunohistochemical results combined with microscopic morphology allowed a diagnosis of follicular carcinoma originating from struma ovarii. INTERVENTIONS: After exclusion of contraindications to surgery, the patient underwent surgical exploration on July 26, 2022, during which frozen pathological examination was performed. OUTCOMES: The patient recovered well and was discharged. At the first follow-up visit in October 2022, the patient had an excellent survival. CONCLUSION: The analysis of the microscopic morphological characteristics and immunohistochemistry deepened our understanding of the pathological characteristics of ovarian and thyroid follicular carcinoma, and further provides a diagnostic reference for other clinicians who will encounter these conditions in the future.
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Adenocarcinoma Folicular , Neoplasias Ovarianas , Estruma Ovariano , Neoplasias da Glândula Tireoide , Feminino , Humanos , Estruma Ovariano/diagnóstico , Estruma Ovariano/cirurgia , Estruma Ovariano/patologia , Neoplasias Ovarianas/diagnóstico , Neoplasias Ovarianas/cirurgia , Neoplasias Ovarianas/patologia , Adenocarcinoma Folicular/diagnóstico , Adenocarcinoma Folicular/cirurgia , Neoplasias da Glândula Tireoide/patologiaRESUMO
BACKGROUND: Cardiac myxoma (CM) is an important etiology of stroke in young adults, but studies on CM-related ischemic stroke (CM-IS) are limited and conflicting. Hence, we investigated clinical characterizations, risk factors of CM-IS, and short-term survival after surgical resection. METHODS: We performed a retrospective analysis of data from all CM patients at three referral management centers and conducted follow-up examination. RESULTS: Among 414 CM patients, 402 were recruited for further analysis, including 54 patients with CM-IS and 348 patients with CM without stroke (Non-stroke). In the acute phase, patients presented with NIHSS 3 (interquartile range: 0-10) and clinical presentation comprising neurological, cardiac and constitutional symptoms. Multivariate analysis showed that the factors associated with an increased risk of CM-IS were tumor width < 30 mm [OR = 2.652, 95% CI: 1.061-6.627, P = 0.037], tumors with high-mobility (OR = 2.700, 95% CI: 1.357-5.371, P = 0.005), thrombus on the tumor surface (OR = 1.856, 95% CI: 1.003-3.434, P = 0.049), and lower B-type natriuretic peptide (BNP) levels (OR = 0.995, 95% CI: 0.989-0.999, P = 0.047). The overall three-year survival rate was 95.7% (95% CI: 94.9-96.5) in CM-IS patients who underwent surgery. CONCLUSIONS: CM-IS patients had mild or moderate neurologic deficits with various presentations at disease onset. Narrower tumor width, tumors with high-mobility, thrombus on the tumor surface, and lower BNP levels are potential predictors of CM-IS development. Surgical removal of CM is safe and efficacious in patients with CM-IS.
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AVC Isquêmico , Mixoma , Acidente Vascular Cerebral , Trombose , Adulto Jovem , Humanos , Estudos Retrospectivos , Estudos de Casos e Controles , Peptídeo Natriurético Encefálico , Acidente Vascular Cerebral/etiologia , Acidente Vascular Cerebral/cirurgia , Acidente Vascular Cerebral/diagnóstico , Fatores de Risco , Mixoma/complicações , Mixoma/cirurgia , Mixoma/patologia , Trombose/complicaçõesRESUMO
Achieving the long-term stability of perovskite solar cells is arguably the most important challenge required to enable widespread commercialization. Understanding the perovskite crystallization process and its direct impact on device stability is critical to achieving this goal. The commonly employed dimethyl-formamide/dimethyl-sulfoxide solvent preparation method results in a poor crystal quality and microstructure of the polycrystalline perovskite films. In this work, we introduce a high-temperature dimethyl-sulfoxide-free processing method that utilizes dimethylammonium chloride as an additive to control the perovskite intermediate precursor phases. By controlling the crystallization sequence, we tune the grain size, texturing, orientation (corner-up versus face-up) and crystallinity of the formamidinium (FA)/caesium (FA)yCs1-yPb(IxBr1-x)3 perovskite system. A population of encapsulated devices showed improved operational stability, with a median T80 lifetime (the time over which the device power conversion efficiency decreases to 80% of its initial value) for the steady-state power conversion efficiency of 1,190 hours, and a champion device showed a T80 of 1,410 hours, under simulated sunlight at 65 °C in air, under open-circuit conditions. This work highlights the importance of material quality in achieving the long-term operational stability of perovskite optoelectronic devices.
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Objective: This study aimed to explore risk factors, clinical features, and prognosis of patients with hypertrophic cardiomyopathy (HCM) complicated by ischemic stroke (IS). Methods: We conducted a retrospective analysis of all HCM patient data and a 1-year follow-up study. Results: Totally, 506 patients with HCM, including 71 with IS, were enrolled. Older age (≥63 years) was associated with an increased risk of IS in HCM patients (OR = 1.045, 95% CI: 1.018-1.072, p = 0.001). Among 37 patients complicated by IS, 22 (59.5%, 22/37) manifested as cardioembolism (CE) subtype, and 13 (35.1%, 3/37) small artery occlusion (SAO) subtype, according to TOAST classification. In the acute phase, the IS patients presented with NIHSS 4 (interquartile range: 1, 10). Multi-infarction was more common than single infarction (72.7 vs. 27.3%), while cortical + subcortical infarction (CE group: 50%) or subcortical infarction (SAO group: 53.8%) constituted most IS cases. Additionally, the blood supply areas of anterior circulation (CE group: 45.5%; SAO group: 92.3%) or anterior + posterior circulation (CE group: 50%) were mainly involved. The 1-year survival rate of HCM patients with concomitant IS was 81.8%, and IS was associated with 1-year all-cause death in HCM patients (HR = 5.689, 95% CI: 1.784-18.144, p = 0.003). Conclusion: Older age is a risk factor for IS occurrence in HCM patients. Patients with HCM complicated by IS had mild or moderate neurologic deficits at disease onset. CE and SAO subtypes predominate in patients with concomitant IS, especially the former. Multiple cortical and subcortical infarctions are their neuroimaging characteristics, mainly involving the anterior circulation or anterior + posterior circulation. Is is a risk factor for all-cause death in HCM patients within 1 year.
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Honey is a natural product and is heavily consumed for its well-known nutritional functions. Honeys with different floral origins possess distinctive flavors, tastes, functions and economic values. It is vital to establish an effective strategy for identifying the authenticity of honey. The intrinsic genetic materials of pollen were adopted as target analytes for the effective identification of honey with floral origins. With an optimized protocol for the rapid gene extraction from honey, target genetic templates were amplified on-site with a portable device. Conveniently, all on-site amplified functional products were easily judged by the designed lateral flow strip (LFS), which was defined as the molecular LFS in this research. Additionally, the entire on-site genetic authentication of honey was completed in less than 2 h by visual observation. Commercial honey products have been successfully identified with excellent accuracy. This low-cost, high-efficiency and easy-operational strategy will greatly benefit the quality guarantee of foods with specific functions and geographical markers.
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Análise de Alimentos , MelRESUMO
Objective: This study aimed to identify risk factors and create a predictive model for ischemic stroke (IS) in patients with dilated cardiomyopathy (DCM) using the Bayesian network (BN) approach. Materials and methods: We collected clinical data of 634 patients with DCM treated at three referral management centers in Beijing between 2016 and 2021, including 127 with and 507 without IS. The patients were randomly divided into training (441 cases) and test (193 cases) sets at a ratio of 7:3. A BN model was established using the Tabu search algorithm with the training set data and verified with the test set data. The BN and logistic regression models were compared using the area under the receiver operating characteristic curve (AUC). Results: Multivariate logistic regression analysis showed that hypertension, hyperlipidemia, atrial fibrillation/flutter, estimated glomerular filtration rate (eGFR), and intracardiac thrombosis were associated with IS. The BN model found that hyperlipidemia, atrial fibrillation (AF) or atrial flutter, eGFR, and intracardiac thrombosis were closely associated with IS. Compared to the logistic regression model, the BN model for IS performed better or equally well in the training and test sets, with respective accuracies of 83.7 and 85.5%, AUC of 0.763 [95% confidence interval (CI), 0.708-0.818] and 0.822 (95% CI, 0.748-0.896), sensitivities of 20.2 and 44.2%, and specificities of 98.3 and 97.3%. Conclusion: Hypertension, hyperlipidemia, AF or atrial flutter, low eGFR, and intracardiac thrombosis were good predictors of IS in patients with DCM. The BN model was superior to the traditional logistic regression model in predicting IS in patients with DCM and is, therefore, more suitable for early IS detection and diagnosis, and could help prevent the occurrence and recurrence of IS in this patient cohort.
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Deep learning has become an essential technique in image steganography. Most of the current deep-learning-based steganographic methods process digital images in the spatial domain. There are problems such as limited embedding capacity and unsatisfactory visual quality. To improve capacity-distortion performance, we develop a steganographic method from the frequency-domain perspective. We propose a module called the adaptive frequency-domain channel attention network (AFcaNet), which makes full use of the frequency features in each channel by a fine-grained manner of assigning weights. We apply this module to the state-of-the-art SteganoGAN, forming an Adaptive Frequency High-capacity Steganography Generative Adversarial Network (AFHS-GAN). The proposed neural network enhances the ability of high-dimensional feature extraction through overlaying densely connected convolutional blocks. In addition to this, a low-frequency loss function is introduced as an evaluation metric to guide the training of the network and thus reduces the modification of low-frequency regions of the image. Experimental results on the Div2K dataset show that our method has a better generalization capability compared to the SteganoGAN, with substantial improvement in both embedding capacity and stego-image quality. Furthermore, the embedding distribution of our method in the DCT domain is more similar to that of the traditional method, which is consistent with the prior knowledge of image steganography.
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Algoritmos , Processamento de Imagem Assistida por Computador , Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de ComputaçãoRESUMO
Generation of serotonin neurons (SNs) from human pluripotent stem cells (hPSCs) provides a promising platform to explore the mechanisms of serotonin-associated neuropsychiatric disorders. However, neural differentiation always yields heterogeneous cell populations, making it difficult to identify and purify SNs in vitro or track them in vivo following transplantation. Herein, we generated a TPH2-EGFP reporter hPSC line with insertion of EGFP into the endogenous tryptophan hydroxylase 2 (TPH2) locus using CRISPR-Cas9-mediated gene editing technology. This TPH2-reporter, which faithfully indicated TPH2 expression during differentiation, enabled us to obtain purified SNs for subsequent transcriptional analysis and study of pharmacological responses to antidepressants. In addition, the reporter system showed strong EGFP expression to indicate SNs, which enabled us to explore in vitro and ex vivo electrophysiological properties of SNs. In conclusion, this TPH2-EGFP reporter cell line might be of great significance for studies on human SN-related development and differentiation, drug screening, disease modeling, and cell replacement therapies.
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Células-Tronco Pluripotentes , Serotonina , Diferenciação Celular/genética , Linhagem Celular , Genes Reporter , Humanos , Neurônios/metabolismo , Células-Tronco Pluripotentes/metabolismo , Triptofano Hidroxilase/genética , Triptofano Hidroxilase/metabolismoRESUMO
Deep learning (DL) based hyperspectral images (HSIs) denoising approaches directly learn the nonlinear mapping between noisy and clean HSI pairs. They usually do not consider the physical characteristics of HSIs. This drawback makes the models lack interpretability that is key to understanding their denoising mechanism and limits their denoising ability. In this paper, we introduce a novel model-guided interpretable network for HSI denoising to tackle this problem. Fully considering the spatial redundancy, spectral low-rankness, and spectral-spatial correlations of HSIs, we first establish a subspace-based multidimensional sparse (SMDS) model under the umbrella of tensor notation. After that, the model is unfolded into an end-to-end network named SMDS-Net, whose fundamental modules are seamlessly connected with the denoising procedure and optimization of the SMDS model. This makes SMDS-Net convey clear physical meanings, i.e., learning the low-rankness and sparsity of HSIs. Finally, all key variables are obtained by discriminative training. Extensive experiments and comprehensive analysis on synthetic and real-world HSIs confirm the strong denoising ability, strong learning capability, promising generalization ability, and high interpretability of SMDS-Net against the state-of-the-art HSI denoising methods. The source code and data of this article will be made publicly available at https://github.com/bearshng/smds-net for reproducible research.
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Respiratory muscle paralysis caused by acute cervical spinal cord injury usually leads to pulmonary ventilation dysfunction and even death from respiratory failure. In addition to invasive treatments such as mechanical ventilation, the utilization of noninvasive respiratory support equipment plays an important role in long-term assisted breathing. In this study, we describes a wearable, noninvasive vest with adjustable pressure that enables assisted breathing and with an automatic alarm, and we aims to explore its safety and effectiveness on healthy adult participants. The vest monitors the human heart rate and the blood oxygen index data in real time, the alarm is automatically activated when the data is abnormal. Eight healthy participants had no obvious discomfort during the test while wearing the vest. Lung volumes, antero-posterior diameters, and left-right diameters at the second, fourth, and sixth ribs levels were acquired before and after inflation of the vest airbag, the data acquired by the imaging analysis using chest computed tomography showed significant differences before and after the inflation (p < 0.05). Thus, The vest designed for this study can achieve uniform and effective compression of the thorax, significantly changed the size of the thorax and lungs. It is expected to be applied as noninvasive support for patients with respiratory dysfunction.
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Tórax , Dispositivos Eletrônicos Vestíveis , Adulto , Humanos , Pulmão , Pressão , Tórax/fisiologia , Tomografia Computadorizada por Raios XRESUMO
We propose a quantum-classical hybrid variational algorithm, the quantum orbital minimization method (qOMM), for obtaining the ground state and low-lying excited states of a Hermitian operator. Given parametrized ansatz circuits representing eigenstates, qOMM implements quantum circuits to represent the objective function in the orbital minimization method and adopts a classical optimizer to minimize the objective function with respect to the parameters in ansatz circuits. The objective function has an orthogonality constraint implicitly embedded, which allows qOMM to apply a different ansatz circuit to each input reference state. We carry out numerical simulations that seek to find excited states of H2, LiH, and a toy model consisting of four hydrogen atoms arranged in a square lattice in the STO-3G basis with UCCSD ansatz circuits. Comparing the numerical results with existing excited states methods, qOMM is less prone to getting stuck in local minima and can achieve convergence with more shallow ansatz circuits.
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This study was conducted to evaluate the effects of extra virgin olive (EVO) oil incorporation on the physicochemical properties and microstructure of surimi gels subjected to ultrasound-assisted water-bath heating. As the oil content was increased from 0 to 5 g/100 g, the breaking force and gel strength of the surimi gels significantly decreased, while the whiteness level exhibited the opposite tendency irrespective of the heating method. Compared with the traditional water-bath heating method, the ultrasonic heating promoted the unfolding of the α-helix structure and intensified the formation of ß-sheet content and non-covalent bonds (ionic bonds, hydrogen bonds, and disulfide bonds), especially disulfide bonds, which contributed to the further crosslinking of the proteins and to gelation, thereby improving the gels' strength. In addition, smaller cavities and compact microstructures were observed in the low-oil (≤3 g/100 g) surimi gels under ultrasonic treatment, which effectively prevented water migration in the gel network and resulted in a high water holding capacity and uniform water distribution. However, the ultrasonic treatment barely remedied the poor microstructures of the high-oil (>3 g/100 g) surimi gels owing to oil coalescence, which weakened the protein-protein interaction. In conclusion, ultrasonic treatment combined with water-bath heating significantly improved the gelation properties of the low-oil surimi gels, although it did not remarkably improve those of the high-oil gels. The choice of a suitable oil concentration could be of great importance for the production and functioning of surimi products via ultrasound-assisted treatments.
