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Abstract Background Acute pancreatitis (AP) has become a significant global health concern, and a high body mass index (BMI) has been identified as a key risk factor exacerbating this condition. Within this context, lipid metabolism assumes a critical role. The complex relationship between elevated BMI and AP, mediated by lipid metabolism, markedly increases the risk of complications and mortality. This study aimed to accurately define the correlation between BMI and AP, incorporating a comprehensive analysis of the interactions between individuals with high BMI and AP. Methods Mendelian randomization (MR) analysis was first applied to determine the causal relationship between BMI and the risk of AP. Subsequently, three microarray datasets were obtained from the GEO database. This was followed by an analysis of differentially expressed genes and the application of weighted gene coexpression network analysis (WGCNA) to identify key modular genes associated with AP and elevated BMI. Functional enrichment analysis was then performed to shed light on disease pathogenesis. To identify the most informative genes, machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were employed. Subsequent analysis focused on the colocalization of the Quantitative Trait Loci (eQTL) data associated with the selected genes and Genome-Wide Association Studies (GWAS) data related to the disease. Preliminary verification of gene expression trends was conducted using external GEO datasets. Ultimately, the diagnostic potential of these genes was further confirmed through the development of an AP model in mice with a high BMI. Results A total of 21 intersecting genes related to BMI>30, AP, and lipid metabolism were identified from the datasets. These genes were primarily enriched in pathways related to cytosolic DNA sensing, cytokine‒cytokine receptor interactions, and various immune and inflammatory responses. Next, three machine learning techniques were utilized to identify HADH as the most prevalent diagnostic gene. Colocalization analysis revealed that HADH significantly influenced the risk factors associated with BMI and AP. Furthermore, the trend in HADH expression within the external validation dataset aligned with the trend in the experimental data, thus providing a preliminary validation of the experimental findings.The changes in its expression were further validated using external datasets and quantitative real-time polymerase chain reaction (qPCR). Conclusion This study systematically identified HADH as a potential lipid metabolism-grounded biomarker for AP in patients with a BMI>30.

Surface electromyography (sEMG), a human-machine interface for gesture recognition, has shown promising potential for decoding motor intentions, but a variety of nonideal factors restrict its practical application in assistive robots. In this paper, we summarized the current mainstream gesture recognition strategies and proposed a gesture recognition method based on multimodal canonical correlation analysis feature fusion classification (MCAFC) for a nonideal condition that occurs in daily life, i.e., posture variations. The deep features of the sEMG and acceleration signals were first extracted via convolutional neural networks. A canonical correlation analysis was subsequently performed to associate the deep features of the two modalities. The transformed features were utilized as inputs to a linear discriminant analysis classifier to recognize the corresponding gestures. Both offline and real-time experiments were conducted on eight non-disabled subjects. The experimental results indicated that MCAFC achieved an average classification accuracy, average motion completion rate, and average motion completion time of 93.44%, 94.05%, and 1.38 s, respectively, with multiple dynamic postures, indicating significantly better performance than that of comparable methods. The results demonstrate the feasibility and superiority of the proposed multimodal signal feature fusion method for gesture recognition with posture variations, providing a new scheme for myoelectric control.

ObjectiveThis review aims to provide a quantitative and qualitative bibliometric analysis of literature from 2013 to 2023 on the role of exosomes in PC, with the goal of identifying current trends and predicting future hotspots.MethodsWe retrieved relevant publications concerning exosomes in PC, published between 2013 and 2023, from the Web of Science Core Collection. Bibliometric analyses were conducted using VOSviewer(1.6.19), CiteSpace(6.2.R4), and Microsoft Excel (2019).ResultsA total of 624 papers were analyzed, authored by 4017 researchers from 55 countries/regions and 855 institutions, published in 258 academic journals. China (n=285, 34.42%) and the United States (n=183, 24.87%) were the most frequent contributors and collaborated closely. However, publications from China had a relatively low average number of citations (41.45 times per paper). The output of Shanghai Jiao Tong University ranked first, with 28 papers (accounting for 4.5% of the total publications). Cancers (n=31, 4.9%); published the most papers in this field. Researcher Margot Zoeller published the most papers (n=12) on this topic. Research hotspots mainly focused on the mechanisms of exosomes in PC onset and progression, the role of exosomes in PC early diagnosis and prognosis, exosomes promote the development of PC chemoresistance, and potential applications of exosomes as drug carriers for PC therapies. We observed a shift in research trends, from mechanistic studies toward clinical trials, suggesting that clinical applications will be the focus of future attention. Emerging topics were pancreatic stellate cells, diagnostic biomarkers, mesenchymal stem cells, extracellular vesicles.ConclusionOur scientometric and visual analysis provides a comprehensive overview of the literature on the role of exosomes in PC published during 2013–2023. This review identifies the frontiers and future directions in this area over the past decade, and is expected to provide a useful reference for researchers in this field.

Deformable registration plays a fundamental and crucial role in scenarios such as surgical navigation and image-assisted analysis. While deformable registration methods based on unsupervised learning have shown remarkable success in predicting displacement fields with high accuracy, many existing registration networks are limited by the lack of multi-scale analysis, restricting comprehensive utilization of global and local features in the images. To address this limitation, we propose a novel registration network called multi-scale feature extraction-integration network (MF-Net). First, we propose a multiscale analysis strategy that enables the model to capture global and local semantic information in the image, thus facilitating accurate texture and detail registration. Additionally, we introduce grouped gated inception block (GI-Block) as the basic unit of the feature extractor, enabling the feature extractor to selectively extract quantitative features from images at various resolutions. Comparative experiments demonstrate the superior accuracy of our approach over existing methods.

Summary: The transmembrane channel-like (TMC) protein family comprises eight members, with TMC1 and TMC2 being extensively studied. This study demonstrates substantial co-expression of TMC7 with the mechanosensitive channel Piezo2 in somatosensory neurons. Genetic deletion of TMC7 in primary sensory ganglia neurons in vivo enhances sensitivity in both physiological and pathological mechanosensory transduction. This deletion leads to an increase in proportion of rapidly adapting (RA) currents conducted by Piezo2 in dorsal root ganglion (DRG) neurons and accelerates RA deactivation kinetics. In HEK293 cells expressing both proteins, TMC7 significantly suppresses the current amplitudes of co-expressed Piezo2. Our findings reveal that TMC7 and Piezo2 exhibit physical interactions, and both proteins also physically interact with cytoskeletal β-actin. We hypothesize that TMC7 functions as an inhibitory modulator of Piezo2 in DRG neurons, either through direct inhibition or by disrupting the transmission of mechanical forces from the cytoskeleton to the channel.

In clinical practice and research, the classification and diagnosis of neurological diseases such as Parkinson’s Disease (PD) and Multiple System Atrophy (MSA) have long posed a significant challenge. Currently, deep learning, as a cutting-edge technology, has demonstrated immense potential in computer-aided diagnosis of PD and MSA. However, existing methods rely heavily on manually selecting key feature slices and segmenting regions of interest. This not only increases subjectivity and complexity in the classification process but also limits the model’s comprehensive analysis of global data features. To address this issue, this paper proposes a novel 3D context-aware modeling framework, named 3D-CAM. It considers 3D contextual information based on an attention mechanism. The framework, utilizing a 2D slicing-based strategy, innovatively integrates a Contextual Information Module and a Location Filtering Module. The Contextual Information Module can be applied to feature maps at any layer, effectively combining features from adjacent slices and utilizing an attention mechanism to focus on crucial features. The Location Filtering Module, on the other hand, is employed in the post-processing phase to filter significant slice segments of classification features. By employing this method in the fully automated classification of PD and MSA, an accuracy of 85.71%, a recall rate of 86.36%, and a precision of 90.48% were achieved. These results not only demonstrates potential for clinical applications, but also provides a novel perspective for medical image diagnosis, thereby offering robust support for accurate diagnosis of neurological diseases.

Introduction: The transmembrane channel-like (TMC) protein family contains eight members, TMC1–TMC8. Among these members, only TMC1 and TMC2 have been intensively studied. They are expressed in cochlear hair cells and are crucial for auditory sensations. TMC6 and TMC8 contribute to epidermodysplasia verruciformis, and predispose individuals to human papilloma virus. However, the impact of TMC on peripheral sensation pain has not been previously investigated.Methods: RNAscope was employed to detect the distribution of TMC6 mRNA in DRG neurons. Electrophysiological recordings were conducted to investigate the effects of TMC6 on neuronal characteristics and M channel activity. Zn2+ indicators were utilized to detect the zinc concentration in DRG tissues and dissociated neurons. A series of behavioural tests were performed to assess thermal and mechanical sensation in mice under both physiological and pathological conditions.Results and Discussion: We demonstrated that TMC6 is mainly expressed in small and medium dorsal root ganglion (DRG) neurons and is involved in peripheral heat nociception. Deletion of TMC6 in DRG neurons hyperpolarizes the resting membrane potential and inhibits neuronal excitability. Additionally, the function of the M channel is enhanced in TMC6 deletion DRG neurons owing to the increased quantity of free zinc in neurons. Indeed, heat and mechanical hyperalgesia in chronic pain are alleviated in TMC6 knockout mice, particularly in the case of heat hyperalgesia. This suggests that TMC6 in the small and medium DRG neurons may be a potential target for chronic pain treatment.

ObjectivesNon-high-density lipoprotein cholesterol (non-HDL-C) has attracted attention because it is associated with a variety of diseases and is easy to measure. However, the relationship between non-HDL-C and depression is still unclear. Our aim was to assess the relationship between non-HDL-C and depression using the cross-sectional NHANES survey from 2005 to 2018.MethodsWe examined the association between non-HDL-C and depression using weighted multivariable logistic regression models and subgroup analysis. Sensitivity analysis demonstrated the robustness of the results.ResultsThere were 42,143 participants in this study and 8.6% had depression (weighted 7.53%). Non-HDL-C was higher in participants with depression compared to those without depression (weighted mean 3.64 vs. 3.73, p

Abstract Novel adjuvants are highly demanded to aid in development of improved or new vaccines against existing or emerging infectious diseases. Considering commonly used Alum and MF59 adjuvants induce tissue stress and release of endogenous danger signals to mediate their adjuvant effects, physical modalities may be used to induce tissue stress and endogenous danger signal release to enhance vaccine-induced immune responses. Furthermore, physical adjuvants are less likely to induce significant systemic adverse reactions due to their localized effects. Recently we found non-invasive radiofrequency (RF) pretreatment of the skin could significantly enhance intradermal vaccine-induced immune responses in murine models that included pandemic influenza vaccine, pre-pandemic vaccine, and influenza internal antigen vaccine. It remained to be explored whether the physical RF adjuvant (RFA) could be used to boost seasonal influenza vaccination, spare vaccine doses, and induce cross-protective immunity. This study found the physical RFA could significantly enhance seasonal influenza vaccine-induced immune responses against each viral strain and robustly enhance low-dose (nanograms) H3N2 vaccine-induced immune responses and protection in murine models. RFA also induced cross-protective immunity against heterologous and heterosubtypic influenza viruses. Further studies found heat shock protein 70 (inducible endogenous danger signal) and myeloid differentiation primary response 88 adaptor played a crucial role in dose-sparing effects of RFA. These data strongly support further development of the physical RFA to boost influenza vaccination.

We report a representative concurrent event of four wave modes at L ≈ 5.0, including electrostatic electron cyclotron harmonic (ECH) waves, exohiss, magnetosonic (MS) waves, and electromagnetic ion cyclotron (EMIC) waves, based on the observations from Van Allen Probe A on October 15, 2015. The diffusion coefficients induced by these waves are calculated by using both the Full Diffusion Code and test particle simulations. Moreover, the scattering effects of these waves on energetic electrons are simulated by using a two-dimensional Fokker–Planck diffusion model. The results show that ECH waves mainly scatter low-pitch-angle (< 20°) electrons at 0.1–10 keV; exohiss can significantly scatter hundreds of kiloelectron volt electrons to form a reversed energy spectrum; MS waves mainly affect high-pitch-angle electrons (> 60°); and EMIC waves scatter only > 5 MeV electrons. The combined scattering effects of exohiss and MS waves are stronger than those of exohiss alone. The top-hat pitch angle distributions produced by exohiss are relaxed after adding the effect of MS waves. Because the energies of electrons scattered by ECH waves and EMIC waves are much lower and higher than those scattered by exohiss and MS waves, respectively, the combined scattering effects with the addition of ECH and EMIC waves show little difference from the results for the combination of MS waves and exohiss. These results suggest that distinct wave modes can occur simultaneously and scatter electrons in combination or individually, which requires careful consideration in future global simulations of the complex dynamics of radiation belt energetic electrons.

Abstract Based on Cassini observations, we report representative electrostatic electron cyclotron harmonic (ECH) wave events observed in Saturn's magnetosphere within remote plasma injections. Unlike local injections, remote injections are “older” injection events that have evolved to form a dispersed signature in particle energy spectrum. We show that Saturnian ECH waves within remote injections present a strong fundamental band and much weaker high harmonic bands. By calculating the linear wave growth rates based on the measured electron velocity distributions, we indicate that Saturnian ECH waves can be excited by the loss cone distribution of remotely injected, hot electrons of ∼100 eV to several keV. We find that ECH waves tend to intensify with increased fluxes of injected hot electrons but weakening with increased evolution time of the flux tubes, which is consistent with the results of wave growth rates and improves the current understanding of the generation of ECH waves at Saturn.

How to learn informative representations from Electromyography (EMG) signals is of vital importance for myoelectric control systems. Traditionally, hand-crafted features are extracted from individual EMG channels and combined together for pattern recognition. The spatial topological information between different channels can also be informative, which is seldom considered. This paper presents a radically novel approach to extract spatial structural information within diverse EMG channels based on the symmetric positive definite (SPD) manifold. The object is to learn non-Euclidean representations inside EMG signals for myoelectric pattern recognition. The performance is compared with two classical feature sets using accuracy and F1-score. The algorithm is tested on eleven gestures collected from ten subjects, and the best accuracy reaches 84.85%±5.15% with an improvement of 4.04%~20.25%, which outperforms the contrast method, and reaches a significant improvement with the Wilcoxon signed-rank test. Eleven gestures from three public databases involving Ninapro DB2, DB4, and DB5 are also evaluated, and better performance is observed. Furthermore, the computational cost is less than the contrast method, making it more suitable for low-cost systems. It shows the effectiveness of the presented approach and contributes a new way for myoelectric pattern recognition.

Flowering stage of soybean is an important agronomic trait, which is important for soybean yield, quality and adaptability, and is the external expression of integrating external environmental factors and endogenous signals of the plant itself. Cropping system can change soil properties and fertility, which in turn determine plant growth and yield. The microbial community is the key regulator of plant health and production performance. Currently, there is limited understanding of the effects of cropping systems on microbial community composition, ecological processes controlling community assembly in different soil-plant continuum compartments of soybean. Here, we hope to clarify the structure and assembly process of different soybean compartments bacterial community at flowering stage through our work. The results showed that intercropping decreased the species diversity of rhizosphere and phyllosphere, and phylloaphere microbes mainly came from rhizosphere. FAPROTAX function prediction showed that indicator species sensitive to intercropping and crop rotation were involved in nitrogen/phosphorus cycle and degradation process, respectively. In addition, compared to the continuous cropping, intercropping increased the stochastic assembly processes of bacterial communities in plant-associated compartments, while crop rotation increased the complexity and stability of the rhizosphere network and the deterministic assembly process. Our study highlights the importance of intercropping and crop rotation, as well as rhizosphere and phyllosphere compartments for future crop management and sustainable agricultural regulation of crop microbial communities.

There exist significant gaps in nutritional status between urban and rural populations in China. The previous literature has shown that more knowledge and usage of nutrition labels are instrumental in improving diet quality and health. The aim of the study is to analyze: (1) Are there urban–rural disparities in consumer knowledge, use and perceived benefits of nutrition labels in China; (2) If so, what are the magnitudes of the disparities; (3) What can explain the disparities, and how can the disparities be reduced? The Oaxaca–Blinder (O-B) decomposition is utilized to analyze the predictors of urban–rural disparities in nutrition labels based on a self-conducted study of Chinese individuals. The information from a total of 1635 individuals (aged 11–81 years) across China in 2016 was collected in the survey. We find that rural respondents have less knowledge, lower usage and perceived benefits of nutrition labels than their urban counterparts. Demographics, focus on food safety, frequent shopping locations and income jointly explain 98.9% of the disparity in the knowledge of nutrition labels. Nutrition label knowledge is the predictor which contributes most to urban–rural disparity in label use—accounting for 29.6% of the disparity. Nutrition label knowledge and use are the two biggest predictors of disparities in perceived benefits—accounting for 29.7% and 22.8% of the disparity in perceived benefits, respectively. Our study suggests that policies aiming to improve income and education, as well as raising awareness of food safety in rural areas, are promising in closing the urban–rural disparities in nutrition labels knowledge, use, diet quality and health in China.

The supply level of irrigation and water conservancy is related to the utilization efficiency of water resources, the production level of farmers, and the supply quality of agricultural products, especially relating to national food security and stability. Based on 1169 pieces of data collected from field surveys in three provinces of China in 2019, an evaluation system of social network relationships was constructed from five aspects: network scale, network tightness, trust and commitment, social atmosphere and sense of belonging, and social participation. These five aspects are the channels for farmers to obtain information. A binary logistic model was used to analyze the impact of the social network relationships on farmers’ participation in small water-saving irrigation and water conservancy facilities supply, and the key factors affecting farmers’ behavior were selected. The purpose of this study is to further improve the evaluation system of social network relationships and the study of the effect of social network relationships on farmers’ water-saving behaviors, enrich relevant theories and provide a feasible path for the implementation of water-saving irrigation from the macro initiative level. The results show that higher network closeness and policy satisfaction, water management experience, agricultural insurance, strong family decision-making power, etc., will reduce the likelihood of farmers participating in the supply of small water-saving irrigation and water conservancy facilities; increasing network compactness will increase the possibility of farmers’ participation in the supply; trust and commitment, social ethos and sense of belonging, social participation, and other factors have no significant influence on farmer participation behavior. It can be seen that network tightness and network scale play an important role in the behavior of farmers’ participation in public affairs. In conclusion, social network relationships will affect farmers’ participation in the supply of irrigation and water conservancy facilities, but different dimensions have different influences on it.

Visualizing the function of pancreatic β-cells in vivo has been a long-sought goal for β-cell researchers. Unlike imaging of β-cells in mammalian species with conventional positron emission tomography and single-photon emission computed tomography, which only provides limited spatial-temporal resolution, transparent zebrafish embryos are a unique model that allows high-resolution fluorescent imaging of β-cells in their native physiological microenvironment in vivo. Here, we detail a protocol for real-time visualization of individual β-cell function in vivo in a non-invasive manner, through combination of a novel transgenic zebrafish reporter line Tg (ins:Rcamp1.07) with both a commercial spinning-disc confocal microscope and an in-house developed super-resolution microscope (2P3A-DSLM). The protocol described here allows for the longitudinal monitoring of dynamic calcium activities from heterogeneous β-cells in early developing zebrafish embryos and is readily adaptable for use in imaging other important processes in islet biology, as well as screening new compounds that can promote β-cell function or maturation using a living whole organism system.

At present, an oral tumor is usually treated by surgery combined with preoperative or postoperative radiotherapies and chemotherapies. However, traditional chemotherapies frequently result in substantial toxic side effects, including bone marrow suppression, malfunction of the liver and kidneys, and neurotoxicity. As a new local drug delivery system, the smart drug delivery system based on hydrogel can control drug release in time and space, and effectively alleviate or avoid these problems. Environmentally responsive hydrogels for smart drug delivery could be triggered by temperature, photoelectricity, enzyme, and pH. An overview of the most recent research on smart hydrogels and their controlled-release drug delivery systems for the treatment of oral cancer is given in this review. It is anticipated that the local drug release method and environment-responsive benefits of smart hydrogels will offer a novel technique for the low-toxicity and highly effective treatment of oral malignancy.

Aging is associated with diminished immune system function, which renders old people vulnerable to influenza infection and also less responsive to influenza vaccination. This study explored whether the CpG 1018 adjuvant was effective in enhancing influenza vaccine efficacy in aged mice equivalent to human beings in their late 50s to early 60s. Using the influenza pandemic 2009 H1N1 (pdm09) vaccine as a model, we found that the CpG 1018 adjuvant could significantly enhance the pdm09 vaccine-induced serum antibody titer, while the pdm09 vaccine alone failed to elicit significant antibody titer. In contrast, the pdm09 vaccine alone elicited significant antibody titer in young adult mice. Antibody subtype analysis found that the pdm09 vaccine alone elicited Th2-biased antibody responses in young adult mice, while incorporation of the CpG 1018 adjuvant promoted the elicitation of potent Th1-biased antibody responses in aged mice. The pdm09 vaccine alone was further found to induce significant expansion of Th2 cells in young adult mice, while incorporation of the CpG 1018 adjuvant stimulated significant expansion of Th1 cells in aged mice. The CpG 1018 adjuvant also stimulated vaccine-specific cytotoxic T lymphocytes in aged mice. The pdm09 vaccine in the presence of CpG 1018 elicited significant protection against lethal viral challenges, while the pdm09 vaccine alone failed to confer significant protection in young adult or aged mice. Our study provided strong evidence to support the high effectiveness of the CpG 1018 adjuvant to boost influenza vaccination in aged mouse models.

Vaccine delivery technologies are mainly designed to minimally invasively deliver vaccines to target tissues with little or no adjuvant effects. This study presents a prototype laser-based powder delivery (LPD) with inherent adjuvant effects for more immunogenic vaccination without incorporation of external adjuvants. LPD takes advantage of aesthetic ablative fractional laser to generate skin microchannels to support high-efficient vaccine delivery and at the same time creates photothermal stress in microchannel-surrounding tissues to boost vaccination. LPD could significantly enhance pandemic influenza 2009 H1N1 vaccine immunogenicity and protective efficacy as compared with needle-based intradermal delivery in murine models. The ablative fractional laser was found to induce host DNA release, activate NLR family pyrin domain containing 3 inflammasome, and stimulate IL-1β release despite their dispensability for laser adjuvant effects. Instead, the ablative fractional laser activated MyD88 to mediate its adjuvant effects by potentiation of antigen uptake, maturation, and migration of dendritic cells. LPD also induced minimal local or systemic adverse reactions due to the microfractional and sustained vaccine delivery. Our data support the development of self-adjuvanted vaccine delivery technologies by intentional induction of well-controlled tissue stress to alert innate immune systems for more immunogenic vaccination.

Due to their location, malignant brain tumors are one of humanity's greatest killers, among these tumors, gliomas are the most common. The early detection of gliomas can contribute to the design of proper treatment schemes and, thus, improve the survival rate of patients. However, it is a challenging task to detect the gliomas within the complex structure of the brain. The conventional artificial diagnosis is time-consuming and relies on the clinical experience of radiologists. To detect gliomas more efficiently, this paper proposes a noninvasive automatic diagnosis system for gliomas based on the machine learning methods. First, image standardization, including size normalization and background removal, is applied to produce standard images; then, the modified dynamic histogram equalization is implemented to enhance the low-contrast standard brain images, and skull removal based on outlier detection is presented. Furthermore, hybrid features, including gray-level co-occurrence matrix, pyramid histogram of the oriented gradient, modified completed local binary pattern, and intensity-based features are extracted together from the enhanced images, and their dimensions are reduced by principal component analysis. Kernel support vector machine (KSVM) combined with the particle swarm optimization is eventually adopted to train classifiers; in this paper, brain magnetic resonance imaging images are labeled with normal, glioma, and other. The experimental results show that the accuracy, sensitivity, and specificity of the proposed method can reach 98.36%, 99.17%, and 97.83%, respectively, which indicates that the proposed method performs better than many current systems.

In this paper, a parallelogram set-membership estimation (PSME) algorithm is proposed to perform osteotomy trajectory estimation in two dimension plane with high accuracy to achieve high-precision osteotomy by an orthopedic surgery robot and meet the safety requirements of the osteotomy. First, to realize the tight envelope of the estimated set on the osteotomy trajectory, a parallelogram envelopment expression is proposed that describes the state set and the observation set. Second, a minimum-area parallelogram envelope method is applied to quickly converge the osteotomy trajectory estimation set to a reliable range. Moreover, the unknown but bounded noise model solves the robustness problem of the algorithm under non-Gaussian conditions, and realizes the accurate estimation of the osteotomy trajectory in any noise environment. Finally, the simulated and experimental results demonstrate that the estimation accuracy and anti-noise performance of the PSME algorithm are better than other estimation algorithms. In the osteotomy experiment, the average osteotomy error is less than 1 mm, which meets the safety requirements of the osteotomy. Furthermore, PSME holds great potential in other estimation problems.

Electrowetting is a widely used and effective method to tune the wettability of ionic liquids at solid-liquid interfaces, but it usually requires an external electric field. Here, we proposed a strategy for conveniently tuning ionic liquid wettability by adopting ferroelectric LiNbO3 single crystals as functional substrates. A heating pretreatment process was applied to modulate the surface charge characteristics of LiNbO3 substrates, leading to an improved wettability of [EMIM][BF4] and [EMIM][NTf2] on the LiNbO3 substrates with both positively poled (+Z) and negatively poled (−Z) surfaces. This work may be of great interest in the field of ferroelectric-based microelectronics.

Vaccine adjuvants ensure sufficient engagement of the immune system in vaccination, however safety issues can be associated with novel chemical adjuvants. Here, Cao et al. report a physical radiofrequency adjuvant to simultaneously augment vaccine-induced humoral and cellular immune responses without potentially harmful adverse reactions.

Nucleoprotein (NP) and matrix protein 1 (M1) are highly conserved among influenza A viruses and have been attractive targets to develop vaccines to elicit cross-reactive cytotoxic T lymphocytes (CTLs). Yet, external antigens are often presented on major histocompatibility complex class II molecules and elicit humoral immune responses. In this study, we present a physical radiofrequency adjuvant (RFA) to assist recombinant NP and M1 to elicit potent CTL responses. We found recombinant NP/M1 immunization in the presence of RFA could elicit potent anti-NP CTLs and confer significant protection against homologous viral challenges, while NP/M1 immunization alone failed to elicit significant CTL responses or confer significant protection. Interestingly, RFA failed to elicit potent anti-M1 CTL responses or anti-NP or anti-M1 antibody responses. Different from RFA, AddaVax adjuvant was found to significantly increase NP-specific antibody responses but not CTLs. NP/M1 immunization in the presence of RFA or AddaVax similarly reduced body weight loss, while only the former significantly increased the survival. We further found NP/M1 immunization in the presence of RFA did not significantly increase serum IL-6 release (a systemic inflammatory mediator) and rather reduced serum IL-6 release after boost immunization. NP/M1 immunization in the presence of RFA did not induce significant local reactions or increase body temperature of mice. The high potency and safety strongly support further development of RFA-based recombinant NP/M1 vaccine to elicit cross-protective immunity.

This article focuses on the relationship between Fintech and bank risk-taking behavior. Since Robo-Advisor is one of the mature applications of Fintech, we found that the development of Fintech will have a greater impact on small and medium-sized banks through the establishment of a Robo-Advisor model. This paper uses a benchmark regression model to analyze the municipal digital financial inclusion index compiled by Peking University and the annual report data of 155 small and medium-sized banks from 2011 to 2016. We found that the development of Fintech has significantly reduced bank risk-taking level. This result is still valid after the robustness test of replacing the bank’s risk-taking index and replacing the Fintech development index. We used the urban innovation index as an instrumental variable to deal with the endogenous problem, and obtained consistent estimation results. The test of the intermediary effect shows that the development of Fintech will affect the bank risk-taking through channels such as the bank’s internal interest margin, management capabilities, the bank’s external competition intensity, and residents’ saving willingness. Heterogeneity analysis shows the reduction effect of Fintech on bank risk-taking is more pronounced in banks in eastern and western regions in China, the large banks and the urban commercial banks.

The reproductive function of G-protein subunit Galphaq (GNAQ), a member of the G protein alpha subunit family, has been extensively studied in humans and rats. However, no data is available on its status in ruminants. The objectives of this study were to evaluate the expression pattern of the GNAQ in the testis and epididymis of sheep by polymerase chain reaction (PCR). The mRNA expression levels were detected by real-time fluorescent quantitative PCR, and cellular localization of GNAQ in the testis and epididymis was examined by immunohistochemistry. Additionally, GNAQ protein was qualitatively evaluated via western blot, with the results indicating that similarities between GNAQ mRNA levels from sheep was highly conserved with those observed in Bos taurus and Sus scrofa. Our results also indicated that GNAQ exists in the caput and cauda epididymis of sheep, while GNAQ in the testis and epididymis was localized to Leydig cells, spermatogonial stem cells, spermatocytes, Sertoli cells, spermatid, principal cells, and epididymis interstitial cells. The concentrations of GNAQ mRNA and protein in the caput and cauda epididymis were significantly greater than those observed in the corpus epididymis (p

How pancreatic β-cells acquire function in vivo is a long-standing mystery due to the lack of technology to visualize β-cell function in living animals. Here, we applied a high-resolution two-photon light-sheet microscope for the first in vivo imaging of Ca2+activity of every β-cell in Tg (ins:Rcamp1.07) zebrafish. We reveal that the heterogeneity of β-cell functional development in vivo occurred as two waves propagating from the islet mantle to the core, coordinated by islet vascularization. Increasing amounts of glucose induced functional acquisition and enhancement of β-cells via activating calcineurin/nuclear factor of activated T-cells (NFAT) signaling. Conserved in mammalians, calcineurin/NFAT prompted high-glucose-stimulated insulin secretion of neonatal mouse islets cultured in vitro. However, the reduction in low-glucose-stimulated insulin secretion was dependent on optimal glucose but independent of calcineurin/NFAT. Thus, combination of optimal glucose and calcineurin activation represents a previously unexplored strategy for promoting functional maturation of stem cell-derived β-like cells in vitro.

Influenza poses a huge threat to global public health. Influenza vaccines are the most effective and cost-effective means to control influenza. Current influenza vaccines mainly induce neutralizing antibodies against highly variable globular head of hemagglutinin and lack cross-protection. Vaccine adjuvants have been approved to enhance seasonal influenza vaccine efficacy in the elderly and spare influenza vaccine doses. Clinical studies found that MF59 and AS03-adjuvanted influenza vaccines could induce cross-protective immunity against non-vaccine viral strains. In addition to MF59 and AS03 adjuvants, experimental adjuvants, such as Toll-like receptor agonists, saponin-based adjuvants, cholera toxin and heat-labile enterotoxin-based mucosal adjuvants, and physical adjuvants, are also able to broaden influenza vaccine-induced immune responses against non-vaccine strains. This review focuses on introducing the various types of adjuvants capable of assisting current influenza vaccines to induce cross-protective immunity in preclinical and clinical studies. Mechanisms of licensed MF59 and AS03 adjuvants to induce cross-protective immunity are also introduced. Vaccine adjuvants hold a great promise to adjuvant influenza vaccines to induce cross-protective immunity.

High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform, Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application.