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Abstract Early racial disparities in COVID-19 vaccination rates have been attributed primarily to personal vaccine attitudes and behavior. Little attention has been paid to the possibility that inequitable vaccine distribution may have contributed to racial disparities in vaccine uptake when supplies were most scarce. We test the hypothesis that scarce vaccines were distributed inequitably using the shipping addresses of 385,930 COVID-19 vaccine doses distributed in the first 17 weeks of Pennsylvania’s Phase 1 rollout (December 14, 2020 through April 12, 2021). All shipments we analyze were allocated via the Federal Retail Pharmacy Program, a public-private partnership coordinated by the Centers for Disease Control and Prevention. Overall, White people had an average of 81.4% more retail pharmacy program doses shipped to their neighborhoods than did Black people. Regression models reveal that weekly vaccine allocations determined by pharmacy chains—rather than initial shipment and administration site decisions requiring state and federal approval—drove these effects. All findings remained consistent after controlling for neighborhood differences in income, population density, insurance coverage, number of pharmacies, and other social determinants of health. Our findings suggest that the private distribution of scarce public resources should be assessed for racial impact, regulated as public resources, and monitored continuously.

Most heavy metal ions are carcinogenic and non-biodegradable, posing threats to ecological balance and human health in trace amount. Therefore, there is a pressing demand for rapid and dependable detection technologies. Electrochemical sensing technology distinguishes itself with its ease of use and swiftness, rendering it perfect for the expeditious detection of heavy metal elements. This review examines various electrochemical detection techniques for on-site real-time monitoring of heavy metal ions. Advanced methods using innovative electrochemical sensor technologies are explored, highlighting the importance of sensing strategies for the quick and easy monitoring of metal levels in different environments. Additionally, the role of nanotechnology and electrochemical techniques in enhancing the sensitivity and selectivity of sensors for better detection of heavy metals is discussed. Finally, the future direction of sensor development is addressed, focusing on integrating new materials and technologies to improve the performance of sensor in environmental monitoring, food safety and public health.

The design and synthesis of drug molecules is a pivotal stage in drug development that traditionally requires significant investment in time and finances. However, the integration of artificial intelligence (AI) in drug design accelerates the identification of potential drug candidates, optimizes the drug development process, and contributes to more informed decision-making. The application of AI in molecular generation is changing the way researchers explore the chemical space and design novel compounds. It accelerates the process of drug discovery and materials science, enabling rapid exploration of the vast chemical landscapes for the identification of promising candidates for further experimental validation. The application of AI in predicting reaction products accelerates the synthesis planning process, contributes to the automation of synthetic chemistry tasks, and supports chemists in making informed decisions during drug discovery. This paper reviewed the recent advances in two interrelated areas: the application of AI in molecular generation and synthesis routes. It will provide insights into the innovative ways in which AI is transforming traditional approaches in drug development and predict its future progress in these key fields.

Abstract Planning and problem solving are cornerstones of higher brain function. But we do not know how the brain does that. We show that learning of a suitable cognitive map of the problem space suffices. Furthermore, this can be reduced to learning to predict the next observation through local synaptic plasticity. Importantly, the resulting cognitive map encodes relations between actions and observations, and its emergent high-dimensional geometry provides a sense of direction for reaching distant goals. This quasi-Euclidean sense of direction provides a simple heuristic for online planning that works almost as well as the best offline planning algorithms from AI. If the problem space is a physical space, this method automatically extracts structural regularities from the sequence of observations that it receives so that it can generalize to unseen parts. This speeds up learning of navigation in 2D mazes and the locomotion with complex actuator systems, such as legged bodies. The cognitive map learner that we propose does not require a teacher, similar to self-attention networks (Transformers). But in contrast to Transformers, it does not require backpropagation of errors or very large datasets for learning. Hence it provides a blue-print for future energy-efficient neuromorphic hardware that acquires advanced cognitive capabilities through autonomous on-chip learning.

Malic acid-derived polyamides, polyhydrazides, and hydrazides exhibit strong potential for a variety of biological applications. This study demonstrates the synthesis of cobalt, silver, copper, zinc, and iron particles by a facile chemical reduction approach utilizing malic acid-derived polyamides, polyhydrazides, and hydrazides as stabilizing and reducing agents. Comprehensive characterization of the particles was performed using UV–Vis spectroscopy, FTIR, XRD, SEM, and EDX analysis. The synthesized particles included both zero-valent metals and oxides exhibiting mixed-phase compositions that may influence their functional properties. UV–vis analysis confirmed the formation of particles represented by the surface plasmon resonance (SPR) peaks specific to each metal particle. FTIR spectroscopy revealed the interaction of the metal particles with the polymer matrix owing to the significant contribution of functional groups in the processes of reduction and stabilization. Further structural insights were obtained via X-ray diffraction (XRD), which identified crystalline phases, and scanning electron microscopy (SEM), which demonstrated uniform morphologies. Additionally, energy-dispersive X-ray (EDX) analysis provided compositional details, affirming the purity and distribution of metallic elements. These findings highlight the potential of malic acid-derived polymers as versatile agents for nanoparticle synthesis with applications in catalysis, sensing, and biomedical technologies.

Efficiently handling huge data amounts and enabling processing-intensive applications to run in faraway areas simultaneously is the ultimate objective of 5G networks. Currently, in order to distribute computing tasks, ongoing studies are exploring the incorporation of fog-cloud servers onto satellites, presenting a promising solution to enhance connectivity in remote areas. Nevertheless, analyzing the copious amounts of data produced by scattered sensors remains a challenging endeavor. The conventional strategy of transmitting this data to a central server for analysis can be costly. In contrast to centralized learning methods, distributed machine learning (ML) provides an alternative approach, albeit with notable drawbacks. This paper addresses the comparative learning expenses of centralized and distributed learning systems to tackle these challenges directly. It proposes the creation of an integrated system that harmoniously merges cloud servers with satellite network structures, leveraging the strengths of each system. This integration could represent a major breakthrough in satellite-based networking technology by streamlining data processing from remote nodes and cutting down on expenses. The core of this approach lies in the adaptive tailoring of learning techniques for individual entities based on their specific contextual nuances. The experimental findings underscore the prowess of the innovative lightweight strategy, LMAED2L (Enhanced Deep Learning for Earth Data Analysis), across a spectrum of machine learning assignments, showcasing remarkable and consistent performance under diverse operational conditions. Through a strategic fusion of centralized and distributed learning frameworks, the LMAED2L method emerges as a dynamic and effective remedy for the intricate data analysis challenges encountered within satellite networks interfaced with cloud servers. The empirical findings reveal a significant performance boost of our novel approach over traditional methods, with an average increase in reward (4.1 %), task completion rate (3.9 %), and delivered packets (3.4 %). This report suggests that these advancements will catalyze the integration of cutting-edge machine learning algorithms within future networks, elevating responsiveness, efficiency, and resource utilization to new heights.

Abstract Background Although previous studies have found a close relationship between sense of place and creativity, few studies have been conducted considering the micro-environment of the classroom. The mediating role of classmate relationships in the association between students’ sense of place and creativity remains unclear. Methods This study explores classmate relationships as a mediating factor in the relationship between sense of place and creativity. Therefore, we considered a sample of 1555 Chinese high-school students and used a paper-based questionnaire survey. Data analysis was performed using SPSS 24.0, PROCESS 3.2 plugin, and AMOS. Results Sense of place in the micro-environment of the classroom has a significant positive predictive effect on creativity. Sense of place also has a significant positive predictive effect on peer relationships. The mediation analysis reveals that peer relationships play a mediating role in the relationship between the sense of place and creativity. Conclusions This study revealed the associations between sense of place, classmate relationships, and creativity. Creativity is better expressed in students with a strong sense of place in the classroom. Moreover, a student’s sense of place can enhance their creativity by influencing their peer relationships. These findings enrich the research in educational psychology within the classroom, providing new insights for fostering creativity.

The exploration of microbial genetic resources for the production of fFen-flavor Baijiu has not only enriched the microbial library for baijiu production but has also laid the foundation for process improvement and strain optimization in baijiu brewing. In this study, a total of 177 fungal isolates were screened, including Saccharomyces cerevisiae, non-Saccharomyces cerevisiae, molds, and some pathogenic bacteria. Among them, Saccharomyces cerevisiae was the most abundant with 119 isolates, playing a major role in the fermentation of baijiu production. A total of 148 bacterial isolates were obtained from the fermentation mash samples, showing greater diversity compared to fungi. Bacillus species were the most abundant, with 94 isolates. Bacillus licheniformis, in particular, can produce a rich enzymatic system and flavor precursors, making it an important contributor to the sensory quality of baijiu. Lactic acid bacteria were the second most abundant, with 16 isolates. Additionally, five pathogenic fungal species were identified, including Candida pelliculosa, Candida lusitaniae, Fusarium oxysporum, Fusarium solani and Talaromyces marneffei. Six pathogenic bacterial species were also isolated, namely Staphylococcus aureus, Staphylococcus epidermidis, Staphylococcus xylosus, Moraxella osloensis, Actinomyces meyeri and Stenotrophomonas maltophilia. Finally, two strains of high acetate ethyl ester-producing yeast and lactate-degrading bacteria with good tolerance to temperature, pH, and ethanol concentration were identified as Saccharomyces cerevisiae and Bacillus licheniformis, respectively.

Abstract Purpose To evaluate the safety and the effectiveness of our novel penetrating keratoplasty for infectious keratitis. Methods Retrospective, noncomparative, interventional case series of patients with infectious keratitis who received the novel penetrating keratoplasty technique were analyzed. A prepared plastic sheet was located between the diseased cornea and iris-lens diaphragm. After the diseased lesions were removed, the graft was positioned on the plastic sheet and sutured to the recipient bed. The plastic sheet was pulled out from the anterior chamber before the all interrupted sutures were placed. The intra- and post-operative complications, the outcome of the graft and the number of corneal endothelial cells were analyzed. Results A total of 82 eyes of 82 patients was included. The mean follow-up period was 29 ± 16 months (range from 13 to 45 months). No intraocular content extrusion, simultaneous cataract extraction and suprachoroidal hemorrhage occurred. Direct contact between the infectious cornea and the graft was successfully avoided. Greater than expected endothelial cell reduction or complications were not found. Conclusions This modified technique effectively prevents the extrusion of intraocular contents while avoiding the direct contact with donor endothelium during the procedure. The occurrence rate of complications such as endothelial cell loss is not higher than the conventional methods.

Cutting-edge detection is a critical step in mechanized rice harvesting. Through visual cutting-edge detection, an algorithm can sense in real-time whether the rice harvesting process is along the cutting-edge, reducing loss and improving the efficiency of mechanized harvest. Although convolutional neural network-based models, which have strong local feature acquisition ability, have been widely used in rice production, these models involve large receptive fields only in the deep network. Besides, a self-attention-based Transformer can effectively provide global features to complement the disadvantages of CNNs. Hence, to quickly and accurately complete the task of cutting-edge detection in a complex rice harvesting environment, this article develops a Transformer Convolution Network (TCNet). This cutting-edge detection algorithm combines the Transformer with a CNN. Specifically, the Transformer realizes a patch embedding through a 3 × 3 convolution, and the output is employed as the input of the Transformer module. Additionally, the multi-head attention in the Transformer module undergoes dimensionality reduction to reduce overall network computation. In the Feed-forward network, a 7 × 7 convolution operation is used to realize the position-coding of different patches. Moreover, CNN uses depth-separable convolutions to extract local features from the images. The global features extracted by the Transformer and the local features extracted by the CNN are integrated into the fusion module. The test results demonstrated that TCNet could segment 97.88% of the Intersection over Union and 98.95% of the Accuracy in the unharvested region, and the number of parameters is only 10.796M. Cutting-edge detection is better than common lightweight backbone networks, achieving the detection effect of deep convolutional networks (ResNet-50) with fewer parameters. The proposed TCNet shows the advantages of a Transformer combined with a CNN and provides real-time and reliable reference information for the subsequent operation of rice harvesting.

The fermentation process of Chinese Baijiu’s fermented grains involves the intricate succession and metabolism of microbial communities, collectively shaping the Baijiu’s quality. Understanding the composition and succession of these living microbial communities within fermented grains is crucial for comprehending fermentation and flavor formation mechanisms. However, conducting high-throughput analysis of living microbial communities within the complex microbial system of fermented grains poses significant challenges. Thus, this study addressed this challenge by devising a high-throughput analysis framework using light-flavor Baijiu as a model. This framework combined propidium monoazide (PMA) pretreatment technology with amplicon sequencing techniques. Optimal PMA treatment parameters, including a concentration of 50 μM and incubation in darkness for 5 min followed by an exposure incubation period of 5 min, were identified. Utilizing this protocol, viable microorganism biomass ranging from 8.71 × 106 to 1.47 × 108 copies/μL was successfully detected in fermented grain samples. Subsequent amplicon sequencing analysis revealed distinct microbial community structures between untreated and PMA-treated groups, with notable differences in relative abundance compositions, particularly in dominant species such as Lactobacillus, Bacillus, Pediococcus, Saccharomycopsis, Issatchenkia and Pichia, as identified by LEfSe analysis. The results of this study confirmed the efficacy of PMA-amplicon sequencing technology for analyzing living microbial communities in fermented grains and furnished a methodological framework for investigating living microbial communities in diverse traditional fermented foods. This technical framework holds considerable significance for advancing our understanding of the fermentation mechanisms intrinsic to traditional fermented foods.

This research presents a new, eco-friendly, and swift method combining solid-phase extraction and hydrophobic deep eutectic solvents (DES) with high-performance liquid chromatography (SPE-DES-HPLC) for extracting and quantifying catechin and epicatechin in Shanxi aged vinegar (SAV). The parameters, such as the elution solvent type, the XAD-2 macroporous resin dosage, the DES ratio, the DES volume, the adsorption time, and the desorption time, were optimized via a one-way experiment. A central composite design using the Box–Behnken methodology was employed to investigate the effects of various factors, including 17 experimental runs and the construction of three-dimensional response surface plots to identify the optimal conditions. The results show that the optimal conditions were an HDES (tetraethylammonium chloride and octanoic acid) ratio of 1:3, an XAD-2 macroporous resin dosage of 188 mg, and an adsorption time of 11 min. Under these optimal conditions, the coefficients of determination of the method were greater than or equal to 0.9917, the precision was less than 5%, and the recoveries ranged from 98.8% to 118.8%. The environmentally friendly nature of the analytical process and sample preparation was assessed via the Analytical Eco-Scale and AGREE, demonstrating that this method is a practical and eco-friendly alternative to conventional determination techniques. In summary, this innovative approach offers a solid foundation for the assessment of flavanol compounds present in SAV samples.

Abstract Objective Purpose To observe the safety and effect of the C-pupilloplasty for the treatment of iris coloboma and traumatic iris defects. Methods A total of 21 cases (21 eyes) with iris coloboma or traumatic iris defects who underwent C-pupilloplasty (a single-pass three-throw technique) from Feb. 2016 to Mar. 2020 were analyzed retrospectively. Uncorrected visual acuity, refraction, corneal topographic keratometry and endothelial cell density were examined. Results All the patients were successfully treated, and a central and round pupil was restored. The mean follow-up duration was 8.76 ± 3.58 months (ranging from 2 to 14 months). All patients had round or round-like pupils with a diameter less than or equal to 3 mm after the C-pupilloplasty. Very slightly endothelial loss, negligible symptoms such as glare, distortion, dizziness and photophobia were observed. Conclusion We introduced a new technique of pupilloplasty (C-pupilloplasty) which could be a more straight forward and more effective treatment for iris coloboma and traumatic iris defect.

Abstract We investigated the prevalence and clinical metabolic characteristics of lean nonalcoholic fatty liver disease (NAFLD) in an elderly Chinese population and assessed the relevance of lipid markers and genetic variation. All 5,338 community subjects underwent detailed clinical and laboratory examinations and were divided into three groups: lean (Body mass index (BMI)

This study explored the tensile and shear characteristics of fully prefabricated partially composite floor slab joints through the design and testing of two tensile specimens, three steel–concrete specimens, and three concrete–concrete shear specimens. These tests aimed to evaluate how various connection designs influence the joints’ load-bearing capabilities and failure patterns. The findings revealed that the tensile specimens predominantly showed bond failures at the interface of the precast and cast-in-place layers, accompanied by rebar pull-out. Incorporating reinforcing bars or sleeves was found to potentially increase their ultimate load-bearing capacity by about 20%. The shear failures in the steel–concrete specimens were primarily due to interactions between the steel beam and adjacent composite slab, whereas the concrete–concrete specimens mostly underwent local crushing at the load application point and failure at the bonding interface. These observations affirmed the accuracy of the existing methods for calculating tensile and shear strengths, offering vital insights for the architectural design and construction of such floor joints.

Abstract Microvascular endothelial cells (MiVECs) impair angiogenic potential, leading to microvascular rarefaction, which is a characteristic feature of chronic pressure overload‐induced cardiac dysfunction. Semaphorin3A (Sema3A) is a secreted protein upregulated in MiVECs following angiotensin II (Ang II) activation and pressure overload stimuli. However, its role and mechanism in microvascular rarefaction remain elusive. The function and mechanism of action of Sema3A in pressure overload‐induced microvascular rarefaction, is explored, through an Ang II‐induced animal model of pressure overload. RNA sequencing, immunoblotting analysis, enzyme‐linked immunosorbent assay, quantitative reverse transcription polymerase chain reaction (qRT‐PCR), and immunofluorescence staining results indicate that Sema3A is predominantly expressed and significantly upregulated in MiVECs under pressure overload. Immunoelectron microscopy and nano‐flow cytometry analyses indicate small extracellular vesicles (sEVs), with surface‐attached Sema3A, to be a novel tool for efficient release and delivery of Sema3A from the MiVECs to extracellular microenvironment. To investigate pressure overload‐mediated cardiac microvascular rarefaction and cardiac fibrosis in vivo, endothelial‐specific Sema3A knockdown mice are established. Mechanistically, serum response factor (transcription factor) promotes the production of Sema3A; Sema3A‐positive sEVs compete with vascular endothelial growth factor A to bind to neuropilin‐1. Therefore, MiVECs lose their ability to respond to angiogenesis. In conclusion, Sema3A is a key pathogenic mediator that impairs the angiogenic potential of MiVECs, which leads to cardiac microvascular rarefaction in pressure overload‐induced heart disease.

Rice lodging is a crucial problem in rice production. Lodging during growing and harvesting periods can decrease rice yields. Practical lodging judgment for rice can provide effective reference information for yield prediction and harvesting. This article proposes a binocular camera-based lodging judgment method for rice in real-time. As a first step, the binocular camera and Inertial Measurement Unit (IMU) were calibrated. Secondly, Census and Grayscale Level cost features are constructed for stereo matching of left and right images. The Cross-Matching Cost Aggregation method is improved to compute the aggregation space in the LAB color space. Then, the Winner-Takes-All algorithm is applied to determine the optimal disparity for each pixel. A disparity map is constructed, and Multi-Step Disparity Refinement is applied to the disparity map to generate the final one. Finally, coordinate transformation obtains 3D world coordinates corresponding to pixels. IMU calculates the real-time pose of the binocular camera. A pose transformation is applied to the 3D world coordinates of the rice to obtain its 3D world coordinates in the horizontal state of the camera (pitch and roll angles are equal to 0). Based on the distance between the rice and the camera level, thresholding was used to determine whether the region to be detected belonged to lodging rice. The disparity map effect of the proposed matching algorithm was tested on the Middlebury Benchmark v3 dataset. The results show that the proposed algorithm is superior to the widely used Semi-Global Block Matching (SGBM) stereo-matching algorithm. Field images of rice were analyzed for lodging judgments. After the threshold judgment, the lodging region results were accurate and could be used to judge rice lodging. By combining the algorithms with binocular cameras, the research results can provide practical technical support for yield estimation and intelligent control of rice harvesters.

In this paper, we prepared a self-crosslinking amino-functional graphene oxide/styrene acrylic hybrid emulsion (AGO/SA). The styrene acrylic emulsion (SA) was synthesized with acetoacetoxyethyl methacrylate (AAEM) by semicontinuous seeded emulsion polymerization. Then, amino-functionalized graphene oxide (AGO) was prepared via the modification of graphene oxide (GO) with 3-aminopropyltriethoxysilane (APTES) and reacted with the SA emulsion as a crosslinking agent by in situ polymerization. Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray diffraction (XRD), thermal gravity analysis (TGA), and transmission electron microscopy (TEM) were applied to explore the chemical structures and morphologies of the samples. The anti-corrosion performance of the AGO/SA coatings was evaluated by Tafel polarization and electrochemical impedance spectroscopy (EIS). Along with the crosslink between AGO and SA, the micropores in the matrix were reduced, the coating expressed improved corrosion resistance due to the graphene barrier, and the hybrid emulsion with 0.7 wt.% AGO exhibited the best anti-corrosion properties. These results indicate that AGO can act as both a crosslinking agent and an anti-corrosion agent in self-crosslinking hybrid emulsions, which have great potential in the field of environmentally friendly emulsions.

Abstract Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. Methods Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. Results The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. Conclusions The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.

The monitoring of heavy metal ions content has positive significance for water resources quality monitoring and environmental protection. Herein, a tapered microfiber sensor functionalized polydopamine/4-mercaptopyridine (PDA/4-MPY) film for detection of trace mercury ion (Hg2+) concentration in water is proposed and demonstrated. The microfiber interferometer has excellent sensing performance including a refractive index (RI) sensitivity response of 1146 nm/RIU and a low-cross temperature response of −0.03 nm/℃. Hg2+ heavy metal ion is captured by the surface functional film to affect the RI of the microfiber surface, which lead to the wavelength shift of the interference spectrum. Experiments demonstrate that the sensor has an obvious regular response to Hg2+ in the range of 0–100 nM, in which the sensitivity reaches 1.015 nm/nM in the range of 0–10 nM with a high reliability (R2 = 0.99). This sensor has high sensitivity response and strong specific recognition to trace Hg2+ concentration under the advantages of simple fabrication and low cost. Furthermore, the success of the proposed sensor also indicates that the combination of microfiber and nanomaterial has a bright prospect in the field of optical detection.

The introduction of negative pressure wound therapy has revolutionized the management of complicated wounds. However, the maintenance of an effective negative pressure environment is difficult in some instances, such as wounds in close proximity to an intestinal stoma or wounds surrounded by external fixations. We found that adhesive tape adheres more easily to itself than to wet skin or uneven surfaces. Therefore, we placed adhesive tape around surgical wounds prior to covering them with foam and sealing them with more tape. As a result of the strong adhesive force between pieces of tape, this method could provide a better environmental seal, even in situations where space is limited. Pre-placed adhesive tape around the wound site prior to foam placement could provide sufficient adhesion to maintain a continuous negative pressure environment during treatment.

This study aimed to explore key physicochemical characteristics and evolutionary patterns of microbial community structure during the fermentation of aged vinegar. The correlation between microorganisms and physicochemical characteristics during fermentation was examined. The results revealed significant differences in genera at different stages of fermentation. The dominant bacteria in R1 were Bacillus, Lactobacillus, Aspergillus, and Issatchenkia. During the R2 fermentation stage, Lactobacillus, Acetobacter, and Saccharomyces exhibited an upward trend and finally became the dominant bacteria. Aspergillus was the main bacterial genus at the end of overall fermentation. The correlation analysis showed that the bacterial genera significantly positively and negatively correlated with reducing sugars and amino acid nitrogen were the same in Cuqu. Similarly, the bacterial genera significantly positively and negatively correlated with pH and saccharification power were the same. pH, reducing sugar, and saccharification ability were mainly positively correlated with bacterial genera during fermentation. Further, studies found that the overall correlation between fungal communities and physicochemical characteristics was weaker than the correlation with bacteria during fermentation.

In order to investigate the antioxidant activity of Elaeagnus umbellata polysaccharides, the physicochemical characteristics of purified Elaeagnus umbellata polysaccharides (EUP, consisting of two fractions, EUP1 and EUP2) were investigated using UV spectrophotometry, high-performance liquid chromatography (HPLC), high-performance gel permeation chromatography (HPGPC), and Fourier transform infrared spectroscopy (FT-IR). This revealed that EUP1 and EUP2 were acidic polysaccharides with an average molecular weight (MW) of 63 and 38 kDa, respectively. EUP1 mainly consisted of L-rhamnose and D-galactose in a molar ratio of 2.05:1, and EUP2 consisted of D-mannose, L-rhamnose, D-galactose, and D-arabinose in a molar ratio of 2.06:1:2.78:1. Furthermore, EUP exhibited considerable antioxidant potential for scavenging hydroxyl, superoxide anion, DPPH, and ABTS radicals. Therefore, EUP can be developed as a potential antioxidant for the functional food or pharmaceutical field.

Abstract Background Female sex hormone secretion and reproductive ability decrease with ageing. Bone marrow mesenchymal stem cells (BMMSCs) have been postulated to play a key role in treating ovarian ageing. Methods We used macaque ovarian ageing models to observe the structural and functional changes after juvenile BMMSC treatment. Moreover, RNA-seq was used to analyse the ovarian transcriptional expression profile and key pathways through which BMMSCs reverse ovarian ageing. Results In the elderly macaque models, the ovaries were atrophied, the regulation ability of sex hormones was reduced, the ovarian structure was destroyed, and only local atretic follicles were observed, in contrast with young rhesus monkeys. Intravenous infusion of BMMSCs in elderly macaques increased ovarian volume, strengthened the regulation ability of sex hormones, reduced the degree of pulmonary fibrosis, inhibited apoptosis, increased density of blood vessels, and promoted follicular regeneration. In addition, the ovarian expression characteristics of ageing-related genes of the elderly treatment group reverted to that of the young control group, 1258 genes that were differentially expressed, among which 415 genes upregulated with age were downregulated, 843 genes downregulated with age were upregulated after BMMSC treatment, and the top 20 differentially expressed genes (DEGs) in the protein-protein interaction (PPI) network were significantly enriched in oocyte meiosis and progesterone-mediated oocyte maturation pathways. Conclusion The BMMSCs derived from juvenile macaques can reverse ovarian ageing in elderly macaques.

This research presents a novel, eco-friendly, vortex-assisted liquid–liquid microextraction (VALLME) approach, integrating hydrophobic deep eutectic solvents (DESs) with HPLC for the identification and quantification of nine specific flavonoids in Shanxi aged vinegar (SAV). The parameters of DES-VALLME, including the ratio of trioctylmethylammonium chloride to 1,4-butanediol (1:6), DES volume (150 μL), vortex duration (5 min), the concentration of NaCl (0.40 g), and centrifugation time (10 min), were optimized to achieve the maximum extraction efficiency of target substances. Under these optimal conditions, quantitative analyses performed via HPLC demonstrated a broad linear range of 0.20–50.00 μg/mL and correlation coefficients (r2) greater than 0.9944 for all nine calibration curves. The limits of detection (LOD) and limits of quantitation (LOQ) were 0.09–0.18 μg/mL and 0.30–0.60 μg/mL, respectively, ensuring high sensitivity. The relative standard deviations for intra-day and inter-day variability were within the acceptable range, 2.34–3.77% and 3.04–4.96%, respectively, demonstrating the method’s reliability. The recovery rates ranged from 85.97% to 108.11%, underscoring the method’s precision. This technique exhibited a significant enrichment effect (enrichment factor: 43 to 296) on SAV flavonoids. Notably, the eco-friendliness of this procedure was evaluated using the Analytical Eco-Scale, Green Analytical Procedure Index, and Analytical Greenness Metric. The results suggested that this technique is a viable green alternative to traditional flavonoid determination methods in SAV. In summary, this novel method provides a theoretical basis for assessing flavonoid content in SAV samples and tracing SAV products. This contribution has significant implications for enhancing analytical techniques in food chemistry and environmental science and the sustainable development of the food industry.

Abstract Background To determine plasma ephrin-A1 and VEGF165 levels in a cohort of diabetic retinopathy patients. Methods Plasma ephrin-A1 and VEGF165 levels in fifty-five subjects including 19 individuals without diabetes (non-DM), 16 patients with diabetes (DM) but without diabetic retinopathy, and 20 patients with diabetic retinopathy (DR), were determined by ELISA. Serum creatinine, total cholesterol, fasting blood glucose and HbA1c were also measured. One-way ANOVA, Kruskal-Wallis Test, Mann-Whitney U Test corrected by Bonferroni, Pearson Correlation Analysis and Spearman Correlation Coefficient Analysis were used for data analysis. Results Ephrin-A1 expression could be detected in human plasma with an average of 1.52 ± 0.43 (mean ± SEM) ng/ml. In DR subjects, the plasma ephrin-A1concentration was 3.63 ± 4.63 ng/ml, which was significantly higher than that of the other two groups (non-DM: 0.27 ± 0.13 ng/ml, DM: 0.35 ± 0.34 ng/ml). The expression of VEGF165 in human plasma was 34.00 ± 42.55 pg/ml, with no statistical difference among the three groups. There was no correlation between ephrin-A1 and VEGF165 in human plasma, but there was a correlation between plasma ephrin-A1 and duration of diabetes. Conclusions Plasma ephrin-A1 was highly expressed in patients with diabetic retinopathy, and there was no difference of plasma VEGF165 expression in patients with diabetic retinopathy compared to the other two groups, suggesting that changes of plasma ephrin-A1 may be a more sensitive biomarker than plasma VEGF165 in detecting diabetic retinopathy.