Search Results (3,457)

Alkaptonuria (AKU) is a genetically determined disease associated with disorders of tyrosine metabolism. In AKU, the deposition of homogentisic acid polymers contributes to the pathological ossification of cartilage tissue. The controlled use of biomimetics similar to deposits observed in cartilage during AKU potentially may serve the development of new bone regeneration therapy based on the activation of osteoblasts. The proposed biomimetic is pyomelanin (PyoM), a polymeric biomacromolecule synthesized by Pseudomonas aeruginosa. This work presents comprehensive data on the osteoinductive, pro-regenerative, and antibacterial properties, as well as the cytocompatibility, of water-soluble (PyoM_sol) or water-insoluble (PyoM_insol) PyoM. Both variants of PyoM support osteoinductive processes as well as the maturation of osteoblasts in cell cultures in vitro due to the upregulation of bone-formation markers, osteocalcin (OC), and alkaline phosphatase (ALP). Furthermore, the cytokines involved in these processes were elevated in cell cultures of osteoblasts exposed to PyoM: tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-10. The PyoM variants are cytocompatible in a wide concentration range and limit the doxorubicin-induced apoptosis of osteoblasts. This cytoprotective PyoM activity is correlated with an increased migration of osteoblasts. Moreover, PyoM_sol and PyoM_insol exhibit antibacterial activity against staphylococci isolated from infected bones. The osteoinductive, pro-regenerative, and antiapoptotic effects achieved through PyoM stimulation prompt the development of new biocomposites modified with this bacterial biopolymer for medical use. Full article

Using panel data of Chinese cities from 2006 to 2020, this study constructs the carbon emission performance index from the perspective of the dual differences in the four stages of growth, maturity, decline and regeneration of eastern, central, western and resource-based cities (RBCs). This study employs the Dagum Gini coefficient and kernel density estimation to explore σ convergence and β convergence for understanding the dual differences, dynamic evolutionary trend and convergence. Results indicate that during the sample period, the carbon emission performance index of RBCs shows a fluctuating upward trend with regional and typological imbalance influenced by geographical location and division of labour. The carbon emission performance index of RBCs of different regions and types (Growing, Mature, Declining and Regenerative) shows a fluctuating downward trend. However, the carbon emission performance index gap between the 116 RBCs in China is gradually expanding, further corroborating the influence of “excellent but outliers”. The overall level of carbon emission performance index of RBCs exhibits σ convergence, absolute β convergence and conditional β convergence phenomena. Notably, growing and regenerative RBCs demonstrate a clear “catching-up” trend compared to mature and declining RBCs. Furthermore, the inclusion of control variables reveals varying degrees of increased convergence speed. Environmental regulation intensity (ERI), gross domestic product (GDP), energy consumption structure (ECS), technology development level (T), industrial structure (IS) and foreign direct investment demonstrate significant regional and type heterogeneity in the changes in the carbon emission performance index of RBCs. Finally, based on the analysis results, implications are proposed to enhance the carbon emission performance of RBCs of different types, as well as at the national and regional levels. Full article

With the rapid global growth in demand for renewable energy, the traditional energy structure is accelerating its transition to low-carbon, clean energy. Lithium-ion batteries, due to their high energy density, long cycle life, and high efficiency, have become a core technology driving this transformation. In lithium-ion battery energy storage systems, precise state estimation, such as state of charge, state of health, and state of power, is crucial for ensuring system safety, extending battery lifespan, and improving energy efficiency. Although physics-based state estimation techniques have matured, challenges remain regarding accuracy and robustness in complex environments. With the advancement of hardware computational capabilities, data-driven algorithms are increasingly applied in battery management, and multi-model fusion approaches have emerged as a research hotspot. This paper reviews the fusion application between physics-based and data-driven models in lithium-ion battery management, critically analyzes the advantages, limitations, and applicability of fusion models, and evaluates their effectiveness in improving state estimation accuracy and robustness. Furthermore, the paper discusses future directions for improvement in computational efficiency, model adaptability, and performance under complex operating conditions, aiming to provide theoretical support and practical guidance for developing lithium-ion battery management technologies. Full article

Time to maturity and yield are important traits for highbush blueberry (Vaccinium corymbosum) breeding. Proper determination of the time to maturity of blueberry varieties and breeding lines informs the harvest window, ensuring that the fruits are harvested at optimum maturity and quality. On the other hand, high-yielding crops bring in high profits per acre of planting. Harvesting and quantifying the yield for each blueberry breeding accession are labor-intensive and impractical. Instead, visual ratings as an estimation of yield are often used as a faster way to quantify the yield, which is categorical and subjective. In this study, we developed and shared a high-throughput phenotyping method using neural networks to predict blueberry time to maturity and to provide a proxy for yield, overcoming the labor constraints of obtaining high-frequency data. We aim to facilitate further research in computer vision and precision agriculture by publishing the labeled image dataset and the trained model. In this research, true-color images of blueberry bushes were collected, annotated, and used to train a deep neural network object detection model [You Only Look Once (YOLOv11)] to detect mature and immature berries. Different versions of YOLOv11 were used, including nano, small, and medium, which had similar performance, while the medium version had slightly higher metrics. The YOLOv11m model shows strong performance for the mature berry class, with a precision of 0.90 and an F1 score of 0.90. The precision and recall for detecting immature berries were 0.81 and 0.79. The model was tested on 10 blueberry bushes by hand harvesting and weighing blueberries. The results showed that the model detects approximately 25% of the berries on the bushes, and the correlation coefficients between model-detected and hand-harvested traits were 0.66, 0.86, and 0.72 for mature fruit count, immature fruit count, and mature ratio, respectively. The model applied to 91 blueberry advance selections and categorized them into groups with diverse levels of maturity and productivity using principal component analysis (PCA). These results inform the harvest window and yield of these breeding lines with precision and objectivity through berry classification and quantification. This model will be helpful for blueberry breeders, enabling more efficient selection, and for growers, helping them accurately estimate optimal harvest windows. This open-source tool can potentially enhance research capabilities and agricultural productivity. Full article

Citrus reticulata ‘Chachi’ (CRC), recognized for its considerable edible and medicinal significance, is a valuable source of metabolites beneficial to human health. This research investigates the metabolic distinctions and antioxidant properties across four different parts of CRC, using multivariate statistical analysis to interpret metabolomic data and network pharmacology to identify potential antioxidant targets and relevant signaling pathways. The results indicate considerable metabolic differences in different parts of the sample, with 1622 metabolites showing differential expression, including 816 secondary metabolites, primarily consisting of terpenoids (31.02%) and flavonoids (25.22%). The dried mature citrus peel (CP) section demonstrates the highest level of total phenolics (6.8 mg/g), followed by the pulp without seed (PU) (4.52 mg/g), pulp with seed (PWS) (4.26 mg/g), and the seed (SE) (2.16 mg/g). Interestingly, targeted high-performance liquid chromatography of flavonoids reveals the highest level of nobiletin and tangeretin in CP, whereas PU has the highest level of hesperidin, narirutin, and didymin. Furthermore, all four sections of CRC exhibit robust antioxidant properties in in vitro assessments (CP > PU > PWS > SE). Lastly, the network pharmacology uncovered potential antioxidant mechanisms in CRC. This research offers deeper insights into the development and utilization of byproducts in the CRC processing industry. Full article

Rice is the staple diet for most of the world’s population and is considered a major staple crop in China. Anhui province of China is among the leading provinces for rice production, consumption, and commodities; it is well-known as the “land of fish and rice”. Japonica rice cultivation in Anhui Province is mainly categorized into late-maturing medium, early-maturing medium, and early-maturing late japonica. This review explores the suitable ecological zone distribution and corresponding climate characteristics of the three types of japonica rice in Anhui Province. Data on japonica rice varieties approved in the province over the past twenty years were collected, illustrating the development process of japonica rice varieties in the province and their quality and resistance to rice blast disease. The review shows that the yield is positively and significantly correlated with agronomic traits, such as the number of effective panicles and the total number of grains per panicle, plant height, etc. In addition, it elucidates the major problems faced by Anhui’s japonica rice breeding and cultivation, such as frequent events of high temperatures, rice blast disease, and medium to low soil fertility levels. Considering the existing issues in breeding japonica rice varieties in Anhui Province, this review proposes a strategy for breeding high-yield and disease-resistant japonica rice varieties, particularly varieties adaptable to medium and low fertility soil conditions. In brief, this article provides a theoretical basis and practical recommendations for the sustainable development of japonica rice in the Anhui Province of China. Full article

Porcine epidemic diarrhea virus (PEDV) induces enteritis and diarrhea in piglets. Mitochondrial DNA (mtDNA) contributes to virus-induced inflammatory responses; however, the involvement of inflammasomes in PEDV infection responses remains unclear. We investigated the mechanism underlying inflammasome-mediated interleukin (IL)-1β secretion during the PEDV infection of porcine intestinal epithelial (IPEC-J2) cells. IL-1β production and caspase-1 activity were assessed by quantitative PCR and enzyme-linked immunosorbent assay. NLRP3 inflammasome activation was assessed using immunoprecipitation experiments. Mitochondrial damage was evaluated by analyzing the mitochondrial membrane potential and ATP levels and by the flow cytometry examination of mitochondrial reactive oxygen species (mtROS). Mitochondria and mtDNA localization were observed using immunofluorescence. The inhibition of mtROS and mtDNA production allowed NLRP3 inflammasome and IL-1β expression detection and the evaluation of the pathway underlying NLRP3 inflammasome activation in PEDV-infected IPEC-J2 cells. IPEC-J2 cells upregulated IL-1β upon PEDV infection, where mature IL-1β secretion depended on caspase-1 activity, triggered NLRP3 inflammasome expression and assembly, and caused mitochondrial dysfunction, leading to mtDNA release and NLRP3 inflammasome activation, while mtROS contributed to NF-κB pathway activation, enhancing IL-1β secretion. This is the first demonstration of the mechanism underlying mtDNA release and NLRP3 inflammasome activation facilitating IL-1β secretion from PEDV-infected IPEC-J2 cells. These data enhance our understanding of the inflammatory mechanisms triggered by PEDV. Full article

Hydrogen production technology is becoming increasingly important with the rapid development of hydrogen energy. Among existing hydrogen production technologies, alkaline water electrolysis (AWE) is drawing wide attention due to its advantages such as high maturity and low cost, and its performance analysis and optimization are important for applications. However, the AWE system contains processes with different physical and mathematical properties such as electrochemical reaction and heat transport processes, bringing difficulties to the system modeling. Moreover, the electrical and thermal processes have different characteristic time scales, and the system shows a sophisticated dynamic behavior, which has not been well studied yet. Here, a homomorphic dynamic model of the AWE system in the form of electrical circuit is built to describe the thermal and electrochemical processes uniformly, where the two parts are integrated via the energy conservation seamlessly. The model is verified by comparing with the experimental data and shows a high accuracy. The dynamic simulation analysis is conducted to investigate the dynamic response characteristics of the system under current step changes and fluctuations. The temperature overshoot and oscillation phenomena caused by delays in heat transport processes are studied. Results show that the time delay yields a maximum temperature overshoot of 10 °C, which would reduce the lifespan of the stack. This also highlights the importance of dynamic system analysis. Full article

Currently, the TAAR1 receptor has been identified in various cell groups in the intestinal wall. It recognizes biogenic amine compounds like phenylethylamine or tyramine, which are products of decarboxylation of phenylalanine and tyrosine by endogenous or bacterial decarboxylases. Since several gut bacteria produce these amines, TAAR1 is suggested to be involved in the interaction between the host and gut microbiota. The purpose of this present study was to clarify the TAAR1 function in the intestinal wall and estimate the TAAR1 gene knockout effect on gut microbiota composition. By analyzing public transcriptomic data of the GEO repository, we identified TAAR1 expression in enterocytes, enteroendocrine cells, tuft cells, and myenteric neurons in mice. The analysis of genes co-expressed with TAAR1 in enteroendocrine cells allows us to suggest the TAAR1 involvement in enteroendocrine cell maturation. Also, in myenteric neurons, we identified the co-expression of TAAR1 with calbindin, which is specific for sensory neurons. The 16S rRNA gene-based analysis of fecal microbiota revealed a slight but significant impact of TAAR1 gene knockout in mice on the gut microbial community, which manifests in the higher diversity, accompanied by low between-sample variability and reorganization of the microbial co-occurrence network. Full article

The traditional practice of aging wines in oak barrels has long been associated with the evolution of wine aromas. However, due to rising costs, alternative approaches like aging with oak chips have gained popularity. The aging time, addition dose, and type of toasting of the oak chips are critical parameters affecting the quality of the wine’s aroma. In this study, we focus on wines from Agiorgitiko variety and explore the impact of oak chip maturation on both volatile composition and sensory profile. By analyzing volatile compounds of wine aroma using GC-MS/MS and conducting descriptive sensory analysis, we investigate the effects of three different oak chip toasting levels, three dosages, and three aging periods. Our findings reveal that almost all wines aged with oak chips exhibit higher ester concentrations compared to the control. Notably, heavily toasted oak chips contribute to the sensory attribute of smoky aroma, while medium oak chips are associated with the sensory attribute of barrel aroma. This study provides valuable data for winemakers to determine the most suitable application for their product. Full article

Background: Small vulnerable newborns (SVNs), including those born preterm, small for gestational age, or with low birth weight, are at higher risk of neonatal mortality and long-term health complications. Early exposure to maternal vaginal microbiota and breastfeeding plays a critical role in the development of the neonatal microbiota and immune system, especially in low-resource settings like Burkina Faso, where neonatal mortality rates remain high. Objectives: The DenBalo study aims to investigate the role of maternal and neonatal factors, such as vaginal and gut microbiota, immune development, and early nutrition, in shaping health outcomes in SVNs and healthy infants. Methods: This prospective cohort observational study will recruit 141 mother-infant pairs (70 SVNs and 71 healthy controls) from four health centers in Bobo-Dioulasso, Burkina Faso. The mother-infant pairs will be followed for six months with anthropometric measurements and biospecimen collections, including blood, breast milk, saliva, stool, vaginal swabs, and placental biopsies. Multi-omics approaches, encompassing metagenomics, metabolomics, proteomics, and immune profiling, will be used to assess vaginal and gut microbiota composition and functionality, immune cell maturation, and cytokine levels at critical developmental stages. Conclusions: This study will generate comprehensive data on how microbiota, metabolomic, and proteomic profiles, along with immune system development, differ between SVNs and healthy infants. These findings will guide targeted interventions to improve neonatal health outcomes and reduce mortality, particularly in vulnerable populations. Full article

The use of summarized spectral data in bands obtained by hyperspectral sensors can make it possible to obtain biochemical information about seeds and, thus, relate the results to seed viability and vigor. Thus, the hypothesis of this work is based on the possibility of obtaining information about the physiological quality of seeds through hyperspectral bands and distinguishing seed lots regarding their quality through wavelengths. The objective was then to evaluate the possibility of differentiating soybean genotypes regarding the physiological quality of seeds using spectral data. The experiment was conducted during the 2021/2022 harvest at the Federal University of Mato Grosso do Sul in a randomized block design with four replicates and 10 F3 soybean populations (G1, G8, G12, G15, G19, G21, G24, G27, G31, and G36). After the maturation of each genotype, seeds were harvested from the central rows of each plot, which consisted of five one-meter rows. Seed samples from each experimental unit were placed in a Petri dish to collect spectral data. Readings were performed in the laboratory at a temperature of 26 °C and using two 60 W halogen lamps as the light source, positioned 15 cm between the sensor and the sample. The sensor used was the Ocean Optics (Florida, USA) model STS-VIS-L-50-400-SMA, which captured the reflectance of the seed sample at wavelengths between 450 and 824 nm. After readings from the hyperspectral sensor, the seeds were subjected to tests for water content, germination, first germination count, electrical conductivity, and tetrazolium. The data obtained were subjected to an analysis of variance and the means were compared by the Scott–Knott test at 5% probability, analyzed using R software version 4.2.3 (Auckland, New Zealand). The data on the physiological quality of the seeds of the soybean genotypes were subjected to principal component analysis (PCA) and associated with the K-means algorithm to form groups according to the similarity and distinction between the genetic materials. After the formation of these groups, spectral curve graphs were constructed for each soybean genotype and for the groups that were formed. The physiological quality of the soybean genotypes can be differentiated using hyperspectral bands. The spectral bands, therefore, provide important information about the physiological quality of soybean seeds. Through the use of hyperspectral sensors and the observation of specific bands, it is possible to differentiate genotypes in terms of seed quality, complementing and/or replacing traditional tests in a fast, accurate, and non-destructive way, reducing the time and investment spent on obtaining information on seed viability and vigor. The results found in this study are promising, and further research is needed in future studies with other species and genotypes. The interval between 450 and 649 nm was the main spectrum band that contributed to the differentiation between soybean genotypes of superior and inferior physiological quality. Full article

The non-destructive detection of fruit quality is indispensable in the agricultural and food industries. This study aimed to explore the application of hyperspectral imaging (HSI) technology, combined with machine learning, for a quality assessment of pears, so as to provide an efficient technical method. Six varieties of pears were used for inspection, including ‘Sucui No.1’, ‘Zaojinxiang’, ‘Huangguan’, ‘Akizuki’, ‘Yali’, and ‘Hongli No.1’. Spectral data within the 398~1004 nm wavelength range were analyzed to compare the predictive performance of the Least Squares Support Vector Machine (LS-SVM) models on various quality parameters, using different preprocessing methods and the selected feature wavelengths. The results indicated that the combination of Fast Detrend-Standard Normal Variate (FD-SNV) preprocessing and Competitive Adaptive Reweighted Sampling (CARS)-selected feature wavelengths yielded the best improvement in model predictive ability for forecasting key quality parameters such as firmness, soluble solids content (SSC), pH, color, and maturity degree. They could enhance the predictive capability and reduce computational complexity. Furthermore, in order to construct a quality prediction model, integrating hyperspectral data from six pear varieties resulted in an RPD (Ratio of Performance to Deviation) exceeding 2.0 for all the quality parameters, indicating that increasing the fruit sample size and variety number further strengthened the robustness of the model. The Backpropagation Neural Network (BPNN) model could accurately distinguish six distinct pear varieties, achieving prediction accuracies of above 99% for both the calibration and test sets. In summary, the combination of HSI and machine learning models enabled an efficient, rapid, and non-destructive detection of pear quality and provided a practical value for quality control and the commercial processing of pears. Full article

Filling post-extraction alveoli with hydroxyapatite-based materials is becoming an increasingly common procedure in veterinary dentistry. In dogs, tooth roots vary in structure depending on the weight of the dog, but data on tooth length and volume have not yet been described. This study aimed to establish reference data on tooth root length and post-extraction alveolar volume for mature maxillary and mandibular incisors and canines in dogs. We determined the mean length and volume of these teeth in dogs in the weight ranges of 1–5 kg, 5–10 kg, 10–20 kg, and over 20 kg. The obtained values given showed a correlation between tooth length and alveolar volume in a specific weight range. A review of the commercially available hydroxyapatite-based bone substitute materials was then conducted. A table is presented which shows how to calculate the volume of bone substitute material required to fill a post-extraction alveolus with a given material. Statistics were used to assess significant differences between the mass of the bone substitute product used (μL) for specific weight ranges and to demonstrate the correlation between tooth length and alveolar volume for a specific weight range. The data obtained in this study can serve as reference values for tooth crown length and alveolar volume, allowing operators to plan a specific volume of bone substitute material for filling post-extraction alveoli. This research is interesting because it shows that the weight of an animal is an important aspect in planning the amount of bone substitute material for tooth extraction. In clinical work, it is much easier to weigh an animal than it is to make calculations based on the length of the tooth root. Full article

In recent years, biomarkers in granulosa cells (GC) have been determined and associated in several species with oocyte maturation, in vitro fertilization success, and embryo development outcomes. The identification of biomarkers of oocyte competence can aid in improving assisted reproductive technologies (ARTs) in the southern white rhino (SWR). This study aimed to identify biomarkers present in SWR GC associated with oocytes that either did or did not mature in vitro. We evaluated follicle development (FD), meiotic competence (MC), cell death and atresia (CDA), and embryonic genome activation (EGA). Our objective was to design biomarkers to predict oocyte in vitro maturation results in the SWR. RNA was isolated from GC obtained during ovum pick up (OPU) for qPCR analysis. Overall, 22 genes were assessed, and nine were differentially expressed between GC from oocytes that did or did not mature in vitro (FD-GDF9 and mTOR; MC-GGPS1, JMY, and NPR2; CDA-COL4A1, MACIR, and TMPO; EGA-NFYA). From these data, we determined that GC can be used as a predictor for oocyte in vitro maturation outcome in the SWR. Our results provide crucial information needed to improve in vitro maturation and ARTs in this species. Full article