Simultaneous profiling of multi-omics single-cell data represents exciting technological advancements for understanding cellular states and heterogeneity. Cellular indexing of transcriptomes and epitopes by sequencing allowed for parallel quantification of cell-surface protein expression and transcriptome profiling in the same cells; methylome and transcriptome sequencing from single cells allows for analysis of transcriptomic and epigenomic profiling in the same individual cells. However, effective integration method for mining the heterogeneity of cells over the noisy, sparse, and complex multi-modal data is in growing need.In this article, we propose a multi-modal high-order neighborhood Laplacian matrix optimization framework for integrating the multi-omics single-cell data: scHoML. Hierarchical clustering method was presented for analyzing the optimal embedding representation and identifying cell clusters in a robust manner. This novel method by integrating high-order and multi-modal Laplacian matrices would robustly represent the complex data structures and allow for systematic analysis at the multi-omics single-cell level, thus promoting further biological discoveries.https://github.com/jianghruc/scHoML.Matlab code is available at  DiffereWith the flourishing development of single-cell multi-omics technologies, a growing number of methods have been proposed for integrating multi-omics data. These methods can be categorized into two major types. A major type of methods was designed for multi-omics data measured in different cells. Representative methods include Seurat  proposedIn this article, we focus on integrative analysis of parallel multi-omics single-cell data. Most of the current methods mainly aim to model a common low-dimensional embedding or unified relationship between cells. Taking into consideration of the sparsity and nonlinearity nature of single-cell data, also inspired by the above findings, we propose a multi-modal high-order neighborhood Laplacian matrix optimization framework (scHoML) for integrating multi-omics single-cell data. The method is very flexible and robust, which can be applied for efficiently integrating multi-omics data both in simulation and real-world datasets generated by scM &T-seq, CITE-seq technologies, etc. The article is structured as follows. In Section 2, we present preliminary information on the framework for multi-omics data integration. Section 3 presents the method scHoML for integrating multi-omics data. Experimental results are presented in Section 4. Section 5 discusses the application capability of scHoML. Finally, Section 6 concludes the article.In this section, we provide preliminary information on the framework for multi-omics data integration.High-order networksn is the number of samples, d is the dimensionality of the attributes. We first present the high-order networks for the dataset X modeling the connectivity of the data through definitions of adjacent matrices in different orders Assume single modal dataset W1,where j, k) is connected if and only if vertex j is in the nearest neighbors of the vertex k.In the construction of the first-order adjacent matrix iW, we follow the similar concept in obtaining j is also similar to vertex k in a i − 1th-order connectivity. Let jth row of iW, high-order adjacent matrix iW satisfy the form of adjacent matrix.In the construction of high-order adjacent matrix High-order Laplacian matrixThe corresponding normalized Laplacian matrices in different orders can be derived based on ith-order degree matrix, a diagonal matrix and ith-order normalized Laplacian matrix can be defined asLet Multi-modal Laplacian matrixV modals ith modal and ith modal.Suppose we have paralleled profiled single-cell multi-omics data in ith-order normalized Laplacian matrix for the pth modal, i.e. ith-order adjacent matrix for pth modal single-cell data, ith-order degree matrix for the pth modal single-cell data.According to the steps in construction of high-order networks and high-order Laplacian matrix, we define V modals, we have in total U × V Laplacian matrices for different modals and different orders.Hence, in single-cell data composed of Multi-modal multi-order Laplacian matrix fusionIn multi-modal single-cell data integration, how to integrate the modal-specific Laplacian matrices to formulate a fused, appropriate Laplacian matrix is a central and critical problem.i, Taking into consideration on the modal-specific Laplacian matrices, we first integrate Laplacian matrices for specific order Second, incorporating high-order connectivity information embedded in the high-order Laplacian matrix, we propose the linear combination of Intuitively, we aim to approximate the fused Laplacian matrix In this section, we present the method for integrating multi-omics data, to optimize the fused Laplacian matrix, as well as obtaining the low-dimensional representation for the multi-omics data.L. In terms of multi-omics single-cell data, the proposed Laplacian matrix For single modal data, spectral clustering can be realized through solving optimization problem with given Laplacian matrix H as the low-dimensional embedding for the multi-modal data, and the optimization problem can be expressed as follows:How to automatically determine the parameters embedded in Laplacian matrix W1 that can be decomposed into eigen-matrix form High-order Laplacian matrix can model the hidden high-order connection information among data, but the value of order needs properly selected as too high order may distort the original relationship embedded in the dataset. Hence, we focus on integration of Laplacian matrix in first order and second order, to preserve global data structure in a better manner, as well as improving learning performance. However, the positive-semi-definite property of Here Λ is a diagonal matrix, and Taking into consideration on the nonconvexity of the above problem, we propose alternative optimization framework to solve the problem by updating each variable iteratively. For the convenience of optimization, we rewrite The optimization process consists of the following five steps:Updating Definea > 0, we can obtainIf a = 0, then we can deduce b = 0. In this case, we keep λ unchanged. That means if kλ represent the value of λ in kth iteration, we will let If Updating W: Given fixed W can be generated through the optimization problem as follows:where W of c eigenvectors of B  and O(n), respectively. Furthermore, to update μ, we need to solve a standard quadratic programming problem. Let ε be the precision of the result and V be the number of modals, the complexity of solving the quadratic programming problem is t iterations, the total complexity of our method is The computational complexity of scHoML is mainly caused by SVD decomposition when updating H through appropriate evaluation on the cellular relationships between cells.In the optimization of high-order neighborhood Laplace matrix, we simultaneously obtain a common low-dimensional embedding s and cell t  varied from 3 to 7.The rank of X1 and X2 were generated with X2 with dropouts were greater than 0.7, we set the values to be 1, otherwise 0. In total, there are 500 simulated cells with four different cell types, the number of attributes in modal 1 and 2 are 5000 and 2000, respectively.Similar to the above-mentioned procedures, dropouts on both Mouse embryonic stem cells (mESCs) data.The dataset was obtained from 77 mESCs, including 13 cells cultured in “2i” media and 64 serum-grown cells, which were profiled by parallel scM&T-seq technique .pbmc_inhouse data.It is a CITE-seq dataset extracted from a healthy donor under IRB approval from the University of Pittsburgh  was downloaded from 10× Genomics website. A total of 7865 human peripheral blood mononuclear cells (PBMCs) with 14 surface protein markers are included in the dataset in addition to matched scRNA-seq data. Cells with uncertain cell types were removed. In total, there are 6661 cells involved, containing seven different cell types: B cells, CD14+ monocytes, CD16+ monocytes, CD4+ T cells, CD8+ T cells, dendritic cells, and natural killer (NK) cells.We compare our proposed method scMoHL with state-of-the-art method scAI , for decSCbest: a single-view spectral clustering applied for all the views with the best clustering for output.r in the range of MSE  on the best H chosen (scAI is applied to each dataset 10 times with different seeds) in the comparisons. Besides, we also introduce consensus hierarchical clustering method on the matrix H obtained by scAI (scAI-hc). Regarding the cluster number, if the cluster number is a parameter as input including scAI-hc, we use the true cluster number for comparison. In simulation_data1, the true cluster number is 3, and the estimated cluster number by Leiden algorithm and scHoML are both 3. For simulation_data2, where the true cluster number is 4, the estimated cluster number by scHoML is 4. But for Leiden algorithm, the estimated cluster number is 5. mESC dataset has two different cell states, but in Leiden algorithm, the estimated cluster number is equal to the number of cells in the dataset, namely, 77. scHoML can accurately estimate the cluster number. For pbmc_inhouse and pbmc_10X data, where the true cluster numbers are 5 and 7, respectively, scAI-Leiden estimated the cluster number to be 13 and 77 instead. Overall, scAI-Leiden tends to overestimate the heterogeneous groups inside datasets.For performance evaluation, we introduced two popular measures: Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI) for comparing the clustering accuracy. Aside from scMoHL, all the compared algorithms demonstrate specific patterns, with no dominant methods.scAI demonstrates obvious superiority compared to SCbest, MSE, CoregSC, AASC, RMSC, AMGL, AWP, and OPMC for simulated datasets. But the performance of scAI is not satisfactory when applied in mESC and pbmc datasets. SCbest as a single-view clustering method performs in an unstable way, where the ARI values obtained for the two simulated datasets and mESC dataset are unsatisfactory, revealing the complex structure of multi-modals in the respected datasets. But it is interesting to see that on pbmc data, SCbest outperforms most of the compared methods including scAI, indicating that in this dataset, there exists some modal showing a clear relationship among data. MSE as a multi-modal integration method performs similarly as SCbest. In particular for simulation data2 when the single-view clustering result showing 0.2323 in ARI value on average, MSE cannot learn a better integration of modals, getting only 0.0111 in averaged ARI value. For AASC, the clustering result on mESC data is the best excluding scHoML, showing 0.7022 in averaged ARI value. The performance of RMSC and AMGL resembled with each other on simulated datasets. When applied on real-world datasets, RMSC and AGML cannot compete with scAI in ARI values on mESC data. RMSC and AGML show better performance than scAI in ARI values and NMI values on pbmc data. For AWP algorithm, the best clustering performance is achieved on pbmc_10X dataset, 0.7230 in ARI value. OPMC algorithm shows the best clustering performance in pbmc_inhouse data, while has poor discrimination power in other datasets.When we compare single-view clustering-based algorithm (SCbest) with the other multi-modal clustering methods, some conclusions can be made as follows. First, different modal may reveal the data in different perspectives, there are cases when some particular modal shows a clear relationship among data. Second, integration methods may not fully integrate the proper information embedded in modals, showing unsatisfactory result compared to single-view based clustering method. Taking CoregSC for example, the results in mESC data, pmbc_inhouse and pbmc_10X data are inferior to that of SCbest. Third, appropriate evaluation of the data relationship is of critical significance for entangling the heterogeneity described by multi-modals. Among all the compared methods, scHoML as a graph-based embedding method provides a better description on the relationship between cells, showing that the incorporation of high-order correlation contributes in a positive manner for relationship description.scAI demonstrates explicit superiority compared to other traditional multi-view data clustering methods for simulation datasets. For simulation datasets, scAI ranks the second best, slightly inferior to scHoML. In real-world datasets such as mESC data, scAI with consensus hierarchical clustering method ranks the third in clustering accuracy in terms of ARI and NMI values. It is interesting to see that in pbmc-inhouse data and pbmc-10X data, scAI is not satisfactory. The computational efficiency is also restricted in scAI when the datasets contain large population of cells. When we compare different clustering methods in conjunction with scAI, we have the following findings. scAI with consensus hierarchical clustering (scAI-hc) outperforms scAI with Leiden clustering (scAI-Leiden) in cellular population heterogeneity analysis in an overall manner. For simulation datasets, scAI-Leiden perform in a similar way with scAI-hc. While for real-world datasets, scAI-Leiden perform in a unsatisfactory way. One possible explanation may be that the resolution parameter play an important role in the performance of scAI-Leiden method, especially in tuning the number of clusters to be detected. In the experiments with resolution parameter 0.1, the tuned number of clusters is more reasonable compared to default resolution parameter 1 for many datasets. Hence we used resolution parameter 0.1 for performance illustration. Second, we further checked the influence of resolution parameter on the clustering performance for datasets as shown in n, which is In the comparison, scHoML shows the best performance for integration in both simulation datasets and real-world single-cell multi-omics data when the performance is evaluated in ARI and NMI measures. As a nonlinear relationship modeling framework, scHoML used Laplacian matrix to model the relationship in multi-modal single-cell data. In particular, the incorporation of high-order neighborhood Laplacian matrix in optimization contributes to a better description of the geometric structure of the complex multi-modal data. Besides, scHoML can robustly represent the noisy, sparse multi-omics data in a unified low-dimensional embedding space. The cluster number determination strategy with sample-specific Silhouette coefficient for small sample problems as well as variance-based statistical measure offers a flexible way for accurately estimating the intrinsic clusters in the data. However, the computational complexity would become an unavoidable issue if the involved number of cells is large, because the time complexity is proportional to the number of cells All the compared methods attempt to find a common low-dimensional embedding for the single-cell multi-omics data, hence we compared 2D visualization of aggregated low-dimensional embeddings by different methods to evaluate the embedding capabilities of the considered methods. All the figures are attached in H showing appropriate relationship in the cells, where the cells are almost distinguishable. scHoML demonstrates clear superiority in getting common embedding information for the two simulation datasets, and the cell subpopulations were clearly distinguishable in the low-dimensional space when using the aggregated data.As shown in H obtained by those methods failed to give play to the descriptive advantages of ADT features, but quite influenced by the noisy RNA data. scHoML tends to obtain the satisfactory common embedding for pbmc_inhouse data. Similar results can be discovered by pbmc_10X data.Take a further look at the intrinsic complexity of the multi-omics data, we analyze the tSNE plots for original data in all considered modals. Due to the inherent sparsity and noise in the data, the cells were not well separated in the scRNA-seq data and the scATAC-seq data using t-SNE, for simulation datasets as shown in subfigs (a) and (b) for simulation data1 and (c) and (d) for simulation data2 in From the computational results, we can confirm the robustness and effectiveness of scHoML in dealing with single-cell multi-omics data under different signal-to-noise ratio scenarios. In the following, we discuss the feasibility and effectiveness of multi-modal and high-order Laplacian matrix optimization in scHoML. While only considering one mode of single-cell data, we have the following observations. Different datasets demonstrate different characteristics. For simulation data1 (subfigs a and b), simulation data2 (subfigs c and d), and mESC data (subfigs e and f) as shown in Besides, we compared scHoML to a robust single-cell muti-omics integration method UnionCom  cannot be rejected at the 5% significance level. Here H = 1 indicates that the null hypothesis can be rejected at the 5% level. To be specific, when we consider hypothesis test on cluster 3 and the remaining clusters, in the ADT data with 14 attributes, we filter the differentially expressed attributes for cluster 3 through hypothesis testing. They are “CD16,” “CD56,” “CD45RA,” etc. It is well verified that “CD16” and “CD56” are classical markers for NK cells (t-test leads to a filtration of top-ranked genes “FCGR3A,” “GNLY,” “SPON2,” “FGFBP2,” and “TKTL1.” “FCGR3A,” according to UniProtKB/Swiss-Prot, mediates IgG effector functions on NK cells (t-test on cluster t-test” for clusters 1–7 are as follows: cluster 1: “CD8a,” “CD45,” “CD3,” “CD127,” “TIGIT”; cluster 2: “CD8a,” “CD45,” “TIGIT,” “PD-1,” “CD3”; cluster 3: “CD19,” “CD45,” “CD56,” “CD16,” “TIGIT”; cluster 4: “CD3,” “CD4,” “CD127,” “CD45,” “CD25”; cluster 5: “CD14”; cluster 6: “CD56,” “CD45,” “CD16,” “TIGIT,” “CD19”; cluster 7: “CD8,” “CD127,” “CD45,” “CD3,” “PD-1.” We can see that in the identified markers, cluster 1 and cluster 2 have many common markers, indicating that OPMC cannot distinguish the cells in a clear manner where some clusters share similar patterns. In AWP method, the markers identified using “t-test” for clusters 1–7 are as follows: cluster 1: “CD8a,” “CD127,” “CD3,” “TIGIT,” “PD-1”; cluster 2: “CD4,” “CD45,” “PD-1,” “CD127”; cluster 3: “CD14”; cluster 4: “CD19,” “CD45,” “CD56,” “CD16,” “TIGIT”; cluster 5: “CD19,” “CD45”; cluster 6: “CD16,” “CD56,” “CD45,” “TIGIT,” “CD15”; cluster 7: “CD3,” “CD4,” “CD127,” “CD45,” “CD15.” We can see that the identified markers in cluster 5 are a subset of markers, meaning that AWP cannot distinguish the cells in a clear manner as well. In scAI-hc, the markers identified using “t-test” for clusters 1–7 are as follows: cluster 1: none; cluster 2: “CD4,” “CD3,” “CD45,” “CD14,” “CD25,” “CD127,” “PD-1”; cluster 3: “CD56,” “CD45”; cluster 4: “CD56,” “CD45,” “CD19,” “CD16,” “TIGIT,” “CD15”; cluster 5: “CD45”; cluster 6: “CD14”; cluster 7: “CD8a,” “CD127,” “CD3,” “TIGIT,” “PD-1,” “CD45,” “CD15.” We can see that the identified markers in cluster 5 are a subset of markers cluster 3, the identified markers in cluster 3 are a subset of markers, cluster 4, cluster 2 and cluster 7 have a lot of common markers, indicating that scAI-hc cannot help distinguish the cells in a clear manner as well. In scHoML method, the markers identified using “t-test” for clusters 1–7 are as follows: cluster 1: “CD19,” “CD45”; cluster 2: “CD8a,” “CD127,” “CD3,” “CD45”; cluster 3: “CD16,” “CD56,” “CD45,” “TIGIT,” “CD15”; cluster 4: “CD3,” “CD4,” “CD127,” “CD45,” “CD25”; cluster 5: “CD14”; cluster 6: “CD8a,” “CD45,” “TIGIT,” “PD-1,” “CD3”; cluster 7: “CD19.” Cluster 7 only contains 0.02% number of cells. We can see that the identified markers in each cluster have distinct characteristics, for example, “CD16,” “CD56,” “TIGIT” in cluster 3 are typical biomarkers for NK cells, demonstrating the ability of scHoML in identifying meaningful biomarkers.We conduct statistical analysis to compare the differences between specific cluster and the remaining clusters by scHoML, to investigate the potential of scHoML in cellular state identification. We performed NK cells . A furthIn this study, we propose a multi-modal high-order neighborhood Laplacian matrix optimization framework for integrating the multi-omics single-cell data: scMoHL. scHoML can robustly model the complex data structures and represent the noisy, sparse multi-omics data in a unified low-dimensional embedding space. Experiments on simulated datasets as well as real single-cell multi-omics data reveal that scHoML faithfully aligned heterogeneous modalities. The embedded data can further be utilized for heterogeneity analysis as well as cellular state identification, expecting to shed light on intriguing studies for revealing significant mechanisms among cells.btad414_Supplementary_DataClick here for additional data file.


There was no difference in energy expenditure per unit of body mass versus additional energy per kilogram of body weight (p > 0.05). 3) No significant differences in changing exercise intensity were observed in the REV group compared to those in the RE group during the adjacent incremental load phases of △ (40%–0%) and △ (80%–40%) of load during and 30 min after exercise (p > 0.05). Our results suggest that 1) vibration training can increase energy expenditure during low-intensity training and excess post-exercise oxygen consumption, and improve the exercise intensity. 2) The effects on energy expenditure were the same for both weight-bearing and non-weight-bearing deep squats, up to 40% of body mass.From the perspective of energy expenditure, this study investigated the effects of whole-body vibration training on the energy metabolism of deep squats with different weights. Twenty-two healthy male college students with sports experiences were selected and randomly assigned to perform resistance exercise vibration (REV) or resistance exercise (RE) with varying loads two times per week for 4 weeks. Oxygen uptake and heart rate were measured before, during, and after exercises using a gas analyzer, and energy expenditure was calculated. The results showed the following: 1) the oxygen uptake and energy expenditure of the REV group were significantly higher than those of the RE group during and 30 min after exercise ( Does viThere are two main intervention factors in this experiment: one is the mechanical factor. According to Rittweger J et al., 30 Hz is a relatively suitable frequency stimulus for the human body during vibration training . BertuccIn this study, our results were statistically analyzed to demonstrate that vibration stimulation can increase additional energy consumption and deepen EPOC; with the increase in load, the intensity of energy consumption increased by vibration training is more than that increased by non-vibration training . On the According to previous studies , when thα to 0.05, and power to 0.8. We calculated that we needed to recruit at least 21 subjects. Therefore, we selected 22 healthy male college students as subjects  − (3.2255*CO2)2CHO = (4.585*CO2) − (1.7012*CO2)2FAT = (1.6946*COEE = (CHO*4) + (FAT*9)The formula  is then REE = 88.362+(13.397*body weight)+(4.799*height)−(5.677*age)To obtain the actual energy expended during exercise, the energy produced by resting metabolism during exercise (EE-REE) was subtracted from the total energy expenditure in each phase .REE = 88p-value was greater than 0.05, indicating that they followed a normal distribution. The p-value of Levene’s chi-square test was greater than 0.05, and the data were consistent with equal variance. Then, according to the sphericity test, if the p-value is greater than 0.05, it means that there is no correlation between the data on repeated measurements. If the p-value was less than 0.05, the correction was performed with Greenhouse–Geisser correction. The statistically significant level was p < 0.05. The highly significant level was p < 0.01. Then, we defined the “∆” notation to indicate the change in relative oxygen uptake between two different loading levels. For example, ∆ (40%–0%) denotes the change in relative oxygen uptake between 0% and 40% loads, while ∆ (80%–40%) denotes the change in relative oxygen uptake between 40% and 80% loads.SPSS 18.0 was used for statistical analysis, and results were expressed as mean ± standard deviation (X ± SD). To test whether energy expenditure, oxygen uptake, and other indices were statistically different under different loads in the vibration and non-vibration conditions, two-factor repeated measures ANOVA was used. Whenever two factors interacted, separate effects were identified, and when they did not, the main effects were identified and effect sizes were reported to highlight the separate effects. Each group’s data were tested by Shapiro–Wilk’s method and the p = 0.149), “load” , and no difference obtained from a statistical test due to “vibration stimulation” * “load” p = 0.143 is shown in Statistical analysis of the changes in oxygen uptake in different phases of the exercise process and the reliability of its data was conducted, according to confidence intervals . Total op = 0.000), “load” , and by “vibration stimulation"*"load”  had an interaction effect according to a statistical test (p < 0. 05). The oxygen uptake of the body gradually increased with the increase in load in both REV and RE groups, respectively, and there was a significant difference (p < 0.01) between the groups , “load” , and due to “vibration stimulation“*”load”  had an interaction effect, according to a statistical test (p < 0.05). Under the premise of controlling vibration stimulation, the oxygen uptake of the organism gradually increased with increasing load in both REV and RE groups, and there was a significant difference between them (p < 0.01). This indicates that vibration stimulation has an effect on oxygen uptake, and the oxygen uptake at 80% load was significantly greater than that at 40% and 20%.Total oxygen uptake within 30 min after exercise, “vibration stimulation” , ‘load’ , and by ‘vibration stimulation‘*’load’  had no interaction effect, according to a statistical test (p > 0.05). After 30 min since the end of the exercise, there was no significant difference in the oxygen uptake at 0%, 20%, 40%, and 80%, indicating a basic return to a resting state and the end of EPOC.Total oxygen uptake 30–60 min after exercise, ‘vibration stimulation’ , “load” , and by "vibration stimulation” * “load”  was not different according to a statistical test. This indicates that during the resting phase, all groups were at the same basal metabolic level as each other, and therefore, there was no significant difference.Statistical analysis of total energy expenditure in different phases of the exercise process was performed shown in . The totp = 0.000), “load” , and due to “vibration stimulation“*”load”  had an interaction effect according to a statistical test (p < 0.05). The energy expenditure of the body gradually increased with the increase in load in both REV and RE groups, respectively, and there was a significant difference between them (p < 0.01)  .p = 0.000), “load” , and due to “vibration stimulation“*”load”  had an interaction effect according to a statistical test (p < 0.05). The energy expenditure of the body gradually increased with the increase in load in both REV and RE groups, respectively, and there was a significant difference between them (p < 0.01). It indicates that the effect of high-intensity vibration exercise is significantly greater than that of low- and medium-intensity exercise.Total energy expenditure within 30 min after exercise, “vibration stimulation” , “load” , and by “vibration stimulation” * “load”  had no interaction effect, according to a statistical test (p > 0.05). It means that vibration training with different weights after 30 min of exercise did not meet statistical differences in energy expenditure.Total energy expenditure 30–60 min after exercise, “vibration stimulation”  and △ (80%–40%) incremental load phases, and the statistical results showed no significant difference between the two groups (p > 0.05). When comparing within the groups, the REV (p = 0.263) and RE groups (p = 0.219) showed no significant effect of the “weight” factor on the relative oxygen uptake (p > 0.05). When comparing between the △ (40%–0%) (p = 0.135) and △ (80%–40%) groups (p = 0.128), there was no significant difference in relative oxygen uptake by “vibration stimulation” (p > 0.05). With the increase in load and the application of vibration stimulation, the intensity of the change in oxygen uptake was not significant.To investigate the change in relative oxygen uptake between adjacent incremental loads during exercise shown in , a pairet-test was performed in both the △ (40%–0%) and △ (80%–40%) incremental load phases, and the statistical results showed no significant difference between the two groups (p > 0.05). When comparing within the groups, the REV (p = 0.303) and RE groups (p = 0.559) showed no significant effect of the “weight” factor on the relative oxygen uptake (p > 0.05). Comparison between the △(40%–0%) (p = 0.134) and △(80%–40%) groups (p = 0.157) showed no significant difference between “vibration stimulation” and relative oxygen uptake (p > 0.05).To investigate the change in relative oxygen uptake between adjacent incremental loads after exercise, a paired-samples t-test was performed in the incremental load △ (40%–0%) and △ (80%–40%) phases, and the statistical results showed that there was no significant difference between the two groups (p > 0.05). When comparing within the groups, the REV (p = 0.846) and RE groups (p = 0.772) showed that the “weight” factor did not have a significant effect on relative energy expenditure (p > 0.05). When comparing between the △ (40%–0%) (p = 0.055) and △ (80%–40%) groups (p = 0.207), it showed that there is no significant difference between “vibration stimulation” and relative energy expenditure (p > 0.05).To investigate the change in relative energy expenditure between adjacent incremental loads after exercise, a paired-samples The purpose of this study was to investigate the effect of whole-body vibration training on energy metabolism during deep squats of different intensities from the perspective of energy expenditure. Our results showed that vibration training can increase energy expenditure and post-exercise excess oxygen consumption during low-intensity training and improve exercise intensity.2 of the REV group was significantly higher than that of the RE group  and △ (80%–40%). This may be because the subjects were a group of people who were experienced in exercise and had a high recovery capacity of the body. Therefore, different individuals have different sensitivities to the load stimulus due to the difference in the strength level, which is also an important factor affecting the experimental results.The results of this experiment showed that VOThe main finding of this experiment is that the trend of energy expenditure is stable in each weight-bearing relative energy expenditure and does not cause a difference between the energy consumed per unit of body weight and the additional energy consumed per kilogram. The amount of energy consumed per kilogram per unit of body weight and external load is approximately equal, whether under vibrating or non-vibrating conditions. From this, it can be found that in addition to the energy generated by the active muscle firing contraction during work, there is also a large amount of energy consumption generated by other muscle groups in static contraction to maintain body stability . BecauseThe experimental results showed that the energy expenditure of the REV group was significantly higher than that of the RE group. The experiments concluded that the energy expenditure of continuous weighted deep squats with 40 Hz vibration training at 50% of the weighted body weight was greater for 2 mm amplitude than for no amplitude and at 4 mm amplitude . TherefoThe results of the present experiment are also in line with the conclusion of the aforementioned authors’ that as the weight becomes heavier, the intensity of change in the energy consumed by the body becomes greater . Even unRegarding the effect of energy expenditure, researchers found that the REV group had significantly higher energy expenditure than the RE group . In thisFrom our experimental results, it is found that as the load weight increases, the energy expenditure also increases , but theFirst, the generalizability to different populations and training levels is limited by the relatively small sample size of this study. Further research could be conducted by increasing the sample size and including more subjects from different backgrounds to better understand the effects of whole-body vibration training in different populations. Second, the duration of this study was relatively short. It only covered certain training cycles. Future studies may consider designing interventions of longer duration to understand the ongoing effects of long-term training in order to more fully evaluate the effects of whole-body vibration training. Additionally, measurements of physiological metrics, such as energy expenditure and excess oxygen consumption, were limited in this study. The effects of whole-body vibration training on other physiological measures and athletic performance, such as cardiorespiratory fitness, strength performance, and musculoskeletal adaptations, could be further investigated in future studies. Finally, only one specific vibration parameter and load variation was addressed in this study. Further studies could investigate the effects of different vibration parameters and loading modes on energy metabolism and training effects. This would help optimize whole-body vibration training protocols.First, the present study was conducted under controlled laboratory conditions with a rigorous experimental design and reliable data collection methods. This adds credibility to the study. Second, we examined the effects of vibration training from multiple perspectives by conducting a comprehensive analysis of the differences in energy expenditure between the REV and RE groups for different weight-bearing deep squat situations. In addition, the intervening factors in this study included both mechanical and extrinsic resistance load changes. This added to the complexity and scientific validity of the study. Finally, this study provides an efficient and easy-to-use exercise modality for the population wanting to lose fat. This provides practicality and guidance for real-world applications.Whole-body vibration training may increase energy expenditure during low-intensity deep squat exercise and excess oxygen consumption within 30 min of exercise. However, when performing weighted squat training under vibration or non-vibration conditions within 40% of body mass, the energy consumption effect per unit of body weight is consistent with that caused by additional weight per kilogram carried. In addition, vibration training can be implemented through simple operations that improve our exercise efficiency through ease of use. The vibration stimulation of the wobbly platform also creates an adaptive stimulus that is psychologically conducive to withstanding greater training loads. However, increasing intensity requires caution and gradual progress. Therefore, in daily exercise activities, for people trying to lose fat through mass sports, if they are tired of regular running and weight training, they can try vibration training, through which the lean body mass and fat loss compared to the traditional means of exercise will be better.


Cipollotto Nocerino) to obtain antioxidant-rich polyphenolic extracts. A Box–Behnken design (BBD) was used to assess the impact of microwave-assisted extraction (MAE) variables  on total polyphenol content (TPC) measured by Folin–Ciocalteu method and the antioxidant power determined by FRAP assay. Response surface methodology (RSM) was applied, and regression equations, analysis of variance, and 3D response curves were developed. Our results highlighted that the TPC values range from 0.76 to 1.43 mg GAE g−1 dw, while the FRAP values range from 8.25 to 14.80 mmol Fe(II)E g−1 dw. The optimal extraction conditions predicted by the model were 60 °C, 22 min, ethanol concentration 51% (v/v), and solvent volume 11 mL. These conditions resulted in TPC and FRAP values of 1.35 mg GAE g−1 dw and 14.02 mmol Fe(II)E g−1 dw, respectively. Furthermore, the extract obtained under optimized conditions was characterized by UHPLC-ESI-Orbitrap-MS analysis. LC/MS–MS platform allowed us to tentatively identify various compounds belonging to the class of flavonoids, saponins, fatty acids, and lipids. Finally, the ability of CN optimal extract to inhibit the intracellular reactive oxygen species (ROS) release in a hepatocarcinoma cell line using an H2O2-induced oxidative stress model, was evaluated. The results highlighted the potential of CN extract as a valuable source of polyphenols with significant antioxidant properties, suitable for various applications in the food and pharmaceutical industries.Many studies have explored the extraction of bioactive compounds from different onion solid wastes, such as bulb, skin, and peel. However, onion leaves have received limited attention despite their potential as a valuable source of nutraceutical compounds. This study aimed to valorise, for the first time, the agricultural waste in the form of spring onion leaves (CN,  Effectively managing and reducing this waste is crucial for promoting environmental sustainability, enhancing resource efficiency, and preventing food loss and waste3. Recycling and reusing packaging materials not only conserve resources but also reduce the volume of waste that ends up in landfills. Composting organic waste, such as crop residues and food processing by-products, helps produce nutrient-rich soil amendments and reduces reliance on synthetic fertilizers6.Implementing proper waste management practices plays a vital role in minimizing the sector’s environmental impact. Strategies such as recycling, composting, and optimizing packaging can significantly reduce waste generation7.Preventing food loss and waste is a fundamental aspect of building a more sustainable food system. By reducing food waste at various stages of the supply chain, valuable resources like water, energy, and land can be conserved, while also minimizing associated greenhouse gas emissionsAllium cepa L.) is an example of a widely consumed vegetable that contributes considerably to municipal and industrial wastes, consisting of onion skins, outer fleshy scales, roots, leaves and the apical and basal trimming of bulbs and are commonly known as onion solid wastes (OSW)9.Onion  variety. These leaves are characterized by intensely green colour, linear in shape, and end in a pointed tip. They constitute the primary by-product of CN, measuring approximately 15–30 cm in length, a size six to seven times larger than that of its bulb.The CN is a type of onion bulb that has been cultivated for over 2000 years in Campania region, especially in the areas of Pompeii-Nocera. It is characterized by several distinctive features. The harvested bulbs measure 2–4 cm, which classifies them as medium-small-sized onions. The bulb has a cylindrical shape and is flattened at the poles, with a slight thickening at the base of the leaves. The inner and outer layers of the bulb are completely white, and the flesh is succulent and sweet in taste. As a spring harvest onion (from March to June), it is primarily consumed fresh and does not have a high capacity for storage. The annual production is approximately 50,000 tons of fresh produce, resulting in a turnover exceeding 30 million euros. The CN has been granted the Protected Designation of Origin (PDO) status (Reg. CE n. 656/2008)23. Several studies have investigated the extraction of bioactive compounds from onion leaves, mainly using the conventional extraction technique26. These methods typically involve macerating the leaves in different solvents  at varying times and temperatures to optimize the extraction process. This method takes a lot of time, energy, and solvent during processing30.An enormous amount of OSW is generated in several countries. For example, in California, USA, approximately 100,000 tons of OSW are produced annually. Similarly, in the European Union, particularly in Spain, Holland, and the UK, about 500,000 tons of OSW are generated each year31. UAE technique has gained popularity due to its ability to reduce solvent consumption, shorten extraction time, and improve extraction yields. UAE operates through the mechanical and cavitation effects generated by ultrasonic waves, which enhance the mass transfer of targeted compounds by breaking down the cell walls of the plant material33. However, ultrasonication system has disadvantages such as being expensive, occurring undesirable changes in molecules and requiring optimization. Another environmentally friendly extraction technique is microwave-assisted extraction (MAE), which involves the irradiation of samples soaked in a solvent. In contrast to conventional extraction methods, microwave irradiation can directly heat the reactants and solvent by passing through the walls of the reaction container. MAE is widely used in laboratories due to its numerous advantages. It helps reduce energy consumption and the amount of organic solvents required, leading to a decrease in waste generation. Its ability to efficiently extract bioactive compounds makes it a valuable tool in the field of natural product extraction and has gained considerable attention in scientific research and industrial applications35.Recently, optimal conditions for ultrasound-assisted extraction (UAE) have been identified to obtain extracts from Welsh onion leaves. These extracts exhibit high polyphenol content and 2,2-Diphenyl-1-picrylhydrazyl (DPPH) scavenging activity38.However, the complexity of mass transfer and the limited depth of microwave irradiation, influenced by factors including temperature and microwave frequency, present challenges for upscaling the MAE process. Therefore, achieving scale-up of MAE for industrial applications requires an in-depth analysis of how various parameters affect extraction kinetics. Consequently, it is crucial to develop models that can predict the optimum MAE conditions2O2-induced intracellular reactive oxygen species (ROS) exerted by optimal CN extract were evaluated in a hepatocarcinoma cell line.In this current study, we assessed the phytochemical composition and antioxidant properties of CN leaves, aiming to unlock the potential value of this by-product (leaves) for nutraceutical, nutritional, and pharmacological uses. For these purposes, we developed and optimized an alternative method based on MAE for the recovery and isolation of bioactive compounds from CN leaves. A response surface methodology (RSM) through a Box–Behnken design (BBD) was applied, and model fit, regression equations, analysis of variance and 3D response curves were developed. Temperature (60–100 °C), time (5–25 min), extraction volume (6–12 mL) and ethanol concentration (40–80% v/v) were studied as the major parameters affecting the extraction efficiency and the antioxidant properties. A Box–Behnken design was adopted considering total phenolic content (TPC) and ferric reducing antioxidant power (FRAP) as responses. A Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) platform was employed to elucidate the polyphenol profile of CN extract, which was obtained under the optimal extraction conditions determined by developed model. Additionally, in vitro evaluation of cell safety and the quenching of HCipollotto (spring onions) Nocerino DOP”.Folin Ciocalteu’s reagent, gallic acid, sodium carbonate, 2,4,6-Trippyridyl-s-triazine (TPTZ), sodium acetate, acetic acid glacial, hydrochloric acid 37%, Iron(III) chloride hexahydrate, Iron(II) sulfate heptahydrate, 3-− at m/z 609. In MS2, these compounds exhibited a disaccharide moiety, and the loss of 324 Da resulted in the aglycone deprotonated ion at m/z 285. Peaks 13 and 15 were tentatively identified as isomer of Kaempferol attached to a single sugar moiety. The [M − H]− ion at m/z 447 corresponded to the molecular formula C21H20O11, and it produced fragment ions at m/z 285 [M − H-162]−. Compounds 17 and 34 were tentatively identified as kaempferol in positive and negative ionization mode, respectively.According to the negative fragmentation pattern, peaks peak 5 with [M + H]+ ion at m/z 627. Fragment ions at m/z 465 and at m/z 303 were observed, corresponding to the loss of a sugar moiety [M + H–162]+ and to the aglycone portion, respectively. This peak was identified as Quercetin 3,4ʹ-didihexoside. Peaks 8 and 14 showed a molecular ion at m/z 463 [M − H]−, they have been identified as isomers of Quercetin 3-O-hexoside. The deprotonated molecular ion further generated an ion at m/z 301 through the relative loss of sugar moiety (− 162 Da)61.The chromatogram of CN analyzed in positive ionization mode showed a 35 showed at m/z 327 [M − H]− a fragmentation ions at m/z 309 and m/z 229 produced by loss of water molecule and end-group HO-CH=CH(CH2)3CH3, respectively. This compound was tentatively identified as oxo-dihydroxy-octadecenoic acid (oxoDiHODE). A similar fragmentation pattern was also observed for 9,12,13-trihydroxy octadeca-7-enoic acid (TriHODE). Peaks 36, 37 and 39 which were detected at different retention times in the chromatogram, all exhibited ions at m/z 329 [M − H]ˉ. The MS/MS fragmentation pattern showed ions at m/z 311 [M − H–H2O]ˉ, 293 [M − H–H2O-H2O]ˉ and 229 [M – H–100]ˉ corresponding to the loss of water and end-group HO-CH=CH(CH2)3CH3, respectively68.Several fatty acids were identified in CN extract. Peak 52 and 53 exhibited the precursor ion at m/z 295, but the loss of a water molecule [M − H–18]− and the relative cleavage of the C=C bond adjacent to the hydroxyl group gave fragments at m/z 277 and m/z 195, leading to its tentative identification as 13-hydroxyoctadecadienoic acid (13-HODE)74.Chromatographic peaks LC-HRMS/MS in negative ionization mode, enabled the detection of five putative saponins in CN extracts. Saponins were observed as deprotonated formic acid (FA) adducts and the fragmentation pattern generally corresponding to the neutral loss of FA (46 Da) and/or glycosyl moieties, i.e. hexosyl (Hex), deoxyhexosyl (dHex), pentosyl (Pen) Table . In addi26 and 28 exhibited an [M − H]− ion at m/z 963 (C46H75O21) with MS/MS fragments at m/z 917, 771 and, 609 corresponding to the successive loss of formic acid (46 Da), deoxyhexosyl (C6H10O4) and hexosyl groups , respectively. Thus, after the loss of dHex + Hex + FA, the unresolved portion was tentatively identified as sapogenin IV4 , along with an additional hexosyl moiety. Based on these findings, these peaks were identified as Saponin 3-IV4-1.Peaks 49) showed a precursor ion at m/z 753 (C39H61O14) and generated MS/MS base fragment ions at m/z 707 and m/z 561 through the loss of formic acid (46 Da) and deoxyhexosyl (146 Da), respectively. Furthermore, a sequential cleavage of a pentosyl moiety (132 Da) resulted in a putative identification as sapogenin III3 65.Saponin 2-III3 (compound 47) and Dehydrophytosphingosine  were detected in the samples analyzed using positive ionization mode. Peak 47 showed a precursor ion at m/z 318 [M + H]+. The most common fragments associated with this molecule were observed after the loss of a water molecule, resulting in the fragment ion at m/z 300 [M + H–H2O]+. Subsequently, the loss of another water molecule, led to the formation of the m/z 282 fragment [M + H–H2O–H2O]+. A similar fragmentation pattern was observed for Dehydrophytosphingosine , where two consecutive losses of water molecules were observed, resulting in the formation of two fragments at m/z 298 [M + H–H2O]+ and m/z 280 [M + H–H2O–H2O]+.61.Phytosphingosine 3+) to Fe(TPTZ)2+ which is intensely in blue color at low pH77. Although these antioxidant assay are based upon well-known chemical reactions, this probably do not reflect the cellular physiological conditions78. Indeed, an antioxidant is not only a substance able to prevent another substrate from oxidation, but a molecule that protects the whole biological system from damages coming from oxidizing stressors80.FRAP test is a chemical method used to assess the antioxidant activity of a sample in vitro. It is based on the sample’s ability to reduce a Fe81. Hep G2 cells were incubated with CN extract for 24 h at different concentrations (1.56–200 µg/mL) followed by morphology evaluation and determination of cell mortality.For these reasons, we evaluated the antioxidant activity of CN in hepatocarcinoma cell line Hep G2 treated with hydrogen peroxide. Firstly, in vitro cytotoxicity of CN extract by measuring the cell viability of Hep G2 using MTT assay was evaluated. Hep G2 is a popular hepatic cell line used in a broad range of biochemical applications, including cytotoxic studies since it is widely employed as in vitro model to study liver functionsAs shown in Fig. 2O2.Once the cell safety of optimal CN extract had been demonstrated, we proceeded to evaluate its ability to reduce intracellular release of ROS, induced by hydrogen peroxide. Our data highlighted that CN optimal extract (50–25 µg/mL) significantly inhibited ROS release in a concentration dependent manner in Hep G2 cells treated with HBased on the obtained results, it can be concluded that spring onion leaves, which are considered an agricultural by-product, are a valuable source of antioxidant compounds. This finding suggests that they hold potential as functional ingredients for the production of new value-added products, such as functional foods and dietary supplements.Cipollotto Nocerino PDO, a typical Allium cepa cultivar from Campania Region, Italy.In the present study, we investigated, for first time, the nutraceutical potential of green onion stalks, a by-product of MAE platform was employed, leading to the valorisation of these residues, and enhancing the circular economy through improved waste management. For this purpose, BBD approach was effectively useful to maximize the extraction of TPC and FRAP from onion leaves.−1 dw and an antioxidant capacity as measured by the FRAP assay of 14.016 ± 0.24 mmol Fe(II)E g−1 dw.Optimal MAE conditions to extract antioxidant compounds from CN leaves were determined using RSM , and solvent volume of 11 mL). These conditions provided a TPC value of 1.351 ± 0.07 mg GAE g3D response curves showed that a moderate increase in ethanol concentration and higher extraction volume, coupled with extended extraction time and lower temperature, led to an enhanced yield of phenolic compounds and antioxidant activity in the final extracts.2O2. The results demonstrated a significant concentration-dependent inhibition of ROS release.A total of 63 compounds from various classes, including flavonoids, saponins, fatty acids, and lipids, were tentatively identified in the optimal CN extract using UHPLC-ESI-HR-MS/MS. Furthermore, we assessed the antioxidant potential of the CN extract on Hep G2 cells treated with HIn conclusion, our study highlighted that spring onion leaves, often overlooked as agricultural by-products, are indeed a valuable source of antioxidant compounds. They could be used as functional ingredients for value-added products like functional foods and dietary supplements, thus providing innovative solutions for health and nutrition, while also contributing to the mitigation of environmental issues.Supplementary Information.


Where and which countries should receive higher priority for improving inorganic fertilizer use in rice fields in sub-Saharan Africa (SSA)? This study addressed this question by assessing the spatial variation in fertilizer use and its association with rice yield and yield gap in 24 SSA countries through a systematic literature review of peer-reviewed papers, theses, and grey literature published between 1995 and 2021. The results showed a large variation in N, P, and K fertilizer application rates and rice yield and an opportunity for narrowing the yield gap by increasing N and P rates, especially in irrigated rice systems. We identified clusters of sites/countries based on nutrient input and yield and suggested research and development strategies for improving yields and optimizing nutrient use efficiencies. Further research is essential to identify the factors causing low fertilizer use and the poor association between its use and yield in rainfed systems.  •We reviewed inorganic fertilizer use and rice yield in 24 sub-Saharan African countries.•N and P fertilizer rates were higher in irrigated lowlands and arid zone.•Higher N and P rates were associated with a lower yield gap in irrigated rice systems.•Higher P application rates improved the partial factor productivity of applied N.•About 40% of the data points showed a high risk of soil nutrient mining. In production systems of major staple foods such as rice , and as a consequence, a large share of its population faces hunger . Being aectively . The aved upland . Furtherd upland  and thatd upland .Information on yield gaps can help identify regions with the greatest potential to increase current yield through the use of cost-effective agronomic practices and inputs. Likewise, quantifying fertilizer application rate at national, and regional levels is an essential component of fertilizer consumption analysis and demand projection as fertilizers often represent the major source of nutrient input in cropping systems .Although the rice yield gap in SSA has been analyzed in detail , 2017, i22.1https://scholar.google.com/). The searches have been done in English and French to identify studies published before August 20th, 2021 (the end date of our search). By combining the research terms as follow: “fertili*” AND “rice” AND “Africa” OR “NPK” OR “Urea” OR “fertilizer use efficiency” OR “paddy” OR “Oryza”, we compiled all research papers including original peer-reviewed papers, theses, and grey literature . To ensure comparability, the inclusion or the exclusion criteria were as follows: (i) survey or farmers' field trials carried out in smallholder farmers' fields in SSA where fertilizer management  was done according to local practices were included; (ii) studies reporting data on recommended application rates from research-managed trials conducted in research station or farmers' fields were excluded because they were considered not representative of farmers’ practices; (iii) studies reporting pot experiments were also excluded; (iv) Both grain yield data and fertilizer  application rates should be provided. Then, we removed duplicate studies and irrelevant literature. In addition, for Burkina Faso and Malawi, we retrieved and aggregated agronomic data from the national agricultural statistics dashboard , growing environments , N, P, and K application rates, typical N fertilizer, typical compound fertilizer, and grain yield. The dataset was enriched with information on the geographic position , region , agroecological zone  . We did .The final database consists of 235 data points (or observations) from 1995 to 2021 (collected from 23 published papers or datasets and 2 unpublished) . Here, a data point is the unit of analysis and refers to average data (fertilizer rate or yield) by site or country * season and/or year. To ensure data quality, we performed Rosner's test to identify outliers for N, P, K rates, and grain yield and removed extreme outliers  2.2−1) per unit of nutrient x applied (kg x ha−1). N, P, and K values are expressed on an elemental basis. Thus, PFP (kg grain kg−1) was calculated according to Equation For each data point (or observation), the partial factor productivity (PFP) of a given nutrient N (PFPN), P (PFPP), or K (PFPK) was calculated to show the grain yield  or water-limited potential yield (in rainfed systems) (Yw) and the average farmers’ grain yield (Ya). The relative yield gap (YG) was calculated as the ratio between the absolute yield gap and the potential yield (in irrigated lowland) or water-limited potential yield (in rainfed systems) (Yw) and expressed in percentage Equation .(2)YG=Yw2.3Descriptive statistics  were used to characterize fertilizer  application rates, grain yield, partial factor productivity (PFP), and relative yield gap at the country, geographic position, region, growing environment or AEZ levels. Differences between regions, AEZs, and growing environments for nutrient application rates, grain yield, partial factor of productivity (PFP), and relative yield gap were assessed using one-way analysis of variance (ANOVA) when data met the requested assumptions  and if not using non-parametric Kruskal-Wallis test. Wherever the ANOVA or the Kruskal-Wallis test were significant , post hoc tests  were performed for multiple comparisons of groups. For factors having only two modalities such as geographic position  and whose data did not follow a normal distribution, we assessed the difference using a Wilcoxon-Mann-Whitney test .nnet package  across growing environments. The pairwise association between variables was visualized using a correlogram. As the standard Pearson's correlation estimate is parametric  and can be heavily influenced by extreme values, we either computed the Spearman's rank correlation coefficient (ρ) for mitigating the outliers' concerns . The magWe conducted an in-depth analysis to assess the scope to increase actual yields by increasing N input and thereby identify priority intervention countries, regions, or growing environments. Thus, we performed a hierarchical agglomerative cluster analysis  using Eu3Our review yielded 235 data points from studies conducted between 1995 and 2020 in the three SSA regions: West Africa (171 data points), Central Africa (9 data points), and East and Southern Africa (55 data points). The data points were distributed in three rice-growing environments , five agroecological zones  A and acr3.1−1, respectively. About 8%, 11%, and 27% of data points reported no application of N, P, and K fertilizers , respectively. −1, 11 kg P ha−1, and 11 kg K ha−1) than in Central Africa (CA)  and East and Southern Africa (ESA)   than in rainfed systems (−1), respectively (−1), respectively (−1), respectively (−1 and 18 kg P ha−1) were higher than in humid, sub-humid and highlands zones  with an average of 49, 9, and 8 kg ha K ha−1) . Farmers systems . About 2ectively A. For P ectively B. And foectively C. In arids zones .Fig. 2CuPartial factor productivity of nutrients largely varied (CV ranging from 186 to 521%) with PFPP having the highest coefficient of variation (521%). Furthermore, significant differences were observed among regions but not among growing environments and AEZs . Overall3.2−1 across all sites, with a 57% coefficient of variation. The variability was higher in rainfed lowlands (53%) than in uplands (34%) and irrigated lowlands (28%). There was a significant difference in grain yield among the growing environments and AEZs , respectively  than in rainfed lowlands (66%) and uplands (74%) . Among A3.3Overall, N and P application rates had a strong effect (ρ = 0.6) on rice yield and the reduction of the relative yield gap (ρ = −0.6) while the K application rate had a small effect (ρ = 0.28) on yield and no effect on the reduction of the yield gap . These c3.4Nutrients application rates and growing environments had variable effects on the partial factor productivity of N, P, and K. Concerning PFPN, reducing the N application rate raised the likelihood of being classified in the category “Very high” while increasing the rate raised the likelihood of being classified in the category “Very low”. A lower P application in fields was associated with a higher probability of “Very low” PFPN. In comparison with farmers’ fields in the Humid zone, the ones from the Arid and Highlands zones were more likely to be categorized in the group “Very high”. In comparison with fields in irrigated lowlands, growing rice in rainfed systems raised the likelihood of having a “Very low” PFPN or in other words wasteful application of N A. A lowe3.5−1) and a very high N application rate (mean = 171 kg N ha−1). The PFPN for this cluster is on average 42 kg grain kg−1 N. Around 4% of the data points belong to this cluster. Accounting for 20% and 12% of the data points respectively, cluster 1 (HYHN) and cluster 4 (MYHN) have high N application rates . They differ in their actual yield. Cluster 1 has high yields (mean 5.16 Mg ha−1) while cluster 4 has medium yields (mean 3.33 Mg ha−1). Cluster 3 (LYMN) has medium N application rates (mean 63 kg N ha−1) and low yields . This group gathers 14% of the data points. Cluster 2 (LYLN) and cluster 5 (MYLN) have low N application rates . The difference in their actual yields separates them. Cluster 2 has low yields (mean 1.40 Mg ha−1) while cluster 5 has medium yields (mean 3.20 Mg ha−1). Around 32% and 18% of the data points belong to Clusters 2 and 5, respectively. PFPN of clusters 6 (Very High Yield and Very High N application rate), 1 (High Yield and High N application rate), and 4 (Medium Yield and High N application rate) were in the optimum range (between 30 and 100 kg grain kg−1 N). PFPN of clusters 2 (Low Yield and Low N application rate) and 5 (Medium Yield and Low N application rate) were very high (>100 kg grain kg−1 N) while the one of cluster 3 (Low Yield and Medium N application rate) was low  the association between fertilizer use and yield; then, (ii) factors affecting partial factor productivity of nutrients in rice fields; and, (iii) general research and development recommendations for the clusters identified based on actual yield and N application rates.4.1The huge variability of fertilizer use in the fields reminds the specificity of each region, rice-growing environment, and AEZ, which could be explained by biophysical, economic as well as political factors. For example, N and P fertilizer rates were higher in irrigated lowlands than in rainfed systems. Indeed, rainfed rice farmers give priority to reducing drought and climate variability risks, even in those rainfed lowlands having favorable field water conditions . So, farHigher N and P fertilizer application rates were associated with higher yields and lower yield gaps in irrigated lowlands. This agrees with earlier findings on the positive effects of N and P on yield in irrigated lowlands . AlthougAlthough there is growing evidence of K deficiency in rice fields in SSA , the K a4.2−1 in 2007 to 23 kg ha−1 in 2050 to sustain food production in Africa  of N, P, and K compared to the optimum level, implying an insufficient supply of fertilizer and a high risk of soil nutrient mining . This chn Africa . This inn Africa , high N n Africa . These fThe variations in partial factor productivity of nutrients were related to the rice-growing environment and AEZ. Irrigated lowlands tended to have higher PFPN than rainfed lowlands and uplands as irrigated lowland systems are more efficiently managed with less water stress leading to higher yield . Most ra4.3Based on actual yield and N application rate, we identified six clusters. The research and development recommendations for each cluster are different . ClusterAlthough the research and development recommendations provided here are quite general, we identified priority countries or sites where more research and development resources should be invested using limited data. However, this simple analysis presents some weaknesses and limitations that could be improved in future studies. First, as mentioned earlier, few data points are available per country and site, making general conclusions at national or sub-national levels less accurate. Second, a classification based on yield gaps and N inputs would have been more adequate to draw more relevant research and development recommendations. We did not include yield gap data in our classification as potential yield or water-limited potential yield was not available for more than half of the data points. Further efforts in modeling yield potential and water-limited yield potential for the new sites are needed to fill this gap. In addition, as financial issues, risks for water stress, and climate variability could be key reasons explaining low fertilizer inputs in SSA, further studies including fertilizer prices and other socio-economic and climatic parameters could help to fine-tune these recommendations.5By reviewing the fertilizer use, grain yield, and relative yield gap in smallholder farmer rice fields in 24 countries in sub-Saharan Africa over the last three decades, this study highlighted, for the first time, a large variation of N, P, and K application rates across regions, agroecological zones, and growing environments. The N, P, and K application rates were higher in West Africa than in Central and East, and Southern Africa, and in irrigated lowlands than in rainfed systems. An insufficient supply of fertilizer implying a high risk of soil nutrient mining was reported in almost half of the cases. N and P application rates had a strong effect on the reduction of the relative yield gap in irrigated lowlands while no effect was found in rainfed systems. Reasons for low fertilizer use and its poor association with rice yield need also to be identified for developing a strategy for enhancing its use and reducing the yield gaps. Moreover, as the records of fertilizer application rates in farmers' fields in sub-Saharan Africa are scarce and data obtained in this review is limited, we, therefore, recommend the development of nutrient use efficiency monitoring systems in sub-Saharan Africa countries for long-term trend analysis, nutrient balance assessment, and fertilizer demand forecasts. Such strategies would help increase rice yield, reduce rice import dependency in many sub-Saharan African countries, sustain soil fertility, and, by implication, improve global food security.10.13039/100000865Bill & Melinda Gates Foundation  for supporting this study through the 10.13039/501100015815CGIAR Excellence in Agronomy (2030) project (Incubation Phase).The authors gratefully acknowledge the J.-M.J.: Conceptualization, Methodology, Data collection, curation & analysis, Visualization, Writing - Original Draft; I.A.: Data collection, Writing - Review & Editing; E.R.D.-Y.: Data collection, Writing - Review & Editing; K·S.: Data collection, Writing - Review & Editing; Y.T.: Data collection, Writing - Review & Editing; H.A.: Data collection, Writing - Review & Editing; KS: Conceptualization, Methodology, Data collection, Writing - Review & Editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


The total length of the consensus map was 1613.17 cM, with a mean genetic distance between adjacent bin markers of 0.59 cM. Twenty-seven quantitative trait loci (QTLs) for berry MesF, PPH, and PerB were identified in linkage groups (LGs) 1, 3, 4, 6, 8, 9, 10, 11, 14, 16, and 17, including twelve QTLs that were firstly detected in LGs 6, 11, and 14. Fourteen promising candidate genes were identified from the stable QTL regions in LGs 10, 11, 14, and 17. In particular, VvWARK2 and VvWARK8 refer to chromosome 17 and are two promising candidate genes for MesF and PPH, as the VvWARK8 gene may increase pectin residue binding with WARK for high berry firmness maintenance and the allele for VvWARK2 carrying the ‘CC’ and ‘GA’ genotypes at Chr17:1836764 and Chr17:1836770 may be associated with non-hard texture grape cultivars. In addition, real-time quantitative polymerase chain reaction (RT–qPCR) verification revealed that the promising candidate transcription factor genes VvMYB4-like, VvERF113, VvWRKY31, VvWRKY1, and VvNAC83 may regulate cell wall metabolism candidate gene expression for grape berry texture changes.Berry texture is a noteworthy economic trait for grape; however, the genetic bases and the complex gene expression and regulatory mechanism for the diverse changes in berry texture are still poorly understood. In this study, the results suggest that it is difficult to obtain high-mesocarp firmness (MesF) and high-pericarp puncture hardness (PPH) grape cultivars with high pericarp brittleness (PerB). The high-density linkage map was constructed using whole-genome resequencing based on 151 F Vitis vinifera L.), which belongs to family Vitaceae, is an economically important fruit tree cultivated worldwide. Grape berry texture has high agronomic relevance because of its relationships with the quality parameters and marketing requirements of table, raisin, and wine grapes [vinifera wine and dual-purpose (wine and table) cultivars, such as ‘Chardonnay’ and ‘Terbash’, had soft and non-crisp flesh, and the labruscana grapes, which included table-use and dual-purpose cultivars, lacked crisp flesh texture cultivars due to labruscana cultivars generally having high DFP , which included table-use and dual-purpose cultivars [Grape  of berry firmness had a range of 0.813 to 0.94843 [et al. [et al. [1 population from 2016. In addition, further studies have detected multiple QTLs for berry firmness in LGs 2, 8, 15, 16, 17, and 18 using different grape crossing populations in recent years and inferred that the candidate genes abscisic-aldehyde oxidase-like, endoglucanase 3, senescence-associated protein din1, expansin-A6, polygalacturonase, pectate lyase 4, and VviAGL11 are associated with berry firmness [Grape is a perennial woody liana with a complex genetic background and long growth cycle. Previous studies have found that the broad-sense heritability . The main berry texture trait-related QTLs were detected based on a high-density whole-genome resequencing genetic map from 2017 to 2019, and promising candidate genes were analysed based on the 12X.v2 grapevine genome in the URGI database.2H) for PerB, PPH, and MesF was 0.89–0.96 in RM from 2017 to 2019, especially for PPH, which showed a large and stable 2H (0.96) in the threee consecutive years  population from 2017 to 2019, and most of the three berry texture traits in RM showed wide transgressive segregation . The meave years .P < 0.05) from 2017 to 2019, especially MesF, which showed the strongest positive correlations (0.66–0.78) in all three years. MesF and PPH showed a weak but highly significant positive correlation , and PPH showed a strong significant positive correlation with PerB . However, there was no significant correlation (P < 0.05) between MesF and PerB in this study.Phenotypic correlations between berry texture traits  averaged over the 3 years varied from −0.013 to 0.78 . Each beV. vinifera 12X.v2) using BWA software. A total of 96.64% of the clean reads mapped to the reference genome from the female parent, 96.44% from the male parent and 95.28% from the offspring. The average sequencing depth was 30× for the male parent ‘Muscat Hamburg’, 31× for the female parent ‘Red Globe’ and 5.07× for the offspring , 19.58 Gb from the male parent (‘Muscat Hamburg’) and 508.69 Gb from the offspring. The Q30 and GC contents were calculated to be 92.77% and 35.90% for the female parent, 92.57% and 35.77% for the male parent, and 91.95% and 35.94% for the offspring, respectively  and 597 344 insertions/deletions (InDels) were identified between ‘Red Globe’ and ‘Muscat Hamburg’, of which 1 278 507 and 681 862 SNPs were detected in intergenic regions and introns, respectively, and 66 854 SNPs were nonsynonymous. In addition, 1585 InDels resulted in codon deletion and insertion, 800 InDels resulted in codon changes , and theA total of 93 127 SNPs were assigned to 2725 bin markers for the RM population .The number of SNP markers in the LGs ranged from 2440 to 7508. LG 15 and LG 17 contained the maximum and minimum number of SNP markers, respectively. The number of bin markers in the LGs ranged from 117 to 170. LG 17 and LG 19 contained the smallest and maximum number of bin markers, respectively . Most ofAccording to the high-density bin-based genetic map, a total of 27 QTLs for MesF, PPH, and PerB were identified using multiple QTL mapping from 2017 to 2019 in the RM population , Table 32R); this includes the major QTL MesF10.2 detected in LG 10, with a logarithm of odds (LOD) value of 14.2 in 2019, which explained up to 20.7% of the total variance. Nine QTLs for PPH identified in LGs 6, 10, 11, 14, and 17 explained up to 25.8% of the total variance, with the largest LOD value of 13.9 in 2017. In addition, five QTLs for PerB were identified in LGs 3, 6, 9, 14, and 16, which explained 8.3% to 17.4% of the phenotypic variation  anchored to the 12X.v2 grape reference genome, a total of 412 candidate genes were identified in LGs 10, 11, 14, and 17 with stable QTL confidence intervals . HoweverVvβ-glucosidase 44 (Vitvi14g00996) and VvMYB4-like (Vitvi17g00231) were downregulated in the male parent ‘Muscat Hamburg’ and female parent ‘Red Globe’ during ripening. The relative expression levels of Vvβ-glucosidase 44, VvMYB4-like, and VvFLA 7 (Vitvi11g00950) were significantly higher in ‘Muscat Hamburg’ than in ‘Red Globe’ at preveraison, but the relative expression levels were not significantly different between ‘Red Globe’ and ‘Muscat Hamburg’ at maturation (VvFLA7a (Vitvi11g00975) and Vvβ-glucosidase (Vitvi17g00234) showed similar expression tendencies with Vvβ-glucosidase 44 at preveraison but showed significantly higher in ‘Red Globe’ than in ‘Muscat Hamburg’ at maturation (VvWARK2 (Vitvi17g00175) and VvWARK8 (Vitvi17g00209) showed similar expression tendencies: the expression of both was higher in the female parent ‘Red Globe’ than in the male parent ‘Muscat Hamburg’ at preveraison and maturation, and expression in ‘Muscat Hamburg’ was downregulated during ripening (VvERF113 (Vitvi17g00025) showed an expression tendency similar to those of VvWARK2 and VvWARK8 during ripening, but VvERF113 was downregulated in ‘Red Globe’ from preveraison to maturation (VvGlucuronoxylan 4-O-methyltransferase 1 (Vitvi17g00106) showed no significant difference between ‘Muscat Hamburg’ and ‘Red Globe’ but was higher in ‘Red Globe’ than in ‘Muscat Hamburg’ at maturation (VvWRKY31 (Vitvi10g00063) was higher in ‘Red Globe’ than in ‘Muscat Hamburg’ at maturation, although there was no difference at preveraison  showed an expression tendency similar to VvWRKY31, except for being up-regulated in ‘Red Globe’ at maturation (VvWRKY1 (Vitvi17g00102) showed similar tendency to VvFLA7a, except for up-regulated significantly in ‘Red Globe’ at maturation (VvNAC83 (Vitvi17g00066) showed no significant difference between ‘Muscat Hamburg’ and ‘Red Globe’ at preveraison but showed a higher expression level in ‘Muscat Hamburg’ than in ‘Red Globe’ at maturation, and the expression of Vvβ-1,3-galactosyltransferase 2 (Vitvi10g00092) showed a tendency similar to that of VvNAC83 between ‘Red Globe’ and ‘Muscat Hamburg’ during maturation  for VvWARK2 at Chr17:1836764 and Chr17:1836770; however, the soft-textured grape ‘Muscat Hamburg’ carries the ‘CC’ and ‘GA’ genotypes at Chr17:1836764 and Chr17:1836770  of berry firmness (flesh and skin) was 0.86841 to 0.94843 and Ban et al. [2H of berry firmness was 0.813 based on sensory evaluation with each individual on a scale of 1–5. In the present study, the MesF, PPH, and PerB of berries were analysed individually and showed high 2H in different years (P < 0.05) between MesF and PerB, even though PPH exhibited a significant correlation with both MesF and PerB  with relatively broad QTL intervals may affect candidate gene identification [V. vinifera L.) assembled in a telomere-to-telomere (T2T) manner [A reliable genetic map is essential for identifying QTLs and candidate genes for traits of interest . Becausefication , 48. To fication , 11, 49.fication , 51 becafication , 53. Spe) manner , NGS-bas) manner . Thus, tPrevious studies have shown that berry texture is a typical quantitative trait controlled by multiple genes , 30. To et al. [et al. [et al. [qBF18–2016, qBF18–2017, and qBF18–2018 in LG 18, which corresponded to the grape genome region chr18: 24639353–28 587 457 and were related to the candidate genes abscisic-aldehyde oxidase-like, endoglucanase 3, and NAC 90-like, respectively. In addition, a major QTL for berry firmness reported by Crespan et al. [VviAGL11, which is a main gene linked to seedlessness. In this study, major QTLs for berry texture were not identified in LG 18 by multiple QTL mapping; however, we did detect two QTLs for MesF in LG 18 by single-interval mapping in 2018 and 2019  for MesF and PPH was detected in LG 17 between Block2298 (Chr17: 7998–532 287) and Block2255 (Chr17: 2805906–2 809 152) in 2018 and 2019, with good overlap, and explained up to 16.1% of the total phenotypic variance, with LOD peaks near markers Block2261, Block2264 and Block2265 (Chr17: 657561–706 586)  are believed to be related to texture because Fasciclin-like arabinogalactan protein contributes to plant cell wall integrity and plant stem strength by cellulose deposition [β-Glucosidase decreases fruit softening during ripening [et al. [et al. [et al. [Two new stable QTLs for berry texture in LGs 11 and 14 were first detected in grape, and three candidate genes  and ‘Muscat Hamburg’ (V. vinifera L.) was used in the present study (n = 151). The female parent ‘Red Globe’ has a firm-flesh texture after maturation, and the male parent ‘Muscat Hamburg’ has a soft-flesh texture after maturation. Intraspecific hybridization was conducted in May 2011, and hybrid seeds were collected in October 2011. The offspring and parents were cultivated at Shenyang Agricultural University with commercial vineyard management and pruning. Genomic DNA (gDNA) was extracted from young leaves of the parents and offspring. Clusters were harvested from each plant at maturity from 2017 to 2019, 30 similarly sized berries were used for texture evaluation immediately, and 10 berries were frozen with liquid nitrogen for the following experiment after sampling.An Fg) required to puncture the berry flesh, PPH indicated the force (g) used from the probe touching the peel until the peel is punctured and PerB indicated the displacement distance (mm) from the probe touching the peel to puncture peel. Berry puncture determination was performed at 1 mm/s until the depth reached 7 mm in the berry mesocarp using a needle probe with a diameter of 2 mm (P/2). Then, the probe was reset with a speed of 10 mm/s.The berry mesocarp (flesh) firmness (MesF), pericarp (peel) puncture hardness (PPH), and pericarp brittleness (PerB) were determined using a texture analyzer  according to a previous method, with some modifications . MesF inTotal gDNA was extracted by using the CTAB method . The highttps://plants.ensembl.org/Vitis_vinifera/Info/Index) by Burrows-Wheeler Aligner (BWA) software [http://sourceforge.net/projects/picard/) to reduce the impact of PCR duplication. To ensure the accuracy of the detected SNPs and InDels, the ‘Local realignment’ and ‘InDel realignment’ procedure were executed in the Genome Analysis Toolkit (GATK) v3.6 software using RealignerTargetCreator and IndelRealigner with default parameters. Afterward, the GATK’s HaplotypeCaller was used for SNP and InDel variant calling by generating the genomic VCF (GVCF) files, and the SNPs and InDels were strict filtered by the Bcftools and GATK, including SNPs at least 5 bp away from any InDels, SNPs separated by distances of more than 5 bp, and InDels separated by distances greater than 10 bp. Finally, the SNP set was generated by combining GATK and SAMtools with default parameters [1 offspring was confirmed at a particular SNP locus. To merge the SNP dataset, the ‘pileup’ function in SAMtools was employed, and the genotypes of all SNPs were consistent at the loci in offspring and parents. SNPs identified as polymorphic markers should have two to four alleles between parents, and the polymorphic SNP markers were coded according to the cross-pollinated (CP) populations, which consisted of six segregation types .To ensure that the reads were reliable, Illumina-sequenced raw reads for the genomic survey were first filtered using the fastp (v 0.20.1) preprocessor to remove the low-quality reads, including the number of base with quality value Q ≤ 10 accounts for more than 50% of the read (parameters: -q 10 -u 50), the average quality value of the read less than 20 (parameters: -e 20), the proportion of N (unable to determine the specific base type) on the read was greater than 10% (parameters: -y -Y 10), and the length of the paired-end reads was less than 100 bp or greater than 150 bp (parameters: -l 100 -b 150 -B 150), and then the Q30 and GC contents were calculated using an in-house Perl script. After excluding low-quality reads , raw reads from the two parents and each progeny were sorted according to their barcode sequences, and high-quality reads and clean reads were trimmed from the same sample and mapped to the grape reference genome  test was carried out for goodness-of-fit assessment at a confidence level of 0.01, and the SNP markers showing extreme segregation distortion (P < 0.01) were excluded from linkage map construction.SNP markers that showed less than 4× coverage in both parents and those from unknown chromosomes were discarded. A, SNP marker was retained every 2 kb to ensure a uniform distribution and a high density of SNPs. The chi-square . Total RNA was extracted from ‘Muscat Hamburg’ and ‘Red Globe’ berry skins and mesocarps according to a previous report , with so VvActin .1 population was assessed using the Shapiro–Wilk test. The broad-sense heritability (2H) was calculated based on a previous report [The correlations between MesF, PPH, and PerB within different years were analysed by the nonparametric Spearman correlation coefficient in the R 4.1.2 package (R Development Core Team 2014). The distribution of MesF, PPH, and PerB in the Fs report . Candidas report .Web_Material_uhad226Click here for additional data file.