bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing shrimp nutrition and health: ginseng saponins as functional additives in low-fishmeal diets on Litopenaeus vannamei
Authors: Lin Sihan , Yao Rong , Cui Xin , Guo Yucai , Hu Denghui , Zhou Bin , Zhou Zhaobin , He Xuanshu , Liao Zhihong , Tan Beiping , Niu Jin
Published: October 2024

The research investigated the nutritional physiology effect of ginseng saponins on Litopenaeus vannamei (L. vannamei) under low-fishmeal diets. In total, five experimental groups were arranged, with 21% fishmeal (high-fishmeal) serving as the positive control (PC), 11% fishmeal (low-fishmeal) serving as the negative control (NC), and 11% fishmeal serving as the addition in all three other groups. Similarly, ginseng saponins (GSP, purity of 2%) were added in the order of 0.1%, 0.3%, and 0.5% (GSP0.1, GSP0.3, and GSP0.5), with an 8-week growth cycle. Both GSP0.1 and GSP0.3 showed significantly higher growth performance (final body weight, FBW; weight gain rate, WGR; specific growth rate, SGR) than the NC group, but significantly lower growth performance than the PC group (P<0.05). However, it was found that there was no significant difference in the body composition of the whole shrimp between the experimental groups. Compared to the PC group, the GSP0.3 group exhibited significantly elevated levels of antioxidant enzymes, total antioxidant capacities (T-AOC), superoxide dismutase (SOD), and glutathione peroxidase (GSH-Px) (P<0.05). Additionally, significant differences were observed between the PC and GSP0.3 groups regarding the expression levels of sod, cat, and gsh-px (P<0.05). And there was a better morphological organization of shrimp hepatopancreas in the GSP0.3 group than in all other groups. In comparison with the PC group, there was no significant difference in shrimp survival rates after ammonia nitrogen stress with ginseng saponins added (P>0.05). Whereas, in terms of the relative expression levels of the corresponding genes, in shrimp of the GSP0.3 group, the relative expression of antioxidant-related genes sod, cat, and gsh-px were significantly higher than that of the PC group (P<0.05). Caspase3 and p53, along with bcl-2 and bax, were found to be significantly more expressed in shrimp of the GSP0.3 group than in all other groups (P<0.05). These findings imply that in addition to improving growth performance, adding ginseng saponins at a concentration of 11% fishmeal could improve the antioxidant capacity of L. vannamei as well as its resistance to stress. Therefore, ginseng saponins can be utilized as a functional additive to increase L. vannamei growth performance, enhance antioxidant capacity, and reduce stress in low-fishmeal diets, 0.3% of ginseng saponins is optimal.
Spatial transcriptomics analysis identifies therapeutic targets in diffuse high-grade gliomas
Authors: Yang Yongtao , Hong Yingzhou , Zhao Kai , Huang Minhao , Li Wenhu , Zhang Kui , Zhao Ninghui
Published: October 2024

Introduction

Diffuse high-grade gliomas are the most common malignant adult neuroepithelial tumors in humans and a leading cause of cancer-related death worldwide. The advancement of high throughput transcriptome sequencing technology enables rapid and comprehensive acquisition of transcriptome data from target cells or tissues. This technology aids researchers in understanding and identifying critical therapeutic targets for the prognosis and treatment of diffuse high-grade glioma.

Methods

Spatial transcriptomics was conducted on two cases of isocitrate dehydrogenase (IDH) wild-type diffuse high-grade glioma (Glio-IDH-wt) and two cases of IDH-mutant diffuse high-grade glioma (Glio-IDH-mut). Gene set enrichment analysis and clustering analysis were employed to pinpoint differentially expressed genes (DEGs) involved in the progression of diffuse high-grade gliomas. The spatial distribution of DEGs in the spatially defined regions of human glioma tissues was overlaid in the t-distributed stochastic neighbor embedding (t-SNE) plots.

Results

We identified a total of 10,693 DEGs, with 5,677 upregulated and 5,016 downregulated, in spatially defined regions of diffuse high-grade gliomas. Specifically, SPP1, IGFBP2, CALD1, and TMSB4X exhibited high expression in carcinoma regions of both Glio-IDH-wt and Glio-IDH-mut, and 3 upregulated DEGs (SMOC1, APOE, and HIPK2) and 4 upregulated DEGs (PPP1CB, UBA52, S100A6, and CTSB) were only identified in tumor regions of Glio-IDH-wt and Glio-IDH-mut, respectively. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses revealed that upregulated DEGs were closely related to PI3K/Akt signaling pathway, virus infection, and cytokine-cytokine receptor interaction. Importantly, the expression of these DEGs was validated using GEPIA databases. Furthermore, the study identified spatial expression patterns of key regulatory genes, including those involved in protein post-translational modification and RNA binding protein-encoding genes, with spatially defined regions of diffuse high-grade glioma.

Discussion

Spatial transcriptome analysis is one of the breakthroughs in the field of medical biotechnology as this can map the analytes such as RNA information in their physical location in tissue sections. Our findings illuminate previously unexplored spatial expression profiles of key biomarkers in diffuse high-grade glioma, offering novel insight for the development of therapeutic strategies in glioma.
Hydrolyzed protein formula improves the nutritional tolerance by increasing intestinal development and altering cecal microbiota in low-birth-weight piglets
Authors: Bai Miaomiao , Liu Hongnan , Yan Yalu , Duan Sufang , Szeto Ignatius Man-Yau , He Jian , Hu Jinjie , Fu Yawei , Xu Kang , Xiong Xia
Published: October 2024

Background

Prematurity or low birth weight (LBW), poses a significant challenge in global health. Exploring appropriate and effective nutritional interventions is crucial for the growth and development of LBW infants. Hydrolyzed protein formula has been suggested as a potential solution to prevent intestinal dysfunction and improve digestion and absorption in infants.

Objectives

This study aimed to investigate the benefits of hydrolyzed protein formula on feeding intolerance, intestinal morphological development, and microbiota in a LBW piglet model.

Methods

A total of 24 male piglets (3 d of age, 0.95–1.25 kg average BW) were assigned (8 pens/diet; 1 pigs/pen) into three dietary treatments and fed with a basic formula (BF), standard premature infant formula (SF) and hydrolyzed protein formula (HF) respectively, for 7 d. After the piglets sacrifice, growth performance, amino acid metabolism and intestinal morphology were assessed. 16S rRNA sequencing and microbial metabolic phenotypes analyzed the effects of different formula treatments on intestinal flora structure of LBW piglets.

Results

The HF diet reduced the rates of diarrhea and milk vomiting were reduced by 20.44% (p > 0.05) and 58.44% (p > 0.05), and decreased the crypt depth in the ileum while increasing the ratio of villus height/crypt depth and the mRNA expressions of y+LAT1 and b0,+AT in the ileum (p < 0.05). HF increased the final body weight, serum Thr and essential amino acid contents, and CAT2 and b0,+AT mRNA expressions in ileal mucosa compared with the SF diet (p < 0.05). Microbiota sequencing results showed that the colonic microbial richness indices (Chao1, ACE, and observed species), the diversity indices (Shannon and Simpson), and the phyla Actinobacteriota, unidentified_Bacteria, Acidobacteriota and Actinobacteria, the genus Rubrobacter and RB41 were reduced (p < 0.05) in SF and HF groups. Microbial metabolic phenotypes analysis showed a reduction in the richness of biofilm-forming bacteria (p < 0.05).

Conclusion

In summary, hydrolyzed protein formula had better nutrition and tolerance in LBW suckling piglets by improving amino acid transport and intestinal development, and regulating gut microbial communities.
Dynamical analysis and soliton solutions of a variety of quantum nonlinear Zakharov–Kuznetsov models via three analytical techniques
Authors: Zaagan Abdullah A. , Altalbe Ali , Bekir Ahmet
Published: October 2024

Some new types of truncated M-fractional exact soliton solutions of the two important quantum plasma physics models, extended quantum Zakharov–Kuznetsov and extended quantum nonlinear Zakharov–Kuznetsov, are successfully achieved by applying the expa function technique, the improved (G′/G)-expansion technique, and the Sardar sub-equation technique. These two models have many useful applications when explaining the waves in the quantum electron-positron-ion magnetoplasmas as well as weakly nonlinear ion-acoustic waves in plasma. The obtained results are in the form of dark, bright, periodic, and other soliton solutions. The results are verified and represented by two-dimensional, three-dimensional, and contour graphs. The results are newer than the existing results in the literature due to the use of fractional derivatives. Hence, the solutions will be fruitful in future studies on these models. The solutions obtained are useful in the areas of applied physics, applied mathematics, dynamical systems, and nonlinear waves in plasmas and in dense space plasma. The applied techniques are simple, fruitful, and reliable for solving other models in mathematical physics.
Neutron skins: A perspective from dispersive optical models
Authors: Atkinson M. C. , Dickhoff W. H.
Published: October 2024

An overview of neutron skin predictions obtained using an empirical nonlocal dispersive optical model (DOM) is presented. The DOM links both scattering and bound-state experimental data through a subtracted dispersion relation which allows for fully consistent, data-informed predictions for nuclei where such data exist. Large skins were predicted for both 48Ca (Rskin48=0.25±0.023 fm in 2017) and 208Pb (Rskin208=0.25±0.05 fm in 2020). Whereas the DOM prediction in 208Pb is within 1σ of the subsequent PREX-2 measurement, the DOM prediction in 48Ca is over 2σ larger than the thin neutron skin resulting from CREX. From the moment it was revealed, the thin skin in 48Ca has puzzled the nuclear-physics community as no adequate theories simultaneously predict both a large skin in 208Pb and a small skin in 48Ca. The DOM is unique in its ability to treat both structure and reaction data on the same footing, providing a unique perspective on this Rskin puzzle. It appears vital that more neutron data be measured in both the scattering and bound-state domain for 48Ca to clarify the situation.
Association between household solid fuel usage and trajectories of multimorbidity among middle-aged and older adults: a nationwide population-based cohort study
Authors: Li Yiting , Wu Bingjie , Fan Bingbing , Lv Jiali , Li Chunxia , Su Chang , Liu Aidong , Zhang Tao
Published: October 2024

Background

This study aimed to explore the effect of household solid fuel usage on the multimorbidity trajectories among middle-aged and older adults.

Methods

Based on the 2011–2018 China Health and Retirement Longitudinal Study, the group-based trajectory modeling and the multinomial logistic regression model were used to explore the relationship between multimorbidity trajectories of older adults with different fuel types, duration of solid fuel usage, and potential interaction with PM2.5. Three multimorbidity trajectory patterns were identified by group-based trajectory modeling and labeled as “non-chronic morbidity” (no disease increase), “newly developing multimorbidity” (diseases grew from 0 to 2), and “multi-chronic multimorbidity” (diseases grew from 2 to 4).

Results

Compared to “Non-chronic morbidity,” solid fuel was significantly associated with adverse multimorbidity trajectories, with an odds ratio (OR) and 95% confidence interval (CI) of 1.33 (1.11, 1.60) and 1.35 (1.18, 1.55) for newly developing and multi-chronic group, respectively. An adverse multimorbidity trajectory tended to be established with longer durations of solid fuel usage than “Non-chronic morbidity.” For “Newly-developing multimorbidity,” the ORs (95% CI) for 1–7 years and ≥ 8 years of solid fuel usage were 1.16 (0.94, 1.42) and 1.41 (1.12, 1.76), respectively, with P trend=0.001, while in “Multi-chronic multimorbidity,” those were 1.25 (1.07, 1.47) and 1.68 (1.41, 2.00), respectively, with P trend <0.001. In the interaction analysis, the association between solid fuel usage and trajectories was significant only in areas where PM2.5 was lower than 50 μg/m3.

Conclusion

For the middle-aged and older Chinese population, a higher risk of multimorbidity trajectory is associated with household solid fuel usage, especially in the areas with lower PM2.5.
Brief report: Aging adult utilization of an mHealth intervention for problem drinking
Authors: Sedotto Robyn N. M. , Edwards Alexandra E. , Dulin Patrick L. , King Diane K.
Published: October 2024

Alcohol consumption among aging adults is a growing concern due to its potential to exacerbate age-related health conditions. Developing accessible interventions for this demographic is imperative. Mobile health (mHealth) interventions offer a promising avenue, but their effectiveness and engagement among aging adults remain uncertain. This study is a secondary analysis that aimed to compare the utilization and outcomes of an mHealth intervention between aging (50+) and younger adults in a clinical trial of an mHealth intervention (Step Away) for reduced drinking. At the three-month follow-up, both age groups exhibited significant reductions in alcohol consumption and increased readiness for change. Furthermore, aging adults utilized the mHealth intervention significantly more, expressed a higher likelihood of continued use, and rated the intervention higher on the System Usability Scale (SUS). These findings suggest that mHealth interventions for alcohol-related issues can be equally effective for aging adults and that they readily engage with such tools and find them acceptable. This study underscores the potential of mHealth interventions as a viable solution for addressing alcohol-related concerns among aging adults. Further research targeting mHealth interventions tailored specifically to this demographic is warranted.

Clinical trial registration: https://clinicaltrials.gov/ct2/show/NCT04447794, Identifier [NCT04447794].
Social and metabolic mediation of growth performance in a temperate estuarine fish
Authors: Hoots Elizabeth C. , Doling Juliet Bao Ngoc , Kuchenmüller Luis L. , Skeeles Michael R. , Clark Timothy D.
Published: October 2024

Despite the importance of juvenile growth to lifetime fitness, high variability exists within many fish species where some individuals grow markedly faster than others. Here, we explored whether social context mediated growth performance in n = 35 Galaxias maculatus by manipulating the social environment after identifying different growth phenotypes. We found that initially fast-growing fish slowed their growth rate when pooled with similarly fast-growing individuals, whereas none of the initially slow-growing fish markedly improved their growth when pooled with other slow-growers. We examined for any metabolic underpinnings to the findings by measuring standard, routine, and maximum metabolic rates (SMR, RMR, and MMR) at two time points separated by ~4 months. SMR and RMR positively correlated with growth rates when the fish were pooled into growth phenotypes, but not when they were returned to their original (mixed) social groups. We highlight research directions to further elucidate how social context and metabolism interact to govern growth phenotype.
Hemorrhagic encephalopathies and myelopathies in dogs and cats: a focus on classification
Authors: Santifort Koen M. , Platt Simon
Published: October 2024

The prevalence of hemorrhagic diseases of the central nervous system of dogs and cats is low compared to other diseases such as neoplasia and inflammation. However, the clinical consequences can be devastating. Several etiological and localization-based classification systems have been reported for intracerebral and spinal cord hemorrhage or hematomyelia in humans but similar systems do not exist in veterinary medicine. The authors propose an etiologic classification system for both intraparenchymal hemorrhagic encephalopathy and myelopathy following a review of the literature detailing the presentation, diagnosis, therapy, and prognosis of these diseases. A summary of the investigative and therapeutic approach to these cases is also provided.
Determinants of Unmet Healthcare Needs During the Final Stage of the COVID-19 Pandemic: Insights From a 21-Country Online Survey
Authors: Lewis Samuel , Ewald Louisa , Duber Herbert C. , Mokdad Ali H. , Gakidou Emmanuela
Published: October 2024

Objectives

During the COVID-19 pandemic, essential health services experienced significant disruptions, impacting preventive and chronic care across the world.

Methods

Utilizing the Pandemic Recovery Survey (PRS), conducted online with Facebook’s Active User Base across 21 countries between March and May 2023, this cross-sectional study identifies the magnitude of and key factors associated with unmet preventive and chronic care needs during the late stage of the COVID-19 pandemic.

Results

Approximately 28.2% of respondents reported unmet preventive care needs, and 42.1% experienced unmet chronic care needs, with key determinants including food insecurity (aOR 1.94, 95% CI 1.81–2.07 for preventive services; aOR 1.85, 95% CI 1.68–2.03 for existing conditions) and distrust in health professionals (aOR 1.09, 95% CI 1.03–1.15 for preventive services; aOR 1.53, 95% CI 1.41–1.66 for existing conditions).

Conclusion

The findings underscore a widespread unmet need for health services, highlighting the impact of social determinants and trust in health professionals on service disruption. The results suggest that pandemic recovery efforts should focus on the most affected groups to bridge health disparities and ensure an equitable recovery.
Automatic Scheduling Method for Customs Inspection Vehicle Relocation Based on Automotive Electronic Identification and Biometric Recognition
Authors: Zhou Shengpei , Zhang Nanfeng , Duan Qin , Xiao Jinchao , Yang Jingfeng
Published: October 2024

This study presents an innovative automatic scheduling method for the relocation of customs inspection vehicles, leveraging Vehicle Electronic Identification (EVI) and biometric recognition technologies. With the expansion of global trade, customs authorities face increasing pressure to enhance logistics efficiency. Traditional vehicle scheduling often relies on manual processes and simplistic algorithms, resulting in prolonged waiting times and inefficient resource allocation. This research addresses these challenges by integrating EVI and biometric systems into a comprehensive framework aimed at improving vehicle scheduling. The proposed method utilizes genetic algorithms and intelligent optimization techniques to dynamically allocate resources and prioritize vehicle movements based on real-time data. EVI technology facilitates rapid identification of vehicles entering customs facilities, while biometric recognition ensures that only authorized personnel can operate specific vehicles. This dual-layered approach enhances security and streamlines the inspection process, significantly reducing delays. A thorough analysis of the existing literature on customs vehicle scheduling identifies key limitations in current methodologies. The automatic scheduling algorithm is detailed, encompassing vehicle prioritization criteria, dynamic path planning, and real-time driver assignment. The genetic algorithm framework allows for adaptive responses to varying operational conditions. Extensive simulations using real-world data from customs operations validate the effectiveness of the proposed method. Results indicate a significant reduction in vehicle waiting times—up to 30%—and an increase in resource utilization rates by approximately 25%. These findings demonstrate the potential of integrating EVI and biometric technologies to transform customs logistics management. Additionally, a comparison against state-of-the-art scheduling algorithms, such as NSGA-II and MOEA/D, reveals superior efficiency and adaptability. This research not only addresses pressing challenges faced by customs authorities but also contributes to optimizing logistics operations more broadly. In conclusion, the automatic scheduling method presented represents a significant advancement in customs logistics, providing a robust solution for managing complex vehicle scheduling scenarios. Future research directions will focus on refining the algorithm to handle peak traffic periods and exploring predictive analytics for enhanced scheduling optimization. Advancements in the intersection of technology and logistics aim to support more efficient and secure customs operations globally.
Assessment of Heavy Metals in Tea Plantation Soil and Their Uptake by Tieguanyin Tea Leaves and Potential Health Risk Assessment in Anxi County in Southeast China
Authors: Chang Yunni , Jiang Fuying , Peñuelas Josep , Sardans Jordi , Wu Zhidan
Published: October 2024

Evaluating heavy metal pollution in tea plantation soil and conducting potential health risk assessments are crucial for ensuring the safety of tea consumers. However, soil heavy metal pollution levels and dietary exposure risk remain poorly understood, and there is no consensus on how soil physicochemical properties affect heavy metal concentrations. In this study, seventy-three soil samples and corresponding tea leaves from main tea-producing regions in Anxi County were analyzed for arsenic (As), chromium (Cr), lead (Pb) and copper (Cu) concentrations. The results showed that mean concentrations of As, Cr, Cu and Pb in the soil did not exceed respective risk screening values in China (GB 15618-2018). The concentrations of As, Cr, Cu and Pb in the tea leaves were within limiting values of the Chinese National Food Safety Standard, and the bioaccumulation factor of heavy metals in descending order was Cu > Pb > As > Cr. The hazard index values of heavy metals indicated no potential human health risk. Soil pH, EAl, EA and AP were the main controlling factors for heavy metal in soil and tea leaves. Cu and Pb concentrations in tea leaves were positively correlated with soil Cu and Pb concentrations. These results provide a scientific basis for effective monitoring and management in tea plantations and for controlling potential risks in tea leaves.
Color Dominance-Based Polynomial Optimization Segmentation for Identifying Tomato Leaves and Fruits
Authors: Guerra Ibarra Juan Pablo , Cuevas de la Rosa Francisco Javier , Linares Ramirez Alicia
Published: October 2024

Optimization processes or methods play an essential role in the continuous improvement of various human activities, particularly in agriculture, given its vital role in food production. In precision agriculture, which utilizes technology to optimize food production, a primary goal is to minimize the consumption of resources like water, fertilizers, and the detection of pests and diseases. In the fertilization process, it is essential to identify any deficiencies or excesses of chemical elements. Nutrient deficiencies, which are essential for plant development, are typically detected in the leaves of crops. This paper proposes a methodology for optimizing the color threshold dominance factors employed in the segmentation process for tomato crop leaves and fruits. The optimization is performed using an interpolation method to find the values that maximize the segmentation of leaves and fruits used by the color dominance segmentation method. A comparison of the interpolation method results with those obtained using a greedy algorithm, which iteratively finds the optimal segmentation values, shows nearly identical outcomes. Similarly, a UNetmodel is used for semantic segmentation, the results of which are inferior to those obtained by the proposed interpolation optimization method. The most significant contribution of the interpolation method is that it requires only a single iteration to generate the initial data, in contrast to the iterative search required by the greedy algorithm and the lengthy training process and video card dependency of the UNet model. This results in an 80% reduction in computation time.
Integrating Environmental Covariates into Adaptability and Stability Analyses: A Structural Equation Modeling Approach for Cotton Breeding
Authors: Suela Matheus Massariol , Nascimento Moysés , Nascimento Ana Carolina Campana , Azevedo Camila Ferreira , Teodoro Paulo Eduardo , Farias Francisco José Correia , de Carvalho Luiz Paulo , Jarquin Diego
Published: October 2024

Breeding programs rely on genotype-by-environment interaction (GEI) to recommend cultivars for specific locations. GEI describes how different genotypes perform under varying environmental conditions. Several methods were proposed to assess adaptability and stability across environments. These methods utilize various statistical approaches like parametric and non-parametric regression, multivariate analysis techniques, and even Bayesian frameworks and artificial intelligence. The accessibility of environmental data through platforms like NASA POWER allows breeders to integrate this information into a breeding process. It has been done by using multi-omics integration models that combine data across various biological levels to create accurate predictive models. In the context of phenotypic adaptability and stability analysis, structural equation modeling (SEM) offers an interesting approach to integrating environmental covariates. This work aimed to propose a novel approach that integrates weather information into adaptability and stability analysis, combining SEM with the established Eberhart and Russell model. Additionally, a user-friendly applet, denoted ECERSEM-AdaptStab, was made available to perform the analysis. This approach utilized data from 12 cotton cultivar trials conducted across two growing seasons at 19 sites. This approach successfully integrated environmental covariates into a phenotypic adaptability and stability analysis of cotton cultivars. Specifically, the genotypes TMG 41 WS, IMA CV 690, DP 555 BGRR, BRS 286 and BRS 369 RF were recommended for favorable environments, while the genotypes TMG 43 WS, IMA 5675 B2RF, IMA 08 WS, NUOPAL, DELTA OPAL, BRS 335, and BRS 368 RF are more suitable for unfavorable environments.
Detection of Aspergillus flavus in Figs by Means of Hyperspectral Images and Deep Learning Algorithms
Authors: Cruz-Carrasco Cristian , Díaz-Álvarez Josefa , Chávez de la O Francisco , Sánchez-Venegas Abel , Villegas Cortez Juan
Published: October 2024

Plant diseases cause economic losses and health risks, such as aflatoxins linked to liver cancer. These toxins, produced by fungi like Aspergillus flavus in figs, are often detected late through invasive methods or visual inspection. Since Spain, particularly Extremadura, is a key fig producer, alternative detection methods are essential to preventing aflatoxins in the food chain. The aim of this research is the early detection of Aspergillus flavus fungus using non-invasive techniques with hyperspectral imaging and applying artificial intelligence techniques, in particular deep learning. The images were taken after inoculation of the microtoxin using 3 different concentrations, related to three different classes and healthy figs (healthy controls). The analysis of the hyperspectral images was performed at the pixel level. Firstly, a fully connected neural network was used to analyze the spectral signature associated with each pixel; secondly, the wavelet transform was applied to each spectral signature. The resulting images were fed to a convolutional neural network. The hyperparameters of the proposed models were adjusted based on the parameter tuning process that was performed. The results are promising, with 83% accuracy, 82.75% recall, and 83.25% F1-measure for the fully connected neural network. The high F1-measure demonstrates that the model’s performance is good. The model has a low incidence of false positives for samples that contain aflatoxin, while a higher number of false positives appears in healthy controls. Due to the presence of false negatives, this class also has a high recall. The convolutional neural network results, accuracy, recall, and F1 are 77.25%, indicating moderate model performance. Only class 3, with higher aflatoxin concentration, achieves high precision and low false positive incidence. Healthy controls exhibit a high presence of false negatives. In conclusion, we demonstrate the effectiveness of pixel-level analysis in identifying the presence of the fungus and the viability of the non-invasive techniques applied in improving food safety. Although further research is needed, in this study, the fully connected neural network model shows good performance with lower energy consumption.
Forecast of Sugarcane Yield in Chongzuo, Guangxi—LSTM Model Based on Fusion of Trend Yield and Meteorological Yield
Authors: Ma Pengcheng , Zhang Na , Yang Yunhai , Wang Zeping , Li Guodong , Fu Zhishan
Published: October 2024

Purpose: This paper develops a high-precision yield fusion prediction model for the sugarcane industry in Chongzuo, Guangxi, based on the trend yield and meteorological yield using the long short-term memory (LSTM) model to cope with the multiple factors affecting sugarcane production. Decision support is provided to agricultural producers, policymakers, and supply chain managers so that they can plan resource allocation, market strategies, and policy directions more effectively. Methods: The paper modeled trend yield and weather yield separately to explore the complex relationship between the two in influencing sugarcane production. Trend yields were predicted using the exponential smoothing and multilayer perceptron (MLP) models, while meteorological yields were modeled using stepwise regression. The predicted yields were used again as input variables into the LSTM deep learning network to fit the nonlinear relationship between the two yields. Results: The results showed that (1) the fusion strategy of meteorological yield and MLP trend yield adopted by the model was superior to the fusion strategy of meteorological yield and exponentially smoothed trend yield, achieving a very low mean square error (MSE) of 0.011 and a goodness of fit as high as 0.979, which indicated that the model prediction was highly in agreement with the actual yield, confirming the validity of the method. (2) The prediction curve is basically consistent with the trend of actual sugarcane yield, which predicts that the sugarcane yield in Chongzuo, Guangxi, is expected to maintain a stable and small growth trend in the next eight years. (3) The fusion prediction model proposed in this study provides an accurate and practical solution for sugarcane yield prediction in Chongzuo, Guangxi, with the unique advantage of effectively analyzing and integrating the natural and socio-economic factors affecting the yield, which is of significant reference value for the prediction of sugarcane yield in the local area and even in similar ecoregions.
Uni- and Multivariate Analyses for the Characterization of Popcorn Inbred Lines for Drought Tolerance at Seedling and Vegetative Stages
Authors: Ribeiro Matheus Pereira , Viana José Marcelo Soriano , Gama Guilherme Fontes Vallory , da Silva Laércio Junio , de Oliveira Juraci Alves , Ribeiro Cleberson
Published: October 2024

Water deficit is the main limiting factor in rainfed agricultural production, negatively affecting germination and vegetative development. The objectives of this work were to characterize inbred lines for drought tolerance, to identify the most important root and shoot morphological traits for discriminating genotypes, to compare the efficiency of identifying contrasting inbred lines using uni- and multivariate methods, and to evaluate the effectiveness of identifying drought-tolerant genotypes at seedling and vegetative stages. We assessed 28 popcorn inbred lines and three maize single crosses, two drought-tolerant and one drought-sensitive. At the seedling stage, drought was induced on germitest paper moistened with polyethylene glycol 6000. At the vegetative stage, we applied water stress using lysimeters. We measured root and shoot morphological traits under water stress and no stress and processed the relative values. Uni- and multivariate methods, alone or in combination, were equally efficient for identifying contrasting inbred lines for drought tolerance. The confidence interval and Dunnett’s test worked very well when contrasting controls were included. Principal component analysis allowed to discriminate genotypes and identify the most important traits for discriminating them. At the seedling stage, inbred line 22-1824-2 was the most drought-tolerant and 22-1877-3 the most sensitive. At the vegetative stage, inbred lines 22-1920-1 and 22-1867-4 were the most tolerant and 22-1860-5 was the most sensitive. Root length, volume, dry weight, and surface area, as well as water content, were the most important traits for discriminating genotypes. Due to distinct tolerance mechanisms, drought tolerance should be assessed at both stages.
Characterization of Superoxide Dismutase (SOD) Gene Family and Their Responses to Salinity Stress and Fruit Development in Octoploid Strawberry
Authors: Zhang Yunting , Chen Yan , Gao Weiliang , Tian Su , Lin Bangyu , Gu Xianjie , Lin Yuanxiu , Li Mengyao , Wang Yan , He Wen , Zhang Yong , Chen Qing , Luo Ya , Wang Xiaorong , Tang Haoru
Published: October 2024

Superoxide dismutases (SODs), as the first line of defense against reactive oxygen species (ROS), play an essential role in protecting plants from adverse elicitors during plant growth and development. However, little is known about the SOD gene family and their response to salinity stress and fruit development in cultivated strawberries (Fragaria × ananassa). In this study, 32 SOD genes consisting of 16 Cu/ZnSODs, 11 FeSODs, and 5 MnSOD were identified, which presented three well-resolved clades in the phylogenetic tree. Each clade had similar motifs, and exon–intron structures, which in turn supported the evolutionary classification. Cis-acting element analysis suggested that FaSOD genes might be involved in the plant response to abiotic and biotic stresses, hormones, and light. The analysis of previously published transcriptome data revealed that FaSOD genes are expressed variably under salt stress. Among these SODs, FaMSD5 was expressed at relatively high levels in strawberry root and leaf, and its transcript abundance significantly increased after salt treatment. Some transcription factors related to photomorphogenesis, hormone signaling pathways, and hyperosmotic salinity response were predicted to bind to the FaMSD5 promoter. These outcomes implied that FaMSD5 might play an important role in protection against salt stress. In addition, the comprehensive transcriptome analysis of FaSOD genes in strawberry fruit showed that almost all FaCSDs and FaMSDs were more highly expressed than FaFSDs at different developmental stages, and the expression patterns of FaCSD1, FaCSD2, FaCSD7, FaCSD8, and FaCSD10 suggested that they were likely to be involved in fruit development and ripening. This study provides a basis for further exploration of the function of the FaSOD gene family in strawberry and provides candidate FaSOD genes for enhancing salinity tolerance and regulating fruit development and ripening.
Agave schidigera Transcriptome Reveals Stress-Responsive Phenylalanine ammonia-lyase Genes in Agave
Authors: Wang Xuxia , Hu Xiaoli , Lin Chen , Liu Qingqing , Li Yubo , Du Dengxiang , Mkapa Dietram , Zhang Weiyi , Huang Xing , Yi Kexian
Published: October 2024

Agave is a significant fiber crop in tropical regions, known for its high fiber strength. Lignin is closely associated with fiber strength, and phenylalanine ammonia-lyase (PAL) serves as the initial enzyme in biosynthesis of lignin. Hence, it is of considerable significance to study the genes of PAL family to analyze the characteristics and mechanism of sisal fiber development. In this research, we conducted a transcriptomic analysis of Agave schidigera, a widely recognized ornamental plant in agave. Approximately 29.85 million clean reads were acquired through Illumina sequencing. In total, 116,602 transcripts including 72,160 unigenes were assembled, and 22.06~63.56% of those unigenes were annotated in public databases. Two, six, six and six PAL genes were successfully identified and cloned from A. schidigera, A. deserti, A. tequilana and A. H11648, respectively. After phylogenetic analysis, these genes were clustered into two branches. Genes AhPLA2a and AhPLA2c exhibited higher expression levels compared to other genes but had different expression patterns. Moreover, AhPLA2a and AhPLA2c were expressed at high levels under full-nutrient, nitrogen-free and phosphorus-free stresses. Most PAL genes were induced by Phytophthora nicotianae Breda, especially AhPAL1a, AhPAL1b, AhPAL2b and AhPAL2c. This research is the first work to present a de novo transcriptome dataset for A. schidigera, enriching its bioinformation of transcripts. The cloned PAL genes and the expression analyses will form the basis of future research on lignin biosynthesis, the relationship between lignin and fiber strength, and stress resistance in Agave species.
FEI-YOLO: A Lightweight Soybean Pod-Type Detection Model
Authors: Li Yang , Teng Shirui , Chen Jiajun , Zhou Wan , Zhan Wei , Wang Jun , Huang Lan , Qiu Lijuan
Published: October 2024

Identifying and statistically analyzing soybean pod types are crucial for seed evaluation and yield estimation. Traditional visual assessment by breeding personnel is time-consuming, labor-intensive, and prone to subjective bias, especially with large datasets. Automatic assessment methods usually struggle with the highly confusing pod types with two and three seeds, affecting the model’s identification accuracy. To address these issues, we propose to improve the standard YOLOv5s object detection model to enhance the differentiation between pod types and to boost the model’s efficiency in prediction. To reduce the number of parameters and the computational load, we propose to introduce the FasterNet Block module in the FasterNet model into the original C3 module, leading to improvements in both detection accuracy and speed. To strengthen the feature extraction and representation for specific targets, the Efficient Multi-Scale Attention (EMA) module is incorporated into the C3 module of the backbone network, improving the identification of similar pod types. The Inner-IoU is combined with the CIoU as the loss function to further enhance detection accuracy and generalization. Experiments comparing FEI-YOLO with the baseline YOLOv5s show that FEI-YOLO achieves an mAP@0.5 of 98.6% and an mAP@0.5:0.95 of 81.1%, with improvements of 1.5% and 1.4%, respectively. Meanwhile, the number of parameters is reduced by 13.2%, and FLOPs decreased by 10.8%, demonstrating the model’s effectiveness and efficiency, enabling rapid and accurate identification of soybean pod types from images.
Using Legume-Enriched Cover Crops to Improve Grape Yield and Quality in Hillside Vineyards
Authors: Silvestroni Oriana , Dottori Edoardo , Pallotti Luca , Lattanzi Tania , Santilocchi Rodolfo , Lanari Vania
Published: October 2024

Natural covering (NATC) has spread on hillside vineyards of central Italy as a replacement for tillage to reduce soil erosion, although it increased nitrogen and water needs. Therefore, in the current context of global warming, using cover crops (CCs) that require less water and provide nitrogen becomes crucial. The effects of two low-competition legume-enriched CCs in a rainfed hillside vineyard—a perennial legume–grass mixture (PLGM) and an annual legume cover crop of Trifolium alexandrinum (ALTA)—were compared with NATC over three years. PLGM and ALTA provided good levels of soil coverage, slightly lower than NATC, which had a negligible presence of legumes. PLGM and ALTA, due to low competition, enhanced vine vigor, resulting in thicker and wider canopies (as indicated by total leaf area and leaf layer number), higher pruning weight, and increased yield. PLGM and ALTA led to good qualitative levels, with higher grapes acidities, lower pH and total soluble solids content and, additionally, significantly higher yeast assimilable nitrogen content. In conclusion, implementing low-competition legume species in CCs is an effective tool to avoid soil erosion in a climate change scenario, leading to increased productivity, higher acidity, and improved nitrogen content in the grapes.
Research Progress of Single-Cell Transcriptome Sequencing Technology in Plants
Authors: Bian Jianwen , Zhuang Zelong , Ji Xiangzhuo , Tang Rui , Li Jiawei , Chen Jiangtao , Li Zhiming , Peng Yunling
Published: October 2024

Multicellular organisms exhibit inherent cellular heterogeneity that cannot be captured by traditional high-throughput sequencing techniques, resulting in the unique cellular characteristics of individual cells being neglected. Single-cell transcriptome sequencing (scRNA-seq) technology can be used to determine the gene expression levels of each individual cell, facilitating the study of intercellular expression heterogeneity. This review provides a comprehensive overview of the development and applications of scRNA-seq technology in plant research. We highlight the significance of integrating single-cell multi-omics approaches to achieve a holistic understanding of plant systems. Additionally, we discuss the current challenges and future research directions for scRNA-seq technology in plant studies, aiming to offer valuable insights for its application across various plant species.
Advanced Plant Phenotyping: Unmanned Aerial Vehicle Remote Sensing and CimageA Software Technology for Precision Crop Growth Monitoring
Authors: Fu Hongyu , Lu Jianning , Cui Guoxian , Nie Jihao , Wang Wei , She Wei , Li Jinwei
Published: October 2024

In production activities and breeding programs, large-scale investigations of crop high-throughput phenotype information are needed to help improve management and decision-making. The development of UAV (unmanned aerial vehicle) remote sensing technology provides a new means for the large-scale, efficient, and accurate acquisition of crop phenotypes, but its practical application and popularization are hindered due to the complicated data processing required. To date, there is no automated system that can utilize the canopy images acquired through UAV to conduct a phenotypic character analysis. To address this bottleneck, we developed a new scalable software called CimageA. CimageA uses crop canopy images obtained by UAV as materials. It can combine machine vision technology and machine learning technology to conduct the high-throughput processing and phenotyping of crop remote sensing data. First, zoning tools are applied to draw an area-of-interest (AOI). Then, CimageA can rapidly extract vital remote sensing information such as the color, texture, and spectrum of the crop canopy in the plots. In addition, we developed data analysis modules that estimate and quantify related phenotypes (such as leaf area index, canopy coverage, and plant height) by analyzing the association between measured crop phenotypes and CimageA-derived remote sensing eigenvalues. Through a series of experiments, we confirmed that CimageA performs well in extracting high-throughput remote sensing information regarding crops, and verified the reliability of retrieving LAI (R2 = 0.796) and estimating plant height (R2 = 0.989) and planting area using CimageA. In short, CimageA is an efficient and non-destructive tool for crop phenotype analysis, which is of great value for monitoring crop growth and guiding breeding decisions.
Fusariotoxins Concentration in Common Wheat Grain Depending on the Farming System (Organic vs. Integrated vs. Conventional) and Changes During Grain Processing
Authors: Wysocka Katarzyna , Cacak-Pietrzak Grażyna , Buśko Maciej , Studnicki Marcin
Published: October 2024

Currently, the EU is focusing on less intensive agrotechnology and sustainable development. It is important to minimize the occurrence of mycotoxins (including Fusariotixins) in food, and to monitor mycotoxin concentration in the food chain. Therefore, this study evaluated Fusarium mycotoxin contamination, specifically type A and B trichothecenes and ergosterol concentration, in wheat grain from a three-year field experiment (2019–2021) conducted at IUNG-PIB in Osiny (Poland), along with its byproducts (bran, flour, bread). Four wheat cultivars were grown under different farming systems: organic (ORG), integrated (INT), and conventional (CONV). Ergosterol was analyzed using HPLC with an absorbance detector while Type A and B trichothecenes were analyzed using gas chromatography and mass spectrometry. Results showed that the farming system significantly influenced type B trichothecenes concentration in grain, with the highest concentration established in ORG-grown wheat. However, the grain concentration from the INT farming system was comparable to that from CONV. Type A trichothecenes concentrations were low and not significantly affected by the farming system. Bran exhibited the highest ergosterol and mycotoxin concentration, while flour and bread exhibited the lowest.
Effect of Vermicompost Application on the Soil Microbial Community Structure and Fruit Quality in Melon (Cucumis melo)
Authors: Tian Mei , Yu Rong , Guo Song , Yang Wanbang , Liu Shengfeng , Du Huiying , Liang Jinjin , Zhang Xingxu
Published: October 2024

Melon (Cucumas melon) is widely cultivated and popular because of its quality value and unique flavor. However, the continuous cropping of melons in greenhouses has various negative effects on the soil environment, melon growth, and quality. Recently, farmers have utilized organic fertilization, especially vermicompost, for melons to resist the harmful effects of continuous cropping. A field experiment was conducted to explore the effects of vermicompost on soil microbes and melon fruit quality via high throughput sequencing and chemical sequencing methods. The results showed that the application of vermicompost decreased (p < 0.05) soil pH and increased organic matter, available phosphorus, biomass, urease, catalase, peroxidase, and alkaline phosphatase. A total of 3447 bacterial and 718 fungal operational taxonomic units were identified in all soil samples. Application of vermicompost decreased (p < 0.05) the relative abundances of Acidobacteriota, Gemmatimonadota, Actinobacteriota, and unclassified and increased the relative abundance of Planctomycetota. Compared with the control soil, vermicompost application resulted in significantly higher bacterial Chao indices and a significantly lower Chao index under vermicompost of 60 t ha−1 based on farmers’ normal fertilizer and significantly lower diversity under vermicompost of 90 t ha−1. Otherwise, vermicompost application increased the photosynthetic rate and chlorophyll content of melon leaves and increased the total sugar, soluble solids, vitamin C, soluble protein, and organic acid contents of melon. The results of redundancy analysis indicated that Proteobacteria exhibited a positive correlation with soil ammonium nitrogen (AN) and pH, while showing a negative association with soil available phosphorus and organic matter. Spearman’s correlation analysis revealed that both total sugar content and central soluble solid content in melon had a significant positive correlation (p < 0.05) with Patescibacteria. This study demonstrates that the application of vermicompost alters the microbial community structure in melon cultivation, enhancing fruit quality; this not only promotes a healthier soil ecosystem but also contributes to sustainable and productive practices in melon farming.