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Central precocious puberty (CPP) is an endocrine disease in children, characterized by rapid genital development and secondary sexual characteristics before the age of eight in girls and nine in boys. The premature activation of the hypothalamic-pituitary-gonadal axis (HPGA) limits the height of patients in adulthood and is associated with a higher risk of breast cancer. How to prevent and improve the prognosis of CPP is an important problem. Vitamin D receptor (VDR) is widely expressed in the reproductive system, participates in the synthesis and function of regulatory sex hormones, and affects the development and function of gonads. In addition, gut microbiota plays an important role in human health by mainly regulating metabolites, energy homeostasis, and hormone regulation. This review aims to clarify the effect of vitamin D deficiency on the occurrence and development of CPP and explore the role of gut microbiota in it. Although evidence on the interaction between vitamin D deficiency, gut microbiota, and sexual development remains limited, vitamin D supplementation and gut microbiota interventions offer a promising, non-invasive strategy for managing CPP., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Guo, Ning, Bai, Tan, Zhou, Guo and Li.)

Representation and learning of concepts are critical problems in data science and cognitive science. However, the existing research about concept learning has one prevalent disadvantage: incomplete and complex cognitive. Meanwhile, as a practical mathematical tool for concept representation and concept learning, two-way learning (2WL) also has some issues leading to the stagnation of its related research: the concept can only learn from specific information granules and lacks a concept evolution mechanism. To overcome these challenges, we propose the two-way concept-cognitive learning (TCCL) method for enhancing the flexibility and evolution ability of 2WL for concept learning. We first analyze the fundamental relationship between two-way granule concepts in the cognitive system to build a novel cognitive mechanism. Furthermore, the movement three-way decision (M-3WD) method is introduced to 2WL to study the concept evolution mechanism via the concept movement viewpoint. Unlike the existing 2WL method, the primary consideration of TCCL is two-way concept evolution rather than information granules transformation. Finally, to interpret and help understand TCCL, an example analysis and some experiments on various datasets are carried out to demonstrate our method's effectiveness. The results show that TCCL is more flexible and less time-consuming than 2WL, and meanwhile, TCCL can also learn the same concept as the latter method in concept learning. In addition, from the perspective of concept learning ability, TCCL is more generalization of concepts than the granule concept cognitive learning model (CCLM).

In the shallow-water waveguide environment, the tonal signals radiated by moving targets carry modal interference and Doppler shift information. The modal interference can be used to obtain the time of the closest point of approach (tCPA) and the ratio of the range at the closest point of approach to the velocity of the source (rCPA/v). However, parameters rCPA and v cannot be solved separately. When tCPA is known, the rCPA and the v of the target can be obtained theoretically by using the Doppler information. However, when the Doppler frequency shift is small or at a low signal-to-noise ratio, there will be a strong parametric coupling between rCPA and v . In order to solve the above parameter coupling problem, a target motion parameter estimation method from tonal signals with a single hydrophone is proposed in this paper. The method uses the Doppler and modal interference information carried by the tonal signals to obtain two different parametric coupling curves. Then, the parametric coupling curves can be used to estimate the two motion parameters. Simulation experiments verified the rationality of this method. The proposed method was applied to the SWellEx-96 and speedboat experiments, and the estimation errors of the motion parameters were within 10%, which shows the method is effective in its practical applications.

Monitoring plant nitrogen (N) in a timely way and accurately is critical for precision fertilization. The imaging technology based on visible light is relatively inexpensive and ubiquitous, and open-source analysis tools have proliferated. In this study, texture- and geometry-related phenotyping combined with color properties were investigated for their potential use in evaluating N in pakchoi ( Brassica campestris ssp. chinensis L.). Potted pakchoi treated with four levels of N were cultivated in a greenhouse. Their top-view images were acquired using a camera at six growth stages. The corresponding plant N concentration was determined destructively. The quantitative relationships between the nitrogen nutrition index (NNI) and the image-based phenotyping features were established using the following algorithms: random forest (RF), support vector regression (SVR), and neural network (NN). The results showed the full model based on the color, texture, and geometry-related features outperforms the model based on only the color-related feature in predicting the NNI. The RF full model exhibited the most robust performance in both the seedling and harvest stages, reaching prediction accuracies of 0.823 and 0.943, respectively. The high prediction accuracy of the model allows for a low-cost, non-destructive monitoring of N in the field of precision crop management.

To find an electrical conductivity (EC) in the nutrient solution used for pakchoi (Brassica campestris L. ssp. Chinensis) cultivation that optimizes the plant's physiology, growth, and quality, we conducted an experiment with eight EC treatments (from EC0 to EC9.6) in a hydroponic production system (i.e. soilless culture) under greenhouse condition in Shanghai, China. Plants biomass production, leaf photosynthesis, vegetable quality variables, tissue nitrate and nitrite contents, and antioxidant enzyme activities were measured. The results showed that very high (EC9.6) or low EC (EC0-0.6) treatments clearly decreased plants fresh weight (FW) and dry weight (DW), leaf size, leaf water content, leaf net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), and taste score. Nitrite content, and antioxidant enzyme activities were low in medium EC treatments (EC1.8 and EC2.4), but high in very high or low EC treatments. Leaf relative chlorophyll, ascorbic acid, and nitrate contents increased gradually from low EC to high EC treatments, while crude fiber and soluble sugar contents decreased. Based on growth and quality criteria, the optimal EC treatment would be EC1.8 or EC2.4 for pakchoi in the hydroponic production system. Too high or too low EC would induce nutrient stress, enhance plant antioxidant enzyme activities, and suppress pakchoi growth and quality., Competing Interests: Co-author Dafeng Hui is Academic Editor for PLOS ONE. Shanghai Dushi Green Engineering Co., Ltd is a commercial company that belongs to Shanghai Academy of Agricultural Sciences. The company provided support in the form of salaries for authors XD, YJ, LH, FL, QZ, JY. The company and founders did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. These do not alter our adherence to PLOS ONE policies on sharing data and materials.

Lettuce is an important leafy vegetable that represents a significant dietary source of antioxidants and bioactive compounds. However, the levels of metabolites in different lettuce cultivars are poorly characterized. In this study, we used combined GC × GC-TOF/MS and UPLC-IMS-QTOF/MS to detect and relatively quantify metabolites in 30 lettuce cultivars representing large genetic diversity. Comparison with online databases, the published literature, standards as well using collision cross-section values enabled putative identification of 171 metabolites. Sixteen of these 171 metabolites (including phenolic acid derivatives, glycosylated flavonoids, and one iridoid) were present at significantly different levels in leaf and head type lettuces, which suggested the significant metabolomic variations between the leaf and head types of lettuce are related to secondary metabolism. A combination of the results and metabolic network analysis techniques suggested that leaf and head type lettuces contain not only different levels of metabolites but also have significant variations in the corresponding associated metabolic networks. The novel lettuce metabolite library and novel non-targeted metabolomics strategy devised in this study could be used to further characterize metabolic variations between lettuce cultivars or other plants. Moreover, the findings of this study provide important insight into metabolic adaptations due to natural and human selection, which could stimulate further research to potentially improve lettuce quality, yield, and nutritional value., Competing Interests: The authors declare that they have no conflict of interest.

Glycine, the simplest amino acid in nature and one of the most abundant free amino acids in soil, is regarded as a model nutrient in organic nitrogen studies. To date, many studies have focused on the uptake, metabolism and distribution of organic nitrogen in plants, but few have investigated the nutritional performance of plants supplied with organic nitrogen. Lettuce ( Lactuca sativa L.), one of the most widely consumed leafy vegetables worldwide, is a significant source of antioxidants and bioactive compounds such as polyphenols, ascorbic acid and tocopherols. In this study, two lettuce cultivars, Shenxuan 1 and Lollo Rossa, were hydroponically cultured in media containing 4.5, 9, or 18 mM glycine or 9 mM nitrate (control) for 4 weeks, and the levels of health-promoting compounds and antioxidant activity of the lettuce leaf extracts were evaluated. Glycine significantly reduced fresh weight compared to control lettuce, while 9 mM glycine significantly increased fresh weight compared to 4.5 or 18 mM glycine. Compared to controls, glycine (18 mM for Shenxuan 1; 9 mM for Lollo Rossa) significantly increased the levels of most antioxidants (including total polyphenols, α-tocopherol) and antioxidant activity, suggesting appropriate glycine supply promotes antioxidant accumulation and activity. Glycine induced most glycosylated quercetin derivatives and luteolin derivatives detected and decreased some phenolic acids compared to nitrate treatment. This study indicates exogenous glycine supplementation could be used strategically to promote the accumulation of health-promoting compounds and antioxidant activity of hydroponically grown lettuce, which could potentially improve human nutrition.

Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

Background: The carbon sequestration potential is affected by cropping system and management practices, but soil organic carbon (SOC) sequestration potential under fertilizations remains unclear in north China. This study examined SOC change, total C input to soil and, via integration of these estimates over years, carbon sequestration efficiency (CSE, the ratio of SOC change over C input) under no fertilization (control), chemical nitrogen fertilizer alone (N) or combined with phosphorus and potassium fertilizers (NP, NK, PK and NPK), or chemical fertilizers combined with low or high (1.5×) manure input (NPKM and 1.5NPKM)., Results: Results showed that, as compared with the initial condition, SOC content increased by 0.03, 0.06, 0.05, 0.09, 0.16, 0.26, 0.47 and 0.68 Mg C ha -1 year -1 under control, N, NK, PK, NP, NPK, NPKM and 1.5NPKM treatments respectively. Correspondingly, the C inputs of wheat and maize were 1.24, 1.34, 1.55, 1.33, 2.72, 2.96, 2.97 and 3.15 Mg ha -1 year -1 respectively. The long-term fertilization-induced CSE showed that about 11% of the gross C input was transformed into SOC pool., Conclusion: Overall, this study demonstrated that decade-long manure input combined with chemical fertilizers can maintain high crop yield and lead to SOC sequestration in north China. © 2016 Society of Chemical Industry., (© 2016 Society of Chemical Industry.)