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Abstract Soil organic matter (SOM) plays key roles in sloping land erosion control. This study explores SOM content across Hubei Province, China, focusing on four soil types under various conservation strategies. Field samples (n = 243) were collected under 27 monitoring sites employing diverse conservation strategies in runoff plots. Results indicated substantial variability in SOM content among soil types, with calcareous soils exhibiting the highest levels (12.63 g kg−1). Conversely, red soils displayed the lowest SOM content (6.32 g kg−1). However, short‐term conservation strategies and their interaction with soil type did not significantly influence SOM. The findings underscore the intricate relationship between soil types and SOM dynamics. This study contributes to the understanding of SOM dynamics in diverse landscapes, offering valuable guidance for policymakers and land managers to apply practices in mitigating erosion and enhancing soil health. Core Ideas Soil organic matter (SOM) characteristics focusing on four soil types under various conservation strategies were studied in Hubei Province. Calcareous soils exhibited the highest levels of SOM. Short‐term conservation strategies have no significant effect on SOM for each soil type. Land managers need proper practices to retain soil organic matter in mitigating erosion and enhancing soil health.

Abstract In plants, B3 transcription factors play important roles in a variety of aspects of their growth and development. While the B3 transcription factor has been extensively identified and studied in numerous species, there is limited knowledge regarding its B3 superfamily in pepper. Through the utilization of genome-wide sequence analysis, we identified a total of 106 B3 genes from pepper (Capsicum annuum), they are categorized into four subfamilies: RAV, ARF, LAV, and REM. Chromosome distribution, genetic structure, motif, and cis-acting element of the pepper B3 protein were analyzed. Conserved gene structure and motifs outside the B3 domain provided strong evidence for phylogenetic relationships, allowing potential functions to be deduced by comparison with homologous genes from Arabidopsis. According to the high-throughput transcriptome sequencing analysis, expression patterns differ during different phases of fruit development in the majority of the 106 B3 pepper genes. By using qRT-PCR analysis, similar expression patterns in fruits from various time periods were discovered. In addition, further analysis of the CaRAV4 gene showed that its expression level decreased with fruit ripening and located in the nucleus. B3 transcription factors have been genome-wide characterized in a variety of crops, but the present study is the first genome-wide analysis of the B3 superfamily in pepper. More importantly, although B3 transcription factors play key regulatory roles in fruit development, it is uncertain whether B3 transcription factors are involved in the regulation of the fruit development and ripening process in pepper and their specific regulatory mechanisms because the molecular mechanisms of the process have not been fully explained. The results of the study provide a foundation and new insights into the potential regulatory functions and molecular mechanisms of B3 genes in the development and ripening process of pepper fruits, and provide a solid theoretical foundation for the enhancement of the quality of peppers and their selection and breeding of high-yield varieties.

Abstract Study question To construct prediction models based on the Bayesian network (BN) learning method for the probability of fertilization failure (including low fertilization rate [LRF] and total fertilization failure [TFF]) in assisted reproductive technology (ART) treatment. Summary answer A BN model was developed to predict TFF/LFR. The model showed relatively high calibration in external validation, which could facilitate the identification of risk factors for fertilization disorders and improve the efficiency of in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment. What is known already The prediction of TFF/LFR is very complex. Although some studies attempted to construct prediction models for TFF/LRF, most of the reported models were based on limited variables and traditional regression-based models, which are unsuitable for analyzing real-world clinical data. Therefore, none of the reported models have been widely used in routine clinical practice. To date, BN modeling analysis is a prominent and increasingly popular machine learning method that is powerful in dealing with dynamic and complex real-world data. Study design, size, duration A retrospective study was performed with 106,640 fresh embryo IVF/ICSI cycles from 2009 to 2019 in one of China's largest reproductive health centers. Participants/materials, setting, methods A total of 106, 640 cycles were included in this study, including 97,102 controls, 4,339 LFR cases, and 5,199 TFF cases. Twenty-four predictors were initially included, including 13 female-related variables, five male-related variables, and six variables related to IVF/ICSI treatment. BN modeling analysis with tenfold cross-validation was performed to construct the predictive model for TFF/LFR. The receiver operating characteristic (ROC) curves and the corresponding area under the curves (AUCs) were used to evaluate the performance of the BN model. Main results and the role of chance All twenty-four predictors were first organized into seven hierarchical layers in a theoretical BN model, according to prior knowledge from previous literature and clinical practice. A machine-learning BN model was generated based on real-world clinical data, containing a total of eighteen predictors, of which the infertility type, ART method, and number of retrieved oocytes directly influence the probabilities of LFR/TFF. The prediction accuracy of the BN model was 91.7%. The AUC of the TFF versus control groups was 0.779 (95% CI: 0.766-0.791), with a sensitivity of 71.2% and specificity of 70.1%; the AUC of of TFF versus LFR groups was 0.807 (95% CI: 0.790-0.824), with a sensitivity of 49.0% and specificity of 99.0%. Limitations, reason for caution First, our study was based on clinical data from a single center, and the results of this study should be further verified by external data. In addition, some critical data (e.g., the detailed IVF laboratory parameters of the sperm and oocytes used for insemination) were not available in this study, which should be given full consideration when further improving the performance of the BN model. Wider implications of the findings Based on extensive clinical real-world data, we developed a BN model to predict the probabilities of fertilization failures in ART, which provides new clues for clinical decision-making support for clinicians in formulating personalized treatment plans and further improving ART treatment outcomes. Study funding/competing interest(s) Dr. Y. Wang was supported by grants from the Beijing Municipal Science & Technology Commission (Z191100006619086). We declare that there are no conflicts of interest. Trial registration number N/A.

This paper addresses the challenge of identifying causes for functional dynamic targets, which are functions of various variables over time. We develop screening and local learning methods to learn the direct causes of the target, as well as all indirect causes up to a given distance. We first discuss the modeling of the functional dynamic target. Then, we propose a screening method to select the variables that are significantly correlated with the target. On this basis, we introduce an algorithm that combines screening and structural learning techniques to uncover the causal structure among the target and its causes. To tackle the distance effect, where long causal paths weaken correlation, we propose a local method to discover the direct causes of the target in these significant variables and further sequentially find all indirect causes up to a given distance. We show theoretically that our proposed methods can learn the causes correctly under some regular assumptions. Experiments based on synthetic data also show that the proposed methods perform well in learning the causes of the target.

In this study, pepper and tomato sour soup，the raw materials of red sour soup were taken as the research objects. Illumina MiSeq, total acid titration, gas chromatography-mass spectrometry and other equipment or methods are used to analyze the dynamic trend of microflora, total acid, pH value and volatile substances in the fermentation process of two kinds of sour soup. The results showed that Lactobacillus namurensis (27.67%) and Lactobacillus pentosus (41.75%) were the dominant lactic acid bacteria in pepper and tomato sour soup, respectively; Debaryomyces hansenii (76.05%，pepper sour soup) and Pichia membranifaciens (80.88%，tomato sour soup) were the dominant fungi in the later stage of fermentation. In terms of volatile substances, 156 and 92 compounds such as alcohols, ketones, aldehydes and esters were detected in pepper and tomato sour soup respectively. Among them, esters (24.99%), terpenes (20.21%) and ketones (17.60%) were several compounds with high content in chili acid soup in the early stage of fermentation; In tomato sour soup, aldehydes (41.30%) and ketones (20.28%) were the main compounds. With the change of fermentation, the main flavor compounds of chili acid soup were alcohols and esters, represented by linalool (10.43%) and ethyl hexadecanoate (12.12%); In tomato sour soup, alcohols, esters and phenols were the advantages. Among them, phenylethanol (31.93%) and 4-ethylphenol (8.71%) played an important role in the flavor of tomato sour soup. In general, the dominant functional microorganisms in the naturally fermented sour soup are Lactobacillus and yeast, and the total acid content higher than 25 g/kg can be obtained within 21~28 days after fermentation. The volatile substances vary greatly with the fermentation period. The relevant data can provide a reference for the development of directed vat set for sour soup and the optimization of processing technology.

To explore the component and content of aroma constituents in Manaohong cherry during development, volatile components were determined by headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) technology at green stage, color stage, commercial stage and ripe stage. Relative odor activity value (ROAV) was used for evaluating the contribution of aroma components, in order to determine the key aroma substances. A total of 114 substances were detected at four stages. Among them, 79, 77, 44 and 53 aroma components were detected at green stage, color stage, commercial stage and ripe stage, respectively. The highest relative content was alcohols at green stage. At color stage, commercial stage and ripe stage, the highest relative content was aldehydes. Aldehydes were synthesized continuously from green stage to the maximum value at commercial stage, and then decreased slightly at the ripe stage. On the contrary, esters and alcohols were synthesized during the green and color stages, and decreased to the lowest value at the ripe stage, and then increased slightly at ripe stage. Aromatic aldehydes, C6 aldehydes and C6 alcohols were the most important aroma components of Manaohong cheery, and their relative contents account for 66.22%~96.70% of the total aroma-active compounds. According to ROAV, a total of 20 key aroma components were identified, among which nonanal, linalor and hexanal were the most important ones in Manaohong cherry. (Z)-3-hexenal with green flavor was the only characteristic key aroma component that appeared at commercial stage.

Understanding soil organic carbon (SOC) at the aggregate level is crucial for soil health in secondary-salinized greenhouse development. Nevertheless, the specific patterns and contributions of ion content and microbial communities on SOC density fractions at the aggregate level remain unclear in secondary-salinized soil. We investigated variations in salts [electrical conductivity (EC) and ions] and microbial communities across various aggregate classes in both a 16-year-use greenhouse and open-field soils. We also examined SOC density fractions, including the light fraction (LF), the heavy fraction of particulate organic matter (POM), and mineral-associated organic matter (MOM) across different aggregates. The findings revealed that a lower Ca2+/K+ along with elevated EC levels (average 2.49 mS cm−1) reduced the macroaggregate percentage in greenhouse compared to open-field conditions, with a lower EC of 0.58 mS cm−1. Bacterial diversity and community composition exhibited no variation across different aggregate sizes at both sites. Conversely, fungal diversity and relative abundance (primarily dominated by Ascomycota of 78.50%) substantially increased in microaggregates (0.25 mm). Macroaggregates exhibited a higher proportion of LF and MOM (3.3–18.2%, 24.9–34.5%, and 2.9–4.0% for LF, MOM, and POM, respectively) than microaggregates. Correlation and redundancy analyses revealed that fungal diversity, particularly the relative abundance of Ascomycota in aggregates < 0.25 mm, significantly and positively influenced (p < 0.05) the proportion of MOM carbon in terms of the overall SOC (J-type). This study provides valuable insights into the distribution patterns of SOC within the secondary salt-affected soils.

Abstract The effect of soil organic matter (SOM) on aggregation of variably-charged red soils (Ultisol) through clay zeta potential is not fully understood. Therefore, the objectives of this study were to investigate the SOM effect on the clay zeta potential and soil aggregation after fertilization. Soils under 17 years of fertilization (manure, NPK + straw, NPK, and control (CK) were adjusted by KCl solution to reach varying soil pH and concentration in order to determine clay zeta potential, cations, and aggregate size distribution. The SOM content and C-functional groups by 13C-NMR analysis were also determined. Results showed that the negative zeta potential displayed a bell-shaped pattern with increasing concentration of KCl, but displayed different amplitude of variation among treatments. Manure had the highest zeta potential value and its degree of variation in relative to the value at KCl concentration of 0.1 mol L−1 (19%), NPK + straw and NPK treatments were similar, and CK was the least. Greater negative zeta potential for manure treatment was attributed to higher SOM content, aromatic-C functional groups, and their greater concentrations of Ca2+ and Mg2+ than did the CK. As a result, higher SOM and clay zeta potential yielded in less release of amount of soil particles (

A permanent collapsing gully, locally called Benggang, formed on slopes with deep granite red soil and is a type of unique gully erosion widely prevalent in southern China. Three different soil configurations (SC), ie, red-transition-sandy (SC Ⅰ, the transition is the soil layer between the red soil and the sandy soil layer), transition-sandy (SC Ⅱ) or sandy (SC Ⅲ) are usually present in the soil profile of the Benggang slope. However, little attention has been paid to impacts of SCs on the triggering of Benggang erosion. In this study, we aimed to explore the relationships between soil water content (SWC) and triggering of Benggang erosion under different SC conditions. The soil properties of different soil layers were measured and the SWC at depths of 20, 40, 60, and 80 cm were monitored at 5-min intervals along a typical Benggang (SC Ⅰ) during 2016–2018. The SWC of Benggang slopes with different SCs were simulated by VADOSE/W model. Results showed that the red soil layer had a higher water retention capacity and shear strength than the sandy soil layer. Even if the SWC is higher (e.g., 0.42 cm3/cm3) at red soil layer or transition layer, the corresponding shear strength is greater than that of sandy soil layer with a lower SWC (e.g., 0.32 cm3/cm3). Relationships between shear strength and SWC of different soil layers indicate that Benggang erosion is triggered by an increase in the SWC in the deep sandy layer. Results also showed that differences exist in the SWC distribution among the different SCs. The SWC is higher in topsoil than in deeper soil in SC Ⅰ and SC Ⅱ, while in SC Ⅲ, the opposite trend is observed. These results revealed that the presence of the red soil or transition layer can reduce the infiltration of rainwater into the deep sandy layer, thus can reduce the possibility of collapse. Our results show that the SC affects the stability of the headwall, and results provide great significances to guide the mitigation of Benggang erosion.

Soil physical quality (SPQ) is a limiting factor affecting crop production. However, the impact of gully erosion on the SPQ index S, defined by Dexter as the inflection point of the soil water retention curve (SWRC), remains unclear, especially when considering different latitudinal regions. This study aimed to apply Dexter’s S-theory to evaluate the distribution of index S in black soils adjacent to various gully positions and investigate its relationship with bulk density (Bd), soil organic matter (SOM), and particle percentage. Soil properties (SWRC, Bd, SOM, and particle percentage) from nine gullies in croplands in three latitudinal regions (Harbin, Hailun, and Nenjiang in Heilongjiang province) were determined at the gully edge (GE0) and 50 m beyond the edge into the croplands (GE50) at the following gully units: head, mid-upper, middle, mid-lower, tail, and conjunctions between main gully and gully branch. The S-index was calculated using parameters such as n, θs, and θr, with SWRC data fitted into the van Genuchten model. The results showed spatial variations in the S-index across latitudinal regions, with slightly higher S-values in Harbin than in Hailun and Nenjiang. The S-index also showed noticeable differences at GE0 and GE50 and at the junctions between the main gully and its branches. Approximately 51% of the samples at GE0 and 28.2% of the samples at GE50 had S-values below 0.035, which Dexter proposed as the boundary between good and poor SPQ, indicating a degradation of SPQ at the gully-surrounding areas. A decreased S-index in the gully vicinity was significantly (p < 0.05) associated with increased bulk density (1.33 vs. 1.21 g cm−3 for GE0 and GE50) and decreased SOM (36.80 vs. 39.36 g kg−1 for GE0 and GE50). In summary, this study indicates that gully erosion affects the farmland S-index at the gully-surrounding areas through SOM and Bd. Accordingly, measures suited to the increase in the S-index of the gully-surrounding areas may be implemented to maximize the crop yield of farmlands.

Rice production is intimately related to water and nitrogen management, whereas rice yield inevitably decreases with extreme rainfall. Optimization of water and nitrogen management may mitigate the degree of rice yield reduction. Hence, we conducted a field experiment in a normal and wet year to investigate suitable water and nitrogen management that could reduce the risk of rice yield reduction. The field experiment comprised six water and nitrogen management methods, which included two irrigation methods (CF: continuous flooding; AWD: alternating wet and dry) and three nitrogen fertilizer levels (N0, N90, and N180: 0, 90, and 180 kg N ha−1, respectively). The results showed an average yield reduction of 23.5% in the wet year compared to normal year. The nitrogen rate of N0, N90, and N180 resulted in a 36.9%, 24.8%, and 11.0% of yield reduction in the wet year, which presented a decrease in yield reduction with the increase in nitrogen rate. Panicle contributed over almost 60% and 75% on average to biomass and total nitrogen uptake, but both the total amount and proportion of nitrogen uptake in panicle showed a decrease in the wet year. In addition, the rice yield showed a significant positive correlation with nitrogen uptake both in the normal and wet year. Therefore, in the wet year, the decrease in nitrogen uptake in panicle results in a yield reduction. With the increase in nitrogen rate, the nitrogen internal use efficiency (IEN) was significantly decreased in the normal year, while it increased in the wet year, and the nitrogen recovery use efficiency (REN) and nitrogen harvest index (HIN) were not affected by nitrogen rate. Therefore, these results suggested that increased nitrogen rate in a wet year could improve rice nitrogen uptake to reduce the risk of yield reduction and maintain the nitrogen use efficiency (NUE).

This paper investigates the problem of selecting instrumental variables relative to a target causal influence X→Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders. We propose a necessary condition for detecting variables that cannot serve as instrumental variables. Unlike many existing conditions for continuous variables, i.e., that at least two or more valid instrumental variables are present in the system, our condition is designed with a single instrumental variable. We then characterize the graphical implications of our condition in linear non-Gaussian acyclic causal models. Given that the existing graphical criteria for the instrument validity are not directly testable given observational data, we further show whether and how such graphical criteria can be checked by exploiting our condition. Finally, we develop a method to select the set of candidate instrumental variables given observational data. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method.