Northwest A&F University

Distributed manufacturing and fine-manufacturing are two typical scenarios of modern manufacturing industries in the context of globalization and customization. The distributed differentiation flowshop integrated scheduling problem (DDFISP) is a novel model that deals with the integrated scheduling problem of these two manufacturing scenarios. In the DDFISP, jobs have multiple customized types and are manufactured in a number of distributed factories. Each factory includes three fine-processing stages: parallel machine fabrication, single machine assembly, and dedicated machine differentiation. In the paper, a new distributed memetic evolutionary architecture is first built, which consists of four modules with distinct functions, including global exploration, local exploitation, knowledge transfer, and search restart. The exploration and exploitation are coevolved in the distributed way and communicated by knowledge transfer. This architecture can be used as a universal model to construct evolutionary algorithms. Following this architecture and devising each module innovatively, a novel knowledge transfer-driven distributed memetic algorithm (KTDMA) is constructed to solve the DDFISP. Specifically, global exploration is performed on multiple populations by dynamically selecting global exploration optimizers from predefined external repository. Local exploitation is executed on an independent elite archive by a destruction-construction local search and a key block local search. Knowledge transfer is conducted to communicate the superior information between exploration and exploitation based on a point-ring topology. Search restart is adaptively carried out to alleviate the search homogeneity. Computational results show the effectiveness of the proposed evolutionary architecture and special designs, and demonstrate that the KTDMA performs more competitive than the compared state-of-the-art algorithms.

Compared to existing distributed flowshop scheduling problems (DFSPs), this paper addresses a more realistic DFSP, which integrates intermachine blocking constraints and two customized processing stages of assembly and differentiation. The manufacturing process includes job fabrication in distributed blocking flowshops, job-to-product assembly on an assembling machine, and product fine-processing on differentiation machines. A novel evolutionary framework consisting of continuous space exploration, discrete space exploitation, and dual space knowledge migration is devised. This framework has advanced features of distribution, memetic evolution, and dual-space coevolution, and can serve as a unified model to construct algorithms for different optimization problems. Based on this evolutionary framework, a matrix and learning co-aided distributed dual-space memetic algorithm (DDMA) is proposed to address the studied problem. In DDMA, exploratory population is represented as a real matrix, where individuals have different identities that will dynamically adjust with evolution. In accordance with identity differences, exploratory population is heterogeneously evolved in the continuous search space by a matrix-aided evolutionary optimizer. The exploitative population consists of elite individuals, which are represented as discrete permutations. It is evolved in parallel with exploratory population and in the discrete search space by a learning-aided evolutionary optimizer, including a reinforcement learning-based multi-neighborhood local search and a statistical learning-based enhanced local search. To communicate the superior evolutionary information obtained by exploration and exploitation, an adaptive knowledge migration across continuous and discrete search spaces is proposed based on the impact of migration on the population diversity. The computational results demonstrate the superiority of DDMA over state-of-the-art algorithms.

Multivariate environmental prediction is essential for precisely regulating mushroom house IoT system. Existing time-series prediction methods, such as Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN), consider the temporal features of multiple variables. However, the potential spatial relations between multiple variables cannot be effectively exploited. Especially, sudden environmental disturbances tend to increase the model’s predictive error. To address this challenge, we proposed a multi-input multi-output (MIMO) prediction model employing a spatial-temporal fusion approach. The model combined TCN with Graph Sampling and Aggregation Network-based dynamic graph learning strategy (TCN-DGSA). It achieves the combined prediction of temperature and the humidity in the mushroom houses. First, TCN extracts temporal features from input data, which enhances the model’s ability to capture long-term temporal dependencies through dilation convolution. Additionally, a dynamic graph learning strategy was developed to learn spatial relationships of multiple variables. This strategy constructed implicit graph structures of input features without empirical knowledge. Then, the sampling and aggregation network effectively extracted the spatial pattern of the graph structure, and achieved the accurate multivariate prediction. Finally, the single-step and multi-horizon prediction performance of the model was verified by ablation experiments. The TCN-DGSA model outperforms baseline models, achieving MAE, RMSE, and R2 of 0.21∘ C, 0.30∘ C, and 0.97 for temperature prediction, and 0.53%, 1.02%, and 0.98 for humidity. Further, after adding Gaussian, Poisson, and uniform noise to raw dataset, the model maintained similar MAE and RMSE across different output horizons. This result demonstrates that TCN-DGSA model has high stability and robustness in complex environments.

The phylum Nematoda represents one of the most cosmopolitan and abundant metazoan groups on Earth. In this study, we reconstructed the phylogenomic tree for phylum Nematoda. A total of 60 genomes, belonging to eight nematode orders, were newly sequenced, providing the first low-coverage genomes for the orders Dorylaimida, Mononchida, Monhysterida, Chromadorida, Triplonchida, and Enoplida. The resulting phylogeny is well-resolved across most clades, with topologies remaining consistent across various reconstruction parameters. The subclass Enoplia is placed as a sister group to the rest of Nematoda, agreeing with previous published phylogenies. While the order Triplonchida is monophyletic, it is not well-supported, and the order Enoplida is paraphyletic. Taxa possessing a stomatostylet form a monophyletic group; however, the superfamily Aphelenchoidea does not constitute a monophyletic clade. The genera Trichinella and Trichuris are inferred to have shared a common ancestor approximately 202 millions of years ago (Ma), a considerably later period than previously suggested. All stomatostylet-bearing nematodes are proposed to have originated ~305 Ma, corresponding to the transition from the Devonian to the Permian period. The genus Thornia is placed outside of Dorylaimina and Nygolaimina, disagreeing with its position in previous studies. Additionally, we tested the whole genome amplification method and demonstrated that it is a promising strategy for obtaining sufficient DNA for phylogenomic studies of microscopic eukaryotes. This study significantly expanded the current nematode genome dataset, and the well-resolved phylogeny enhances our understanding of the evolution of Nematoda.

Dual‐source remotely sensed evapotranspiration (ET) models require accurate separation of soil evaporation (Es), plant transpiration (Ec), and precipitation interception (Ei) based on soil and canopy resistances. Despite the availability of several ET products and algorithms, comprehensive evaluations of resistance configurations remain scarce. This study systematically evaluates various combinations of five soil resistance methods, eight canopy resistance methods, and two precipitation interception algorithms within the Shuttleworth‐Wallace (S‐W) framework. Using eddy covariance data from 119 FLUXNET sites and the latest ET products, we find that the Ball‐Berry‐Leuning method, unified stomatal method, and RL empirical method provide comparable and top‐ranked performance across plant functional types (PFTs) and climate zones, with only a single free parameter calibrated by genetic algorithm. The power function method (S2), sensitive to soil surface water content proves to be the most effective for modeling Es, particularly in water‐limited regions. The performance of best‐performing but unexplored combinations (S2‐C1, S2‐C2, S2‐C5) is consistent with PML‐V2, GLEAM4, and underlying water use efficiency model, explaining 56% of the variation in daily ET and achieving an root mean square error as low as 1.02 mm day⁻¹. However, these models show reduced accuracy in arid zones, where prolonged water stress led to a 38% reduction in R². This highlights the need for a more accurate representation of soil moisture stress in arid regions, which is often overlooked in existing models. Our study offers robust, parsimonious, and broadly applicable models for ET estimation across PFTs and climate zones.

Understanding the mechanisms of maternal microbial transmission is crucial for early gut microbiota development and long‐term health outcomes in offspring. However, early maternal microbial interventions remain a challenge due to the complexity of accurately identifying transmitted taxa. Here, the maternal–offspring microbial transmission model (MOMTM), a deep learning framework specifically designed to map maternal microbiota transmission dynamics across pig breeds and developmental stages, is introduced. Using MOMTM, key transmitted taxa, such as the Christensenellaceae R‐7 are successfully predicted, which show high transmission centrality during early development periods. Additionally, it is demonstrated that galacto‐oligosaccharide intervention in sows promotes a Christensenellaceae R‐7‐dominated enterotype and improves fiber digestibility in offspring. Further analysis reveals that Christensenellaceae, particularly Christensenella minuta, have enhanced adhesion and mucin utilization capabilities, facilitating its gut colonization. These findings highlight MOMTM's potential as a novel approach for microbiota‐targeted health interventions in early life, offering insights into strategies that promote gut health and development from birth.

Dogs serve as ideal research subjects for aging studies. In this study, 9 aging-related cell populations are identified through single-cell RNA sequencing of dogs of different ages. Additionally, 9 CD8+ T cell senescence-specific markers conserved across species are identified. Furthermore, multi-omics technology is employed to characterize 17 transcriptional and protein markers, along with 5 metabolic markers, associated with stem cell aging. Penitrem A and UDP-N-acetylglucosamine are further validated as two consistent metabolic markers of both individual and cellular senescence. A customized metabolic assessment system and blood-based assessment framework specifically for aging dogs are developed. Notably, it is demonstrated that mesenchymal stem cells, particularly those overexpressing NMNAT1, can delay or reverse aging in dogs. This study sheds light on the mysteries of aging from multiple perspectives and provides a broad target for future research efforts aimed at uncovering the complexity of this fundamental biological process.

Eucommia ulmoides (EU) is a traditional medicinal plant widely cultivated across China. The combination of EU and feed significantly affects the growth performance, intestinal microbiota composition, and metabolic characteristics of weaned piglets. Forty Landrace x Yorkshire piglets were randomly assigned to four groups: a control group receiving a basal diet, three treatment groups receiving a basal diet supplemented with EU and EU with mix energy (EU+ME), and EU with high protein and energy (EU+HPE), respectively. Growth performance was monitored over a 25-day feeding period, and fecal samples were collected for subsequent metagenomic sequencing and metabolomic analysis. Piglets supplemented with EU, EU+ME, and EU+HPE exhibited significantly improved growth performance, compared to the control group. Metagenomic analysis revealed significant alterations in gut microbiota composition, with increased beneficial bacterial classes and suppression of Prevotella spp. Metabolomic profiling demonstrated distinct metabolic alterations among the treatment groups, with pathway impact analysis highlighting enhanced protein synthesis and energy metabolism. Furthermore, EU supplementation did not affect porcine epidemic diarrhea virus activity in vitro but reduced LPS-induced intestinal inflammation. These findings suggest that EU could be a promising natural additive for improving piglet health and growth, with potential implications for managing post-weaning challenges in swine production.

Background The genetic basis of the phenotypic diversity of pigs is regulated by variants across the genome, especially the trait of early puberty, which is a crucial trait for enhancing the reproductive ability of pigs and the economy of the pig industry. However, the genetic basis of the early puberty trait in pigs remains largely unknown. Results Here, we report a comprehensive genomic variation map for pigs based on the resequencing of 493 accessions representing 59 different pig breeds or populations, which included 5,211,469 single-nucleotide polymorphisms (SNPs) and 487,725 small insertion/deletion structure variants (InDels). This sets included 45,640 high-quality structural variants (SVs). Our results suggested that Hanjiang black (HJB) pigs cluster with Jianghai-type pigs at the genetic level and that the genome characteristics of some HJB individuals exhibit a certain degree of European pig features. Using introgression and signature selection analysis, we identified several candidate genes associated with bone development and early puberty traits, such as TBX5 , PAPPA2 , IGFBP3 , and MKRN3 . Additionally, the GWAS and differential expression analysis results suggested that the PAPPA2 gene is associated with early puberty in pigs. Conclusions This study revealed that past introgression events could impact the agronomical traits of pigs and contribute raw material of genetics and breeding in pig. Moreover, our results suggest that the PAPPA2 gene is a candidate gene associated with early sexual maturity in pigs and the genomic analysis provided important reference value for studying economic traits for pigs.

Reasoned increases in planting density are key measures to enhance maize yields. However, most existing studies on maize planting density based on long time spans often fail to account for diverse microclimates. The impact of planting density on yield components has not also been well investigated in major production regions of China. Therefore, we conducted a meta-analysis of 1951 data pairs from 160 published papers (2013–2023) to assess the effects of increasing planting density on maize yield, yield components, phenotypic traits, and resource utilization and to determine optimal density increase ranges for different environments. The results showed that increasing planting density improved the leaf area index by 23.4%, plant height by 1.8%, aboveground dry matter accumulation by 15.9%, water use efficiency by 3.8%, nitrogen use efficiency and 34.2%, and grain yield by 10.0–11.0%. Dense planting also increased the maize ear number per area by 34.3% but decreased grain number per ear by 12.5%, 1000-grain weight by 7.2%, and harvest index by 2.4%. Notably, the density increase range emerged as the primary factor influencing yield and its components, with changes in grain number per ear the most significant contributor to yield variations. A 25–50% density increase range was identified as optimal, resulting in an 11.5–13.4% yield increase. Average local planting densities were 63,496 plants·ha–1 in the Northwest, 58,928 plants·ha–1 in the Huang-Huai-Hai region, 58,234 plants·ha–1 in the Northeast, and 51,761 plants·ha–1 in the Southwest. Here, we show for the first time that the optimal density increase range varied by region: 25–50% for the Northeast, >50% for the Huang-Huai-Hai and Southwest, and 0–25% for the Northwest. These findings highlight the importance of tailoring planting density to local conditions, offering a scientific basis for optimizing maize production across diverse regions in China.

BACKGROUND Sternochetus mangiferae (Fabricius) is a monophagous beetle that exclusively feeds on mango seeds and is recognized as one of the most destructive quarantine pests worldwide. Despite its considerable ecological and economic impacts, the genomic basis underpinning its host specialization and invasion potential remains poorly understood. RESULTS A high‐quality genomic assembly was generated, totaling 701.87 Mb with a contig N50 of 3.57 Mb, an Illumina read mapping rate of 98.95%, and a BUSCO score of 98.70%. Comparative genomic analyses revealed extensive adaptive remodeling across specific genes and gene families, elucidating the genomic basis of cryptic host adaptation and monophagous feeding. Positive selection signals were identified in key genes associated with chemoperception and detoxification, including ionotropic receptors (Zam04533.t1 and Zam07460.t1), phosphatidylethanolamine‐binding protein (Zam06212.t1), and ABC transporters [Zam09905.t1 (ABCB) and Zam06853.t1 (ABCG)]. Contractions were observed in gene families involved in chemosensory perception, such as gustatory receptors and odorant receptors, and detoxification‐related genes, including GSTs, carboxyl/cholinesterases, UDP‐glucuronosyltransferases, and cytochrome P450 monooxygenases, consistent with adaptation to a concealed ecological niche. Conversely, expansions in plant cell wall degrading enzymes, such as pectinase CE8 and cellulases GH45, likely enhanced the efficient digestion of mango seeds. Resequencing of 104 globally intercepted individuals from 51 countries across 5 continents revealed weak population genetic structure and low genetic diversity, shaped by adaptive constraints and human‐mediated dispersal associated with the globe mango trade. CONCLUSION This study established a genome framework linking chemosensory perception, detoxification, and plant cell wall degradation to host‐specific adaptation of S. mangiferae, reinforcing the critical role of quarantine in limiting its global invasion. © 2025 Society of Chemical Industry.

Key message A comprehensive GWAS reveals key QTLs, canditate genes, and networks underlying wheat lodging resistance. Abstract Lodging is a complex trait and has implications for wheat grain quality and yield, stem-associated traits are crucial for lodging resistance. Understanding the genetic basis of lodging-associated traits, especially single stem elasticity (SSE), stem strength (SS), and lodging index, is essential for developing effective strategies to enhance lodging resistance in wheat. In this study, 10 lodging-associated traits of 238 diverse wheat varieties were investigated across three diverse growing seasons. The ANOVA revealed significant variations on all traits among wheat genotypes and across three growing seasons. There was significant correlations between SSE, SS, three lodging indices (LI 1, LI 2, LI 3) and stem morphological traits. A total of 126 key candidate quantitative trait loci regions containing at least two marker-trait associations were identified using genome-wide association analysis. By integrating multiple analytical approaches, 84 key candidate genes were screened, including genes encoding enzymes related to stem cell wall synthesis, photosynthesis, hormone synthesis, and root development, which may play an important role in lodging resistance. By using Bayesian ridge regression for genome prediction, the prediction accuracy increased as the number of significant SNPs increased, and high prediction accuracy can also be achieved using only a few top-ranking SNPs. In summary, the results of this study would provide valuable insights for understanding lodging resistance in wheat and its genetic mechanism.

Cryptosporidium spp. are important zoonotic pathogens causing diarrhea in humans and various animals. To date, there are still no effective drugs and vaccines for the prevention and treatment of cryptosporidiosis, mainly due to the absence of knowledge on the interaction mechanisms between hosts and Cryptosporidium . Increasing evidence has indicated that long non-coding RNAs (lncRNAs) play significant roles in various physiological and pathological processes. Our previous study showed that Cryptosporidium parvum infection induced significant differential changes in lncRNA expression profiles in HCT-8 cells. BACE1-AS was a significantly downregulated lncRNA in HCT-8 cells during C. parvum infection, but its roles were still unknown. Results of the present study indicated that C. parvum infection significantly downregulated the expression of BACE1-AS probably through regulating the nuclear factor kappa-B (NF-κB) signaling pathway, and the expression of BACE1-AS was negatively related to the propagation of C. parvum in HCT-8 cells. BACE1-AS was mainly localized outside of the nucleus of HCT-8 cells and promoted the expression of IRF3 by sponging miR-6805-5p during C. parvum infection. Further studies indicated that the BACE1-AS/miR-6805-5p/IRF3 axis delayed in vitro propagation of C. parvum through regulating BCL2-mediated apoptosis of HCT-8 cells. The present study enriches our knowledge on the function of host lncRNAs during Cryptosporidium infection and contributes to our understanding of the interaction between hosts and Cryptosporidium . IMPORTANCE Recent studies indicate that Cryptosporidium can regulate cell apoptosis to promote its development in host cells, but the mechanism is still unclear. This study identified a significantly downregulated host lncRNA BACE1-AS during C. parvum infection, which could regulate cell apoptosis to affect the propagation of C. parvum in vitro by targeting the miR-6805-5p/IRF3 axis. The present study contributes to our understanding of the pathogenesis of Cryptosporidium and provides potential targets for the development of drugs and vaccines against cryptosporidiosis.

The application of silica nanoparticles (SNPs) as nanocarriers for delivering nutrients and pesticide components holds great promise, offering the potential to reduce agrochemical usage while enhancing their efficacy. Herein, we initially present the types, synthesisapproaches, and structural features of SNPs as nanoagrochemical delivery carriers. Subsequently, the loading and stimulus-responsive release strategies of guest molecules used for preparation of SNPs-based nanofertilizers and nanopesticides are summarized. The applications and advantages of SNPs-based nanoformulations in nutrient delivery, disease and pest management, and weed control are also discussed. Finally, the aspects that should be taken into consideration in future research and application of SNPs-based nanoagrochemicals are highlighted. This review aims to provide novel insights and comprehensive perspectives for researchers and practitioners striving to enhance the efficiency and environmental sustainability of agrochemical products.

Pseudocydonia sinensis, commonly known as Chinese quince, belongs to the Rosaceae family and is closely related to apple and pear. Despite its botanical significance, genomic resources for this species remain limited. We present a high‐quality, chromosome‐scale, and haplotype‐resolved genome assembly of P. sinensis, characterized by high BUSCO completeness and QV scores. The genome sizes of Haplotypes A and B are 581.29 Mb and 561.80 Mb, respectively, each spanning 17 chromosomes with a contig N50 of 15.76 Mb. Comparative genomic analyses place P. sinensis within the Maleae tribe, closely related to Malus domestica and Pyrus communis, both of which have undergone an additional whole‐genome duplication event. Gene family and metabolomic analyses show that the genome has an expansion of 1603 gene families, with an accumulation of secondary metabolites such as flavonoids and phenolic acids (e.g. rutin and caffeoylquinic acid). This duplication has contributed to the expansion of gene families involved in secondary metabolite biosynthesis, particularly in the phenylpropanoid and flavonoid pathways. Integrative transcriptomic and metabolomic analyses further revealed the MYB transcription factor ODORANT1 (ODO1) is a key regulator of phenylpropanoid metabolism. Functional assays show that PsMYBODO1 directly binds to and activates the promoters of key genes in this pathway, including PsRT3, PsPAL, PsCAD and PsCOMT. This high‐quality reference genome provides a valuable resource for functional genomic studies and breeding programmes aimed at enhancing the medicinal properties of P. sinensis.

Nitrogen (N) application can improve drought tolerance and water use efficiency (WUE) in crops. Previous studies have shown that aerated irrigation improves crop nitrogen absorption and utilization. However, the mechanisms behind the interaction of water and nitrogen under aerated drip irrigation and its impact on crop WUE remain unclear. This study conducted a 2‐years greenhouse experiment with spring‐summer and autumn‐winter tomato to investigate the effects of water and nitrogen coupling on leaf carbon (C) and nitrogen content, photosynthetic characteristics, plant dry matter accumulation, yield, and WUE. The experiment included three irrigation levels (W1, 50% ETc; W2, 75% ETc; W3, 100% ETc) and three nitrogen application rates (N1, 0 kg ha⁻¹; N2, 150 kg ha⁻¹; N3, 250 kg ha⁻¹). The results showed that increased nitrogen application and irrigation levels significantly increased leaf carbon and nitrogen content, net photosynthetic rate (Pn), and stomatal conductance (Gs) (p < 0.05). Under deficit irrigation, nitrogen application increased leaf carbon content by 2.17% and nitrogen content by 9.34%, improved leaf photosynthetic capacity, and increased Pn by 15.57% and Gs by 19.32%. The W2 treatment demonstrated more significant improvement compared to W1. The W3N3 treatment produced the highest plant dry matter accumulation for both tomato types, with no significant difference from W2N3 (p > 0.05). The W2N3 treatment produced the highest yield, 8.67%–9.13% higher than W3N3. The highest WUE occurred in W2N3 for spring‐summer tomato and W1N3 for autumn‐winter tomato. Although W1N3 had 1.02% higher WUE than W2N3, it had a 15.25% lower yield. Thus, W2N3 is recommended as the optimal water‐nitrogen management strategy for greenhouse tomato production. Correlation analysis revealed that leaf carbon and nitrogen contents positively correlated with Pn, plant dry matter accumulation, and yield, whereas the leaf ratio of carbon and nitrogen (C/N) negatively correlated with WUE, suggesting that leaf carbon and nitrogen contents regulate tomato WUE. Nitrogen application under deficit irrigation enhanced leaf carbon and nitrogen contents, photosynthetic capacity (Pn, Gs), plant dry matter accumulation, yield, and WUE. Regression models suggest that the optimal water and nitrogen application rates for greenhouse tomatoes are 192.30–225.67 mm and 205.93–243.43 kg ha⁻¹ for spring‐summer tomato, and 162.00–181.18 mm and 194.98–237.73 kg ha⁻¹ for autumn‐winter tomato. These findings provide a theoretical basis for water‐efficient agricultural practices and sustainable greenhouse tomato production.

Bipolaris sorokiniana is a prevalent fungal pathogen that resides in the soil and affects various parts of wheat, leading to diseases such as spot blotch, common root rot, head blight and black point. The genetic mechanisms that confer resistance in wheat against this pathogen are not completely known. In this research, 1302 wheat germplasms from around the world were evaluated for resistance to spot blotch at the seedling stage, and it was found that merely 3.8% displayed moderate or better resistance. A genome‐wide association study (GWAS) employing high‐density 660K single‐nucleotide polymorphism (SNP) data pinpointed a segment on chromosome 1BL (621.2–674.0 Mb) containing nine SNPs that are significantly linked to spot blotch resistance, named Qsb.hebau‐1BL. RNA sequencing and reverse transcription‐quantitative PCR analyses demonstrated that the gene TraesCS1B02G410300, which codes for nicotinamide‐adenine dinucleotide phosphate‐binding oxidoreductase (TaNADPO), was markedly upregulated by B. sorokiniana. Five SNP variations were identified in the promoter region of TaNADPO in wheat lines with or without Qsb.hebau‐1BL. Wheat lines that overexpressed TaNADPO exhibited increased resistance to spot blotch and higher accumulation of reactive oxygen species (ROS). In contrast, knockout EMS mutants of Triticum turgidum TdNADPO (tdnadpo‐K2561, Gln125*) and TaNADPO (tanadpo‐J10516796, splice donor variant) showed diminished resistance and lower ROS levels. In conclusion, TaNADPO is a key gene for resistance against B. sorokiniana, providing essential information for the development of spot blotch‐resistant wheat varieties through molecular breeding techniques.

Background and aims Organic carbon (C) materials, such as biochar, straw, and organic fertilizer (OF), play a significant role in regulating soil nitrous oxide (N₂O) emissions and the abundance of nitrogen (N)-cycling genes. Previous data-driven studies have evaluated changes in either N₂O emission or N-cycling gene abundance separately under organic C amendments. However, the link between N₂O emission and the abundance of the nosZ gene (encoding N₂O reductase: responsible for reducing N2O to N2) has not been comprehensively analyzed. Methods To address this, a meta-analysis was conducted to assess the effects of biochar, straw, and OF application on soil cumulative N₂O emission or N₂O flux alongside nosZ gene abundance. A boosted regression tree (BRT) model was employed to identify key moderators. Results Biochar application significantly reduced soil cumulative N₂O emission, while straw addition had the opposite effect, and OF amendments showed no significant response. The abundance of the nosZ gene increased after biochar and OF amendments and exhibited a negative correlation (mixed-effects model) with soil N₂O cumulative emission or dynamic flux. Furthermore, drivers governing N2O cumulative emissions or fluxes were distinctly different as indicated by the BRT model, and changes in N₂O flux were largely explained by the (nirS + nirK)/nosZ ratio and abundance of N-cycling genes (amoA-AOA/AOB, nirS, nirK, and nosZ). Conclusion Overall, the enhanced abundance of the nosZ gene following biochar and OF amendments plays a critical role in reducing N₂O emissions. Future research should focus on unraveling the interactions between organic C properties and N-cycling gene abundance, as well as their regulatory mechanisms underlying N₂O emissions. Graphical Abstract

Gut dysmotility is a prevalent gastrointestinal disorder characterized by disrupted defecation and often accompanied by depression and anxiety. Lycopene (LYC) is a type of carotenoid with strong antioxidant and anti-inflammatory...

Key message Map-based cloning revealed that the mutation in a highly conserved amino acid of the CsPBGD, which encodes porphobilinogen deaminase, causes the phenotype of leaf necrosis and enhanced resistance to powdery mildew and gray mold in cucumber. Abstract Lesion mimic mutants (LMMs) are valuable genetic resources for studying programmed cell death (PCD) and disease resistance. Although a number of genes controlling lesion mimic have been identified in model species, none have been mapped or cloned in cucumber. Here, we identified two cucumber mutants, C1173 and C2123, which exhibit leaf necrosis due to PCD. Genetic analysis revealed that these phenotypes are controlled by two semi-dominant loci, ln1 and ln2, respectively. Both mutants were heterozygous, as homozygous dominants were lethal (one caused cotyledon etiolation lethality; the other was unobtainable). Fine mapping placed the ln1 locus within a 54.1 kb region on chromosome 3. Further investigation revealed ln1 and ln2 were allelic mutations, with CsPBGD (CsaV3_3G031800), encoding porphobilinogen deaminase, identified as the candidate gene for both mutants. Mutations in CsPBGD resulted in amino acid substitutions, Ala314Val in ln1 and Arg197Lys in ln2, disrupting enzyme activity and altering H₂O₂ accumulation. CsPBGD expression was significantly reduced in various organs of ln1. VIGS of CsPBGD in both cucumber and tobacco successfully displayed the leaf necrosis phenotype. CsPBGD proteins from both mutants and wild type (WT) were localized in chloroplasts. The mutants exhibited significantly enhanced resistance to powdery mildew (Podosphaera xanthii) and gray mold disease (Botrytis cinerea). Further studies showed that CsPBGD expression in the mutant was significantly more downregulated than in WT after P. xanthii infection, alongside increased H₂O₂ accumulation. This study is the first to characterize and clone CsPBGD in cucumber, revealing its involvement in resistance to disease.