Explore the vast range of titles available.

Summary China is the origin and evolutionary centre of Oriental pears. Pyrus betuleafolia is a wild species native to China and distributed in the northern region, and it is widely used as rootstock. Here, we report the de novo assembly of the genome of P. betuleafolia‐Shanxi Duli using an integrated strategy that combines PacBio sequencing, BioNano mapping and chromosome conformation capture (Hi‐C) sequencing. The genome assembly size was 532.7 Mb, with a contig N50 of 1.57 Mb. A total of 59 552 protein‐coding genes and 247.4 Mb of repetitive sequences were annotated for this genome. The expansion genes in P. betuleafolia were significantly enriched in secondary metabolism, which may account for the organism's considerable environmental adaptability. An alignment analysis of orthologous genes showed that fruit size, sugar metabolism and transport, and photosynthetic efficiency were positively selected in Oriental pear during domestication. A total of 573 nucleotide‐binding site (NBS)‐type resistance gene analogues (RGAs) were identified in the P. betuleafolia genome, 150 of which are TIR‐NBS‐LRR (TNL)‐type genes, which represented the greatest number of TNL‐type genes among the published Rosaceae genomes and explained the strong disease resistance of this wild species. The study of flavour metabolism‐related genes showed that the anthocyanidin reductase (ANR) metabolic pathway affected the astringency of pear fruit and that sorbitol transporter (SOT) transmembrane transport may be the main factor affecting the accumulation of soluble organic matter. This high‐quality P. betuleafolia genome provides a valuable resource for the utilization of wild pear in fundamental pear studies and breeding.

Lignin, a natural organic polymer that is recyclable and inexpensive, serves as one of the most abundant green resources in nature. With the increasing consumption of fossil fuels and the deterioration of the environment, the development and utilization of renewable resources have attracted considerable attention. Therefore, the effective and comprehensive utilization of lignin has become an important global research topic, with the goal of environmental protection and economic development. This review focused on the bacteria and enzymes that can bio-transform lignin, focusing on the main ways that lignin can be utilized to produce high-value chemical products. Bacillus has demonstrated the most prominent effect on lignin degradation, with 89% lignin degradation by Bacillus cereus . Furthermore, several bacterial enzymes were discussed that can act on lignin, with the main enzymes consisting of dye-decolorizing peroxidases and laccase. Finally, low-molecular-weight lignin compounds were converted into value-added products through specific reaction pathways. These bacteria and enzymes may become potential candidates for efficient lignin degradation in the future, providing a method for lignin high-value conversion. In addition, the bacterial metabolic pathways convert lignin-derived aromatics into intermediates through the “biological funnel”, achieving the biosynthesis of value-added products. The utilization of this “biological funnel” of aromatic compounds may address the heterogeneous issue of the aromatic products obtained via lignin depolymerization. This may also simplify the separation of downstream target products and provide avenues for the commercial application of lignin conversion into high-value products.

Background Pyrus ussuriensis Maxim. are rich in nutrients, with a pleasant aroma and postharvest softening properties. Postharvest softening influences shelf life of fruit and fruit quality. Melatonin is a natural and safe preservative, which can effectively maintain fruit quality after harvesting, and delay softening of fruit. The aim of study was to elucidate mechanism of pear fruit softening and fruit aroma during postharvest storage and effect of melatonin. Results Ethylene production rate, respiration rate, weight loss of fruit, soluble solid content, titratable acidity were assessed, and transmission electron microscopy, metabolite profiling, and whole-transcriptome RNA-sequencing were performed. Four important pathways that pentose and glucuronate interconversion, galactose metabolism, sphingolipid metabolism and the starch and sucrose metabolism pathway were involved in pear fruit softening. Ethylene production pathway-related genes, such as ACS and ACO were involved in pear fruit softening and expression of that under exogenous melatonin treatment were slightly inhibited. Fruit aroma changed after storage mainly through lipoxygenase pathway under ddH 2 O treatment and exogenous melatonin treatment changed composition of volatile organic compounds. CeRNA networks associated with pear softening and aroma were established. Mdm-miR159a, mdm-miR396a/b-p3 and mdm-miR408a were found to modulate both fruit softening and aroma formation through ceRNA analysis. Mdm-miR10988-p3 was functionally diverse and as major regulatory components in ceRNA network. Conclusions This study indicated that degradation of cell wall caused pear fruit softening, lipoxygenase pathway mainly affected change of fruit aroma during postharvest storage and exogenous melatonin treatment could improve fruit firmness after storage and alter pear's aroma. The mechanism underlying these effects was elucidated, providing theoretical basis for study of pear fruit softening and preservation technology.

The accurate and efficient identification of pear varieties is paramount to the intelligent advancement of the pear industry. This study introduces a novel approach to classifying pear varieties by recognizing their leaves. We collected leaf images of 33 pear varieties against natural backgrounds, including 5 main cultivation species and inter-species selection varieties. Images were collected at different times of the day to cover changes in natural lighting and ensure model robustness. From these, a representative dataset containing 17,656 pear leaf images was self-made. YOLOv10 based on the PyTorch framework was applied to train the leaf dataset, and construct a pear leaf identification and classification model. The efficacy of the YOLOv10 method was validated by assessing important metrics such as precision, recall, F1-score, and mAP value, which yielded results of 99.6%, 99.4%, 0.99, and 99.5%, respectively. Among them, the precision rate of nine varieties reached 100%. Compared with existing recognition networks and target detection algorithms such as YOLOv7, ResNet50, VGG16, and Swin Transformer, YOLOv10 performs the best in pear leaf recognition in natural scenes. To address the issue of low recognition precision in Yuluxiang, the Spatial and Channel reconstruction Convolution (SCConv) module is introduced on the basis of YOLOv10 to improve the model. The result shows that the model precision can reach 99.71%, and Yuluxiang’s recognition and classification precision increased from 96.4% to 98.3%. Consequently, the model established in this study can realize automatic recognition and detection of pear varieties, and has room for improvement, providing a reference for the conservation, utilization, and classification research of pear resources, as well as for the identification of other varietal identification of other crops.

Background Pear is one of the most popular and widely cultivated fruits globally, with rich cultivar diversity. Pyrus species are characterized by self-incompatibility and the absence of reproductive barriers between species, leading to extensive gene flow and genetic recombination among different types of cultivars. As a result, cultivar identification technologies have considerable practical significance in pear production. Multiple nucleotide polymorphism (MNP) technology, which combines multiplex PCR amplification and high-throughput sequencing, offers high efficiency and accuracy in pear cultivar identification, and meets the needs of cultivar innovation and industry development. Results We applied MNP technology to pear cultivar identification for the first time, establishing fingerprints for major pear cultivars and exploring the feasibility of MNP markers for pear population structure analysis. Based on genomic resequencing data from 143 pear accessions, 558 marker loci were initially developed, and 310 core markers were retained after screening. The 310 MNP markers showed high polymorphism, with an average of 18.14 alleles per marker locus and PIC values ranging from 0.57 to 0.99. Validation using 76 representative pear cultivars demonstrated reproducibility and accuracy rates exceeding 99% for the 310 MNP markers. A total of 2,850 pairwise comparisons among the 76 cultivars showed an average genetic differentiation of 90.89%. Population structure analyses based on MNP markers effectively reflected the classification relationships among the 76 cultivars, clearly distinguishing European from Asian pears. Conclusion In summary, the developed pear MNP markers possess high polymorphism, stability, and cultivar-discrimination capability, promising extensive future applications in pear cultivar identification and population genetic research.

Flesh texture is an important quality trait and is related to people’s preference for fruit, especially for crisp pears. Puncture tests were carried out on 156 crisp pear fruit germplasm samples to analyze the diversity level of texture traits, to clarify the correlation between sensory description evaluation and instrumental traits, and to explore the effects of fruit ripening, size, and shelf life on the change in flesh texture. The results showed that puncture parameters were significantly different between crisp pear cultivars, and the work associated with the flesh limit compression force had the highest coefficient of variation (0.281). There was a significant correlation between puncture parameters and sensory evaluation scores. The correlation between sensory score and flesh firmness was the highest, with a correlation coefficient of 0.708, indicating that hardness can significantly influence the sensory evaluation of texture. Cluster analysis based on sensory evaluation and puncture determination could divide the germplasm resources of crisp pear into five texture categories: loosen, crunchy, crisp, tight–crisp, and dense. A comprehensive texture score model, constructed by principal component analysis, showed consistency with sensory evaluation scores and proved that the combination of a puncture test and sensory evaluation is the best way to identify and evaluate the texture of crisp pear. Further analysis of the influencing factors of flesh texture showed that fruit maturity and shelf life had significant effects on flesh quality. This study provides an important reference for the standardization, evaluation, and utilization of crisp pear variety resources.

Objective To construct a recurrence prediction model for Elderly Schizophrenia Patients (ESCZP) and validate the model’s spatial external applicability. Methods The modeling cohort consisted of 365 ESCZP cases from the Seventh People’s Hospital of Dalian, admitted between May 2022 and April 2024. Variables were selected using Lasso-Logistic regression to construct the recurrence prediction model, with a nomogram plotted using the “RMS” package in R 4.3.3 software. Model validation was performed using 1,000 bootstrap resamples. Spatial external validation was conducted using 172 cases ESCZP from the Fourth Affiliated Hospital of Qiqihar Medical College during the same period. The model’s discrimination, accuracy, and clinical utility were assessed using Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), calibration curves, and Decision Curve Analysis (DCA). The Hosmer-Lemeshow test was used to evaluate model fit. Results A total of 537 cases ESCZP were included based on inclusion and exclusion criteria, with 150 recurrences within two years and 387 non-recurrences. Lasso-Logistic regression analysis identified Medication status, Premorbid personality, Exercise frequency, Drug adverse reactions, Family care, Social support, and Life events as predictors of ESCZP recurrence. The AUC for the modeling cohort was 0.877 ( 95% CI : 0.837–0.917). For the external validation cohort, the AUC was 0.838 ( 95% CI : 0.776–0.899). Calibration curves indicated that the fit was close to the reference line, demonstrating high model stability. DCA results showed good net benefit at a threshold probability of 80%. Conclusion The nomogram prediction model developed based on Lasso-Logistic regression shows potential in identifying the risk of recurrence in ESCZP. However, further validation and refinement are needed before it can be applied in routine clinical practice.

The renewable pine needles was used as an adsorbent to remove phosphorus from aqueous solutions. Using batch experiments, pine needles pretreated with alkali-isopropanol (AI) failed to effectively remove phosphorus, while pine needles modified with lanthanum hydroxide (LH) showed relatively high removal efficiency. LH pine needles were effective at a wide pH ranges, with the highest removal efficiency reaching approximately 85% at a pH of 3. The removal efficiency was kept above 65% using 10 mg/L phosphorus solutions at desired pH values. There was no apparent significant competitive behavior between co-existing anions of sulfate, nitrate, and chloride (SO4(2-), NO3(-) and Cl(-)); however, CO3(2-) exhibited increased interfering behavior as concentrations increased. An intraparticle diffusion model showed that the adsorption process occurred in three phases, suggesting that a boundary layer adsorption phenomena slightly affected the adsorption process, and that intraparticle diffusion was dominant. The adsorption process was thermodynamically unfavorable and non-spontaneous; temperature increases improved phosphorus removal. Total organic carbon (TOC) assays indicated that chemical modification reduced the release of soluble organic compounds from 135.6 mg/L to 7.76 mg/L. This new information about adsorption performances provides valuable information, and can inform future technological applications designed to remove phosphorus from aqueous solutions.

Bisphenol A (BPA) pollution poses an increasingly serious problem. BPA has been detected in a variety of environmental media and human tissues. Microbial degradation is an effective method of environmental BPA remediation. However, BPA is also biotoxic to microorganisms. In this study, Rhodococcus equi DSSKP-R-001 (R-001) was used to degrade BPA, and the effects of BPA on the growth metabolism, gene expression patterns, and toxicity-resistance mechanisms of Rhodococcus equi were analyzed. The results showed that R-001 degraded 51.2% of 5 mg/L BPA and that 40 mg/L BPA was the maximum BPA concentration tolerated by strain R-001. Cytochrome P450 monooxygenase and multicopper oxidases played key roles in BPA degradation. However, BPA was toxic to strain R-001, exhibiting nonlinear inhibitory effects on the growth and metabolism of this bacterium. R-001 bacterial biomass, total protein content, and ATP content exhibited V-shaped trends as BPA concentration increased. The toxic effects of BPA included the downregulation of R-001 genes related to glycolysis/gluconeogenesis, pentose phosphate metabolism, and glyoxylate and dicarboxylate metabolism. Genes involved in aspects of the BPA-resistance response, such as base excision repair, osmoprotectant transport, iron-complex transport, and some energy metabolisms, were upregulated to mitigate the loss of energy associated with BPA exposure. This study helped to clarify the bacterial mechanisms involved in BPA biodegradation and toxicity resistance, and our results provide a theoretical basis for the application of strain R-001 in BPA pollution treatments.

Wild Ussurian Pear germplasm resource has rich genetic diversity, which is the basis for genetic improvement of pear varieties. Accurately and efficiently identifying wild Ussurian Pear accession is a prerequisite for germplasm conservation and utilization. We proposed YOLOv10n-MCS, an improved model featuring: (1) Mixed Local Channel Attention (MLCA) module for enhanced feature extraction, (2) Simplified Spatial Pyramid Pooling-Fast (SimSPPF) for multi-scale feature capture, and (3) C2f_SCConv backbone to reduce computational redundancy. The model was trained on a self-made dataset of 16,079 wild Ussurian Pear leaves images. Experiment results demonstrate that the precision, recall, mAP50, parameters, FLOPs, and model size of YOLOv10n-MCS reached 97.7(95% CI: 97.18 to 98.16)%, 93.5(95% CI: 92.57 to 94.36)%, 98.8(95% CI: 98.57 to 99.03)%, 2.52M, 8.2G, and 5.4MB, respectively. The precision, recall, and mAP50 are significant improved of 2.9%, 2.3%, and 1.5% respectively over the YOLOv10n model (p<0.05). Comparative experiments confirmed its advantages in precision, model complexity, model size, and other aspects. This lightweight model enables real-time wild Ussurian Pear identification in natural environments, providing technical support for germplasm conservation and crop variety identification.