Styphnolobium japonicum is a significant resource of ornamental and medicinal plants. In this study, we employed high-throughput sequencing to assemble nine chloroplast genomes of S. japonicum. We compared and reconstructed the phylogenetic relationships of these genomes, along with three publicly available chloroplast genomes. Our results showed that the length of the 12 S. japonicum chloroplast genomes ranged from 158,613 bp to 158,837 bp, all containing 129 unique functional genes. The genetic diversity within S. japonicum chloroplast genomes was relatively low, with π = 0.00029, Theta-W = 0.00028, and an indel frequency of 0.62 indels/1 kb. Among the four regions, the SSC region exhibited the highest genetic diversity and indel frequency, while the IR region had the lowest. Non-coding regions displayed greater genetic variation compared to coding regions, with a few highly variable regions identified. The phylogenetic tree constructed revealed that the major cultivars of S. japonicum originated from two genetic ‘sources. S. japonicum ‘JinhuaiJ2’ had an independent origin and showed close relatedness to S. japonicum var. violacea, S. japonicum var. japonicum, and S. japonicum f. oligophylla. On the other hand, other major cultivars shared a common genetic origin and were closely related to S. japonicum f. pendula. This study highlights the variability of chloroplast genomes within S. japonicum and provides insights into the genetic origins of major cultivars and their relationships with different varieties and forma.

European plum (Prunus domestica L.) is widely cultivated worldwide, with China producing 6.8 million t annually (55% of the global total output). However, the Kashgar region of Xinjiang, China’s primary production area, has experienced outbreaks of brown spot disease caused by Alternaria alternata (Fr.) Keissl. Outbreaks of this disease severely hinder both domestic and global development of the European plum industry. Because this pathogen has a strong latent infection capability during the early stages of disease development, its early detection is important. We develop two detection methods targeting the ITS sequence of A. alternata: LAMP-Cresol Red chromogenic visible endpoint detection and LAMP-SYBR Green I real-time fluorescent detection. Both methods demonstrate high specificity for A. alternata, enabling stable detection of the pathogen in various plant samples; detection limits reach the femtogram (fg) level, significantly surpassing conventional PCR detection capabilities. Development of these highly efficient and precise early detection methods provides a solid foundation for sustainable development of China as a global hub of the European plum industry, and contributes significantly to global disease prevention, control, and industrial stability for this crop.

The Weihe River Basin has a significant number of tributaries and a delicate ecological environment. Understanding the spatial and temporal evolution and determinants of landscape ecological risk in the Weihe River Basin (WRB) can improve the scientific protection and development of its watershed ecosystems. This study is based on land use statistics from the WRB for a 30-year period represented by 1990, 2000, 2010, and 2020. An initial model for the assessment of landscaping ecological hazards was created using the software that was also used to generate the landscape ecological risk index, such as ArcGIS 10.4 and Fragstats 4.2-64. Next, the spatial and temporal evolution of landscape ecological risk in the vicinity of the study area was characterized by the trajectory of the center of gravity migration and the spatial autocorrelation of GeoDa. Finally, Geodetector was used to analyze ecological risk drivers in the landscapes. According to the findings, the high-risk and relatively high-risk regions are steadily expanding, while the low-risk and relatively low-risk areas dominate the ecological risk landscape in the WRB. Within the Weihe River Basin, Xianyang and Xi'an are the areas to which the high-risk centers of gravity are migrating. Positive spatial correlations were found between the landscape ecological hazards in the study area, most prominently in the form of high-high and low-low aggregations. The primary drivers are the interplay between the GDP component, temperature, and elevation as a single factor.

To investigate the codon usage bias (CUB) and its influencing factors in the chloroplast genome of Styphnolobium japonicum f. oligophyllum, we sequenced, assembled and annotated the genome using Illumina high-throughput sequencing, and systematically analyzed 52 protein-coding sequences. The chloroplast genome is 158,837 bp with a typical quadripartite structure, containing 129 functional genes. It presents a mean GC3 content of 28.26% and a mean ENC value of 45.40, indicating weak CUB and low gene expression. Among 31 preferred codons (RSCU > 1), 29 (93.5%) end with A/U. Neutrality plot, ENC-plot and PR2-plot analyses reveal that natural selection is the primary regulator of CUB. A total of 19 optimal codons were identified. These results provide fundamental data for the genetic engineering of S. japonicum f. oligophyllum.

Active learning (AL) promises accelerated multi-objective materials discovery, yet acquisition-function choice is often made without matching dataset, budget, and target metric. We retrospectively compared EI-random, EI-cent, EI-maxmin, and EI-prod on Al alloy, steel, and Cu alloy datasets across 100 randomized cold-start trials, using hypervolume, true Pareto-sample discovery, inverted generational distance, balanced-performance score, and iterations to 95% target. EI-prod is best overall on Al dataset; EI-maxmin improves Pareto-front recovery and convergence on steel and Cu datasets; EI-cent gives limited, inconsistent gains. No strategy dominates all datasets or budgets. These results provide a metric-aware, outcome-specific selection guide: use EI-prod for early balanced candidates, and EI-maxmin for broader Pareto-front exploration. They also warn that similar balanced performance does not ensure equivalent front reconstruction and that dataset-specific rankings should inform, not dictate, AL design in materials screening. This reframes acquisition selection as a decision-making, not universal-ranking, problem.