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Abstract Knowledge diffusion in regional innovation ecosystems is an important factor that influences the regional innovation efficiency. In regional innovation ecosystems under digital empowerment, the knowledge diffusion enables the optimal allocation of innovation resources and promotes the sustainable development and ecological evolution of regional innovation ecosystems. In this paper, a SEIR (Susceptible–Exposed–Infected–Recovered) model is proposed for knowledge diffusion in regional innovation ecosystems under digitization. The basic reproduction number of the proposed model is calculated and its stability is validated. Finally, the expressions for the optimal control system and the optimal control parameters are presented. According to the research conclusions of this paper, the knowledge-diffusion ability of innovation agents in an innovation ecosystem affects the knowledge diffusion in a system; the contact rate between innovation agents affects the efficiency of knowledge diffusion in the system and the structure of the system; the digital transmission ability of innovation agents affects the breadth of knowledge diffusion in the system; and the self-learning ability of innovation agents affects the efficiency of knowledge diffusion in the system.The digital technologies help heterogeneous innovation agents in regional innovation ecosystems to break down the knowledge silos. At the same time, the digital technologies enhance the ability of innovation agents to absorb and learn knowledge in regional innovation ecosystems under digitization, thereby increasing the infection rate of knowledge diffusion in such systems.These conclusions extend the theoretical boundaries of innovation ecosystems and knowledge diffusion and offer management implications for enterprises and governments in decision-making.

Abstract Diagnosing wooden foreign bodies (WFBs) using computed tomography (CT) is often missed, leading to adverse outcomes. This study aims to reduce misdiagnoses by exploring the density variation of blood-saline mixtures in ex vivo models. Twenty Cunninghamia lanceolata sticks, selected as WFB models, were randomly assigned to five groups: a control group (saline) and four experimental groups immersed in blood-saline mixtures with varying concentrations. The samples were then placed in a constant-temperature water bath at 36.8 °C. CT scans were performed in the lowest and highest density areas, and the volume of the low-density areas was measured at the post-processing workstation. Finally, the effects of time and concentration on imaging were analyzed, and fitting curves were generated. The blood-saline mixture concentration and time significantly affected the CT number in the three areas. WFB images changed dynamically over time, with two typical imaging signs: the bull's-eye sign on the short axis images and the tram line sign on the long axis images. Fitting curves of the CT number in the lowest density areas with different concentrations can quantify imaging changes. The CT number of the lowest density areas increased with time, following a logarithmic function type, while the CT number of the highest density areas exhibited a fast-rising platform type. The volume of the low-density areas decreased over time. The time of damage caused by WFBs and the influence of varying blood and tissue fluid contents at the damaged site should be considered in the diagnosis. Imaging changes from multiple CT scans at different times can aid in diagnosis.

Abstract The study is to evaluate the performance of ocular biometric measures and uncorrected visual acuity (UCVA) for detecting myopia among Chinese students. Among 5- to 18-year-old Chinese students from two cities of China, trained eye-care professionals performed assessment of ocular biometrics (axial length (AL), corneal curvature radius (CR), anterior chamber depth) under noncycloplegic conditions using NIDEK Optical Biometer AL-Scan, distance visual acuity using retro-illuminated logMAR chart with tumbling-E optotypes, and cycloplegic refractive error using NIDEK autorefractor with administration of 0.5% tropicamide. Spherical equivalent (SER) in diopters (D) was calculated as sphere plus half cylinder, and myopia was defined as SER ≤ − 0.5 D. Performances of ocular biometrics and UCVA (individually and in combination) for detecting myopia were evaluated using sensitivity and specificity, predictive values, and area under ROC curve (AUC) in both development dataset and validation dataset. Among 3436 students (mean age 9.7 years, 51% female), the mean (SD) cycloplegic SER was − 0.20 (2.18) D, and 1269 (36.9%) had myopia. Cycloplegic SER was significantly correlated with AL (Pearson Correlation coefficient r = − 0.82), AL/CR ratio (r = − 0.90), and UCVA (r = 0.79), but was not correlated with CR (r = 0.02, p = 0.15). The AL/CR ratio detected myopia with AUC 0.963 (95% CI 0.957–0.969) and combination with UCVA improved the AUC to 0.976 (95% CI 0.971–0.981). Using age-specific AL/CR cutoff (> 3.00 for age 3.06 for 10–14 years, > 3.08 for ≥ 15 years) as myopia positive, the sensitivity and specificity were 87.0% (95% CI 84.4–89.6%) and 87.8% (86.0–89.6%), respectively, in the development dataset and 86.4% (95% CI 83.7–89.1%) and 89.4% (95% CI 87.3–91.4%), respectively, in the validation dataset. Combining AL/CR and UCVA (worse than 20/32 for age

As for the problem that the traditional single depth prediction model has poor strain capacity to the prediction results of time series data when predicting lake eutrophication, this study takes the multi-factor water quality data affecting lake eutrophication as the main research object. A deep reinforcement learning model is proposed, which can realize the mutual conversion of water quality data prediction models at different times, select the optimal prediction strategy of lake eutrophication at the current time according to its own continuous learning, and improve the reinforcement learning algorithm. Firstly, the greedy factor, the fixed parameter of Agent learning training in reinforcement learning, is introduced into an arctangent function and the mean value reward factor is defined. On this basis, three Q estimates are introduced, and the weight parameters are obtained by calculating the realistic value of Q, taking the average value and the minimum value to update the final Q table, so as to get an Improved MIMO-DD-3Q Learning model. The preliminary prediction results of lake eutrophication are obtained, and the errors obtained are used as the secondary input to continue updating the Q table to build the final Improved MIMO-DD-3Q Learning model, so as to achieve the final prediction of water eutrophication. In this study, multi-factor water quality data of Yongding River in Beijing were selected from 0:00 on July 26, 2021 to 0:00 on September 5, 2021. Firstly, data smoothing and principal component analysis were carried out to confirm that there was a certain correlation between all factors in the occurrence of lake eutrophication. Then, the Improved MIMO-DD-3Q Learning prediction model was used for experimental verification. The results show that the Improved MIMO-DD-3Q Learning model has a good effect in the field of lake eutrophication prediction.

Bacillus thuringiensis, known to be one of the most important biocontrol microorganisms, contains three AA10 family lytic polysaccharide monooxygenases (LPMOs) in its genome. In previous reports, two of them, BtLPMO10A and BtLPMO10B, have been preliminarily characterized. However, some important biochemical features and substrate preference, as well as their potential applications in chitin degradation, still deserve further investigation. Results from present study showed that both BtLPMO10A and BtLPMO10B exhibit similar catalytic domains as well as highly conserved substrate-binding planes. However, unlike BtLPMO10A, which has comparable binding ability to both crystalline and amorphous form of chitins, BtLPMO10B exhibited much stronger binding ability to colloidal chitin, which mainly attribute to its carbohydrate-binding module-5 (CBM5). Interestingly, the relative high binding ability of BtLPMO10B to colloidal chitin does not lead to high catalytic activity of the enzyme. In contrast, the enzyme exhibited higher activity on β-chitin. Further experiments showed that the binding of BtLPMO10B to colloidal chitin was mainly non-productive, indicating a complicated role for CBM5 in LPMO activity. Furthermore, synergistic experiments demonstrated that both LPMOs boosted the activity of the chitinase, and the higher efficiency of BtLPMO10A can be overridden by BtLPMO10B.

Yellow seed is a desirable trait in Brassica oilseed crops. The B. rapa var. Yellow Sarson carry unique yellow seed color genes which are not only important for the development of yellow-seeded oilseed B. rapa cultivars but this variant can also be used to develop yellow-seeded B. napus. In this study, we developed near-isogenic lines (NILs) of Yellow Sarson for the major seed coat color QTL SCA9-2 of the chromosome A9 and used the NILs to fine map this QTL region and to identify the candidate genes through linkage mapping and transcriptome sequencing of the developing seeds. From the 18.4 to 22.79 Mb region of SCA9-2, six SSR markers showing 0.63 to 5.65% recombination were developed through linkage analysis and physical mapping. A total of 55 differentially expressed genes (DEGs) were identified in the SCA9-2 region through transcriptome analysis; these included three transcription factors, Bra028039 (NAC), Bra023223 (C2H2 type zinc finger), Bra032362 (TIFY), and several other genes which encode unknown or nucleic acid binding protein; these genes might be the candidates and involved in the regulation of seed coat color in the materials used in this study. Several biosynthetic pathways, including the flavonoid, phenylpropanoid and suberin biosynthetic pathways were significantly enriched through GO and KEGG enrichment analysis of the DEGs. This is the first comprehensive study to understand the yellow seed trait of Yellow Sarson through employing linkage mapping and global transcriptome analysis approaches.

The recessive genetic male sterility (RGMS) system plays a key role in the production of hybrid varieties in self-pollinating B. napus plants, and prevents negative cytoplasmic effects. However, the complete molecular mechanism of the male sterility during male-gametogenesis in RGMS remains to be determined. To identify transcriptomic changes that occur during the transition to male sterility in RGMS, we examined the male sterile line WSLA and male fertile line WSLB, which are near-isogenic lines (NILs) differing only in the fertility trait. We evaluated the phenotypic features and sterility stage using anatomical analysis. Comparative RNA sequencing analysis revealed that 3,199 genes were differentially expressed between WSLA and WSLB. Many of these genes are mainly involved in biological processes related to flowering, including pollen tube development and growth, pollen wall assembly and modification, and pollen exine formation and pollination. The transcript profiles of 93 genes associated with pollen wall and anther development were determined by quantitative RT-PCR in different flower parts, and classified into the following three major clades: (1) up-regulated in WSLA plants; (2) down-regulated in WSLA plants; and 3) down-regulated in buds, but have a higher expression in stigmas of WSLA than in WSLB. A subset of genes associated with sporopollenin accumulation were all up-regulated in WSLA. An excess of sporopollenin results in defective pollen wall formation, which leads to male sterility in WSLA. Some of the genes identified in this study are candidates for future research, as they could provide important insight into the molecular mechanisms underlying RGMS in WSLA.