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Plant seedling morphogenesis is considerably related to photosynthesis, pigment synthesis, and circadian periodicity during seedling development. We identified and cloned a maize zebra or crossbanding leaves mutant wk3735, which produces pale white kernels and was identified and plays a role in the equilibrium of the Redox state the in/out of ETC by active oxygen scavenging. Interestingly, it produces the zebra leaves during the production of the first seven leaves, which is apparently different from the mutation of homologs AtPTOX in Arabidopsis. It is intriguing to investigate how and why yellow crossbands (zebra leaf phenotype) emerge on leaves. As expected, chlorophyll concentration and photosynthetic efficiency both significantly declined in the yellow sector of wk3735 leaves. Meanwhile, we observed the circadian expression pattern of ZmPTOX1, which was further validated by protein interaction assays of the circadian clock protein TIM1 and ZmPTOX1. The transcriptome data of yellow (muW) and green (muG) sectors of knock-out lines and normal leaves of overexpression lines (OE) at the 5th-leaf seedling stage were analyzed. Zebra leaf etiolated sections exhibit a marked defect in the expression of genes involved in the circadian rhythm and rhythmic stress (light and cold stress) responses than green sections. According to the analysis of co-DEGs of muW vs. OE and muG vs. OE, terms linked to cell repair function were upregulated while those linked to environmental adaptability and stress response were downregulated due to the mutation of ZmPTOX1. Further gene expression level analyses of reactive oxygen species (ROS) scavenging enzymes and detection of ROS deposition indicated that ZmPTOX1 played an essential role in plant stress resistance and ROS homeostasis. The pleiotropic roles of ZmPTOX1 in plant ROS homeostasis maintenance, stress response, and circadian rhythm character may collectively explain the phenotype of zebra leaves during wk3735 seedling development.

In recent years, the tourism industry has developed rapidly. However, traditional tourism information has the disadvantages of slow response speed and limited information content, which cannot reflect the evolution trend of spatial and temporal patterns of tourism information in time. Here, based on the Baidu Index, we construct an evaluation framework to analyse the spatial and temporal flow of tourism information in the Chengdu–Chongqing urban cluster from 2011 to 2021. Then, we analyse the urban links between different network levels from the evolution pattern. Finally, we use the geodetector model to analyse its driving mechanism. The results show that Chengdu and Chongqing are the most active cities in the study area in terms of tourism information. The unbalanced development of tourism information between Chengdu and Chongqing and other cities in the region gradually deepens during the period 2011–2019 (polarization effect), but the unbalanced development moderates after 2019. On the other hand, cities in the middle of the Chengdu–Chongqing cluster always have weak agglomeration effects of tourism information. Cities with high tourism information outflow rates in the Chengdu–Chongqing city cluster are mainly concentrated around Chengdu. The average outflow rate of Deyang is the highest, at 27.8%. Cities with low tourist information outflow rates are primarily located in the west, central and south. Ya’an is the city with the lowest outflow rate, with an average of −62.2%. Specifically, Chengdu is the dominant and most radiantly influential city. The tourism information of the Chengdu–Chongqing urban cluster shows a radial network with Chengdu and Chongqing as the core. The driving force analysis shows that the push factor of tourist source, such as the number of people buying pension insurance, is the core driving mechanism, while the pull factor of destination, such as the park green area, and resistance factors such as psychological distance, are in the secondary position. In general, this paper uses Internet tourism data to expand the traditional tourism information research of the Chengdu–Chongqing urban cluster, which can better respond to the changes and needs of the tourism market and provide reference for the spatial optimization of tourism destinations.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is a promising technique that combines the advantages of PET and MR to provide both functional and structural information of the brain. Deep learning (DL) is a subfield of machine learning (ML) and artificial intelligence (AI) that focuses on developing algorithms and models inspired by the structure and function of the human brain’s neural networks. DL has been applied to various aspects of PET/MR imaging in AD, such as image segmentation, image reconstruction, diagnosis and prediction, and visualization of pathological features. In this review, we introduce the basic concepts and types of DL algorithms, such as feed forward neural networks, convolutional neural networks, recurrent neural networks, and autoencoders. We then summarize the current applications and challenges of DL in PET/MR imaging in AD, and discuss the future directions and opportunities for automated diagnosis, predictions of models, and personalized medicine. We conclude that DL has great potential to improve the quality and efficiency of PET/MR imaging in AD, and to provide new insights into the pathophysiology and treatment of this devastating disease.

Anthocyanin, a kind of flavonoid, plays a crucial role in plant resistance to abiotic stress. Salt stress is a kind of abiotic stress that can damage the growth and development of plant seedlings. However, limited research has been conducted on the involvement of maize seedlings in salt stress resistance via anthocyanin accumulation, and its potential molecular mechanism is still unclear. Therefore, it is of great significance for the normal growth and development of maize seedlings to explore the potential molecular mechanism of anthocyanin improving salt tolerance of seedlings via transcriptome analysis. In this study, we identified two W22 inbred lines (tolerant line pur–W22 and sensitive line bro–W22) exhibiting differential tolerance to salt stress during seedling growth and development but showing no significant differences in seedling characteristics under non–treatment conditions. In order to identify the specific genes involved in seedlings’ salt stress response, we generated two recombinant inbred lines (RILpur–W22 and RILbro–W22) by crossing pur–W22 and bro–W22, and then performed transcriptome analysis on seedlings grown under both non–treatment and salt treatment conditions. A total of 6100 and 5710 differentially expressed genes (DEGs) were identified in RILpur–W22 and RILbro–W22 seedlings, respectively, under salt–stressed conditions when compared to the non–treated groups. Among these DEGs, 3160 were identified as being present in both RILpur–W22 and RILbro–W22, and these served as commonly stressed EDGs that were mainly enriched in the redox process, the monomer metabolic process, catalytic activity, the plasma membrane, and metabolic process regulation. Furthermore, we detected 1728 specific DEGs in the salt–tolerant RILpur–W22 line that were not detected in the salt–sensitive RILbro–W22 line, of which 887 were upregulated and 841 were downregulated. These DEGs are primarily associated with redox processes, biological regulation, and the plasma membrane. Notably, the anthocyanin synthesis related genes in RILpur–W22 were strongly induced under salt treatment conditions, which was consistented with the salt tolerance phenotype of its seedlings. In summary, the results of the transcriptome analysis not only expanded our understanding of the complex molecular mechanism of anthocyanin in improving the salt tolerance of maize seedlings, but also, the DEGs specifically expressed in the salt–tolerant line (RILpur–W22) provided candidate genes for further genetic analysis.

Background: The aim of this study is to search for the predictive value of 3D fat analysis and calculation technique (FACT) and intravoxel incoherent motion (IVIM) parameters in identifying osteoporosis in women. Methods: We enrolled 48 female subjects who underwent 3.0 T MRI, including 3D FACT and IVIM sequences. Bone mineral density (BMD) values and Fracture Risk Assessment (FRAX) scores were obtained. Proton density fat fraction (PDFF) in the bone marrow and the real diffusion (D) value of intervertebral discs were measured on 3D FACT and IVIM images, respectively. Accuracy and bias were assessed by linear regression analysis and Bland–Altman plots. Intraclass correlation coefficients were used to assess the measurements’ reproducibility. Spearman’s rank correlation was applied to explore the correlation. MRI-based parameters were tested for significant differences among the three groups using ANOVA analyses. A receiver operating characteristic (ROC) analysis was performed. Results: The PDFF of the vertebral body showed a negative correlation with BMD (R = −0.393, p = 0.005) and a positive correlation with the FRAX score (R = 0.706, p < 0.001). The D value of intervertebral discs showed a positive correlation with BMD (R = 0.321, p = 0.024) and a negative correlation with the FRAX score (R = −0.334, p = 0.019). The area under the curve values from the ROC analysis showed that the 3D FACT and IVIM sequences could accurately differentiate between normal and osteoporosis (AUC = 0.88 using the PDFF; AUC = 0.77 using the D value). The PDFF value demonstrated a sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 78.6%, 89.5%, 84.6%, and 85.0%, respectively, in its ability to predict osteoporosis. The D value had a sensitivity, specificity, PPV, and NPV of 63.16%, 92.9%, 65.0%, and 77.8%, respectively, for predicting osteoporosis. Conclusions: The 3D FACT- and IVIM-measured PDFF and D values are promising biomarkers in the assessment of bone quality and fracture risk.

Typically, sweet corn, particularly sh2 sweet corn, has low seed vigor owing to its high sugar and low starch content, which is a major problem in sweet corn production, particularly at low temperatures. There is considerable variation in the germination rates among sweet corn varieties under low-temperature conditions, and the underlying mechanisms behind this phenomenon remain unclear. In this study, we screened two inbred sweet corn lines (tolerant line L282 and sensitive line L693) differing in their low-temperature germination rates; while no difference was observed in their germination rates at normal temperatures. To identify the specifically induced genes influencing the germination capacity of sweet corn at low temperatures, a transcriptome analysis of the two lines was conducted at both normal and low temperatures. Compared to the lines at a normal temperature, 3926 and 1404 differently expressed genes (DEGs) were identified from L282 and L693, respectively, under low-temperature conditions. Of them, 830 DEGs were common DEGs (cDEGs) that were identified from both L282 and L693, which were majorly enriched in terms of microtubule-based processes, histone H3-K9 modification, single-organism cellular processes, and carbohydrate metabolic processes. In addition, 3096 special DEGs (sDEGs), with 2199 upregulated and 897 downregulated, were detected in the tolerant line L282, but not in the sensitive line L693. These sDEGs were primarily related to plasma membranes and oxygen-containing compounds. Furthermore, electric conductivity measurements demonstrated that the membrane of L282 experienced less damage, which is consistent with its strong tolerance at low temperatures. These results expand our understanding of the complex mechanisms involved in the cold germination of sweet corn and provide a set of candidate genes for further genetic analysis.

Although bulked segregant analysis (BSA) has been used extensively in genetic mapping, user-friendly tools which can integrate current algorithms for researchers with no background in bioinformatics are scarce. To address this issue, we developed an R package, PNGSeqR, which takes single-nucleotide polymorphism (SNP) markers from next-generation sequencing (NGS) data in variant call format (VCF) as the input file, provides four BSA algorithms to indicate the magnitude of genome-wide signals, and rapidly defines the candidate region through the permutation test and fractile quantile. Users can choose the analysis methods according to their data and experimental design. In addition, it also supports differential expression gene analysis (DEG) and gene ontology analysis (GO) to prioritize the target gene. Once the analysis is completed, the plots can conveniently be exported.

Maize originated from tropical regions and is extremely sensitive to low temperature during germination. Our previous work identified a major QTL, qp1ER1-1, for low temperature germination ability (LTGA) of maize. Here, we introgressed qp1ER1-1 from the tolerant line L220 into the sensitive line PH4CV to generate two near isogenic lines NIL220-3 and NIL220-25. When germinated under cold temperature for 25 days (Cold-25), the NILs showed similar seedling root length and shoot length to L220, but significantly higher than PH4CV. However, when germinated under cold temperature for 15 days (Cold-15) or under normal temperature (25 °C) for 3 days (CK-3), all lines showed similar seedling performance, indicating that introgression of qp1ER1-1 from L220 into PH4CV could improve LTGA of NIL220-3 and NIL220-25. The whole seedlings, including root and shoot, of Cold-15 and CK-3 were harvested for transcriptome analysis, when both stayed at a similar developmental stage. Dry seed embryo was sequenced as a non-germination control (CK-0). Compared with PH4CV, the tolerant line (L220, NIL220-3, and NIL220-25) specifically expressed genes (different expressed genes, DEGs) were identified for CK-0, Cold-15, and CK-3. Then, DEGs identified from Cold-15, but not from CK-0 or CK-3, were defined as tolerant line specifically expressed LTGA genes. Finally, 1786, 174, and 133 DEGs were identified as L220, NIL220-3, and NIL220-25 specifically expressed LTGA genes, respectively. Of them, 27 were common LTGA genes that could be identified from all three tolerant lines, with two (Zm00001d031209 and Zm00001d031292) locating in the confidence interval of qp1ER1-1. In addition, GO analysis revealed that L220 specifically expressed LTGA genes were majorly enriched in the cell division process and plasma membrane related categories. Taken together, these results provided new insight into the molecular mechanism of maize seed LTGA and facilitated the cloning of the qp1ER1-1 gene.

The electrocatalytic nitrogen reduction reaction (NRR) is considered a viable alternative to the Haber–Bosch process for ammonia synthesis, and the design of highly active and selective catalysts is crucial for the industrialization of the NRR. Dual-atom catalysts (DACs) with dual active sites offer flexible active sites and synergistic effects between atoms, providing more possibilities for the tuning of catalytic performance. In this study, we designed 48 graphene-based DACs with N4O2 coordination (MM′@N4O2-G) using density functional theory. Through a series of screening strategies, we explored the reaction mechanisms of the NRR for eight catalysts in depth and revealed the “acceptance–donation” mechanism between the active sites and the N2 molecules through electronic structure analysis. The study found that the limiting potential of the catalysts exhibited a volcano-shaped relationship with the d-band center of the active sites, indicating that the synergistic effect between the bimetallic components can regulate the d-band center position of the active metal M, thereby controlling the reaction activity. Furthermore, we investigated the selectivity of the eight DACs and identified five potential NRR catalysts. Among them, MoCo@N4O2-G showed the best NRR performance, with a limiting potential of −0.20 V. This study provides theoretical insights for the design and development of efficient NRR electrocatalysts.

Lectins exist widely in marine bioresources such as bacteria, algae, invertebrate animals and fishes. Some purified marine lectins have been found to elicit cytotoxicity to cancer cells. However, there are few reports describing the cytotoxic effect of marine lectins on cancer cells through virus-mediated gene delivery. We show here that a replication-deficient adenovirus-carrying gene encoding Haliotis discus discus sialic acid binding lectin (Ad.FLAG-HddSBL) suppressed cancer cell proliferation by inducing apoptosis, as compared to the control virus Ad.FLAG. A down-regulated level of anti-apoptosis factor Bcl-2 was suggested to be responsible for the apoptosis induced by Ad.FLAG-HddSBL infection. Further subcellular localization studies revealed that HddSBL distributed in cell membrane, ER, and the nucleus, but not in mitochondria and Golgi apparatus. In contrast, a previously reported mannose-binding lectin Pinellia pedatisecta agglutinin entered the nucleus as well, but did not distribute in inner membrane systems, suggesting differed intracellular sialylation and mannosylation, which may provide different targets for lectin binding. Further cancer-specific controlling of HddSBL expression and animal studies may help to provide insights into a novel way of anti-cancer marine lectin gene therapy. Lectins may provide a reservoir of anti-cancer genes.

Computer vision has been developed greatly in the past several years, and many useful and interesting technologies have been presented and widely applied. Image matching is an important technology based on similarity measurement. In this paper, we propose a novel feature describing model based on scale space and local principle component analysis for heterogeneous image matching. The traditional uniform eight-direction statistics is updated by a task-related k-direction statistics based on prior information of the keypoints. In addition, the k directions are determined by an approximately solution of a Min-Max problem. The principle component analysis is introduced to compute the main directions of local patches based on the gradient field. In addition, the describing vector is formed by then implementing PCA on each sub-patch of a 4 × 4 mesh. Experimental results show the accuracy and efficiency of proposed method.

Many techniques have been developed for computer vision in the past years. Features extraction and matching are the basis of many high-level applications. In this paper, we propose a multi-level features extraction for discontinuous target tracking in remote sensing image monitoring. The features of the reference image are pre-extracted at different levels. The first-level features are used to roughly check the candidate targets and other levels are used for refined matching. With Gaussian weight function introduced, the support of matching features is accumulated to make a final decision. Adaptive neighborhood and principal component analysis are used to improve the description of the feature. Experimental results verify the efficiency and accuracy of the proposed method.