Dorsoventral photosynthetic asymmetry of tobacco leaves in response to direct and diffuse light.

Plants can change leaf forms, adjusting light conditions on their adaxial and abaxial surfaces, to adapt to light environments and enhance their light use efficiencies. The difference between photosynthesis on the two leaf sides (dorsoventral asymmetry) is an important factor that affects light use efficiency. However, photosynthetic dorsoventral asymmetry is rarely compared under direct and diffuse light conditions. To estimate the impacts of recently reported alterations in direct and diffuse light in the sky radiation on plant carbon assimilation, variations in morphology between the two leaf sides in tobacco (Nicotiana tabacum L.) were investigated, and the dorsoventral responses of photosynthesis to illuminating directions were compared in direct and diffuse light. Dorsoventral asymmetry was reflected in stomatal densities, anatomic structures, and photochemical traits, which caused markedly different photosynthetic rates as well as stomatal conductances both in direct and diffuse light. However, the degree of photosynthetic asymmetry was weakened in diffuse light. The diffuse light caused a greater stomatal conductance on the abaxial side than direct light, which resulted in reduced photosynthetic asymmetry. In addition, the photosynthetic dorsoventral asymmetry could be affected by the photosynthetic photon flux density. These results contribute to understanding the dorsoventral regulation of photosynthesis in bifacial leaves, and provide a reference for breeding to cope with the increase in the proportion of diffuse light in the future.

The asymmetric photosynthetic characteristics of the isobilateral sorghum leaves under the illumination of the diffuse light.

The difference between photosynthesis on the two leaf sides (dorsoventral asymmetry) of photosynthesis is important for light-use patterns, but the asymmetry is environment dependent. Its role in photosynthetic regulation has been intensively studied, but little is known about the impacts of direct and diffuse light on the asymmetry. Because of the current changing fraction of diffuse light in sky radiation, this study investigated the dorsoventral asymmetry of photosynthetic traits under direct and diffuse light conditions in an important food and energy crop, Sorghum bicolor L. A unique method was used to investigate the specific gas exchange of each leaf surface. Anatomical and morphological traits were different between the two surfaces of sorghum leaves, which might result in photosynthetic asymmetry. The variations in photosynthetic rates and stomatal conductance were significant between the two surfaces in direct and diffuse light, but the degree of dorsoventral asymmetry decreased in diffuse light. The integrated P N and G s of the adaxial illumination were significantly higher than that of abaxial illumination both in direct and diffuse light in sorghum leaves, but the ASI of the integrated P Nwas 2.83 in direct light, while significantly dropped to 1.69 in diffuse light. Significant morphological differences between the two surfaces might cause photosynthetic asymmetry in the sorghum leaves. The variations of specific gas exchange were significant between direct and diffuse light, including in the incident and self-transmitted light. Compared with direct light, diffuse light reduced the stomatal sensitivity, with the degree of decline being greater in the adaxial surface, which caused weak dorsoventral asymmetry in photosynthesis. The specific photosynthetic characteristics in sorghum leaves varied obviously in direct and diffuse light, including in the incident and self-transmitted light, which contributed to the different overall gas exchange. Compared with direct light, the decline of stomatal sensitivity, which showed positive correlation with stomatal density, caused weakened dorsoventral asymmetry in photosynthesis in diffuse light. The findings provide new insights into dorsoventral asymmetry and the impact of diffuse light on photosynthesis in isobilateral leaves.

Image Segmentation under the Optimization Algorithm of Krill Swarm and Machine Learning.

This study aims to improve the efficiency and accuracy of image segmentation, and to compare and study traditional threshold-based image segmentation methods and machine learning model-based image segmentation methods. The krill herb optimization algorithm is combined with the traditional maximum between-class variance function to form a new graph segmentation algorithm. The pet dataset is used to train the algorithm model and build an image semantic segmentation system. The results show that when the traditional Ostu algorithm performs image single-threshold segmentation, the number of iterations is about 256. When double-threshold segmentation is performed, the number of iterations increases exponentially, and the execution time is about 2 s. The number of iterations of the improved Krill Herd algorithm in single-threshold segmentation is 6.95 times, respectively. The execution time for double-threshold segmentation is about 0.24 s. The number of iterations is only improved by a factor of 0.19. The average classification accuracy of the Unet network model and the SegNet network model is 86.3% and 91.9%, respectively. The average classification accuracy of the DC-Unet network model reaches 93.1%. This shows that the proposed fusion algorithm has high optimization efficiency and stronger practicability in multithreshold image segmentation. The DC-Unet network model can improve the image detail segmentation effect. The research provides a new idea for finding an efficient and accurate image segmentation method.

Optimization of a Deep Learning Algorithm for Security Protection of Big Data from Video Images.

With the rapid development of communication technology, digital technology has been widely used in all walks of life. Nevertheless, with the wide dissemination of digital information, there are many security problems. Aiming at preventing privacy disclosure and ensuring the safe storage and sharing of image and video data in the cloud platform, the present work proposes an encryption algorithm against neural cryptography based on deep learning. Primarily, the image saliency detection algorithm is used to identify the significant target of the video image. According to the significant target, the important region and nonimportant region are divided adaptively, and the encrypted two regions are reorganized to obtain the final encrypted image. Then, after demonstrating how attackers conduct attacks to the network under the ciphertext attack mode, an improved encryption algorithm based on selective ciphertext attack is proposed to improve the existing encryption algorithm of the neural network. Besides, a secure encryption algorithm is obtained through detailed analysis and comparison of the security ability of the algorithm. The experimental results show that Bob's decryption error rate will decrease over time. The average classification error rate of Eve increases over time, but when Bob and Alice learn a secure encryption network structure, Eve's classification accuracy is not superior to random prediction. Chosen ciphertext attack-advantageous neural cryptography (CCA-ANC) has an encryption time of 14s and an average speed of 69mb/s, which has obvious advantages over other encryption algorithms. The self-learning secure encryption algorithm proposed here significantly improves the security of the password and ensures data security in the video image.

Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features.

To develop and validate a predictive model based on clinical radiology and radiomics to enhance the ability to distinguish between benign and malignant solitary solid pulmonary nodules. In this study, we retrospectively collected computed tomography (CT) images and clinical data of 286 patients with isolated solid pulmonary nodules diagnosed by surgical pathology, including 155 peripheral adenocarcinomas and 131 benign nodules. They were randomly divided into a training set and verification set at a 7:3 ratio, and 851 radiomic features were extracted from thin-layer enhanced venous phase CT images by outlining intranodal and perinodal regions of interest. We conducted preprocessing measures of image resampling and eigenvalue normalization. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (lasso) methods were used to downscale and select features. At the same time, univariate and multifactorial analyses were performed to screen clinical radiology features. Finally, we constructed a nomogram based on clinical radiology, intranodular, and perinodular radiomics features. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), and the clinical decision curve (DCA) was used to evaluate the clinical practicability of the models. Univariate and multivariate analyses showed that the two clinical factors of sex and age were statistically significant. Lasso screened four intranodal and four perinodal radiomic features. The nomogram based on clinical radiology, intranodular, and perinodular radiomics features showed the best predictive performance (AUC=0.95, accuracy=0.89, sensitivity=0.83, specificity=0.96), which was superior to other independent models. A nomogram based on clinical radiology, intranodular, and perinodular radiomics features is helpful to improve the ability to predict benign and malignant solitary pulmonary nodules.

The changes in functional marker genes associated with nitrogen biological transformation during organic-inorganic co-composting.

In this article, the changes in the functional marker genes associated with nitrogen biological transformation during the organic-inorganic co-composting process with adding biochar or not were studied. Results showed that the addition of biochar increased the abundance of ureC, AOA amoA and AOB amoA gene while decreased the abundance of nirK gene. The addition of 10% biochar by weight ratio was better for the optimization of nitrogen biological transformation process. The correlation analysis showed that the ureC gene was highly correlated with NH4+-N concentration while the AOA amoA and AOB amoA genes were moderately correlated with NH4+-N concentration. Similarly, the nirK gene was moderately correlated with NO3--N concentration. This work would contribute to understanding the mechanisms underlying in the nitrogen bio-transformation further at the molecular level during organic-inorganic co-composting.

Rationale: Photosynthesis of Vascular Plants in Dim Light.

Light dominates the earth's climate and ecosystems via photosynthesis, and fine changes of that might cause extensive material and energy alternation. Dim light (typically less than 5 µmol photons m-2 s-1) occurs widely in terrestrial ecosystems, while the frequency, duration, and extent of that are increasing because of climate change and urbanization. Dim light is important for the microorganism in the photosynthetic process, but omitted or unconsidered in the vascular plant, because the photosynthesis in the high-light adapted vascular leaves was almost impossible. In this review, we propose limitations of photosynthesis in vascular plant leaves, then elucidate the possibility and evidence of photosynthesis in terms of energy demand, stomatal opening, photosynthetic induction, and photosynthesis-related physiological processes in dim light. This article highlights the potential and noteworthy influence of dim light on photosynthesis in vascular plant leaves, and the research gap of dim light in model application and carbon accounting.

Root morphological and proteomic responses to growth restriction in maize plants supplied with sufficient N.

The primary objective of this study was to better understand how root morphological alteration stimulates N uptake in maize plants after root growth restriction, by investigating the changes in length and number of lateral roots, (15)NO(3)(-) influx, the expression level of the low-affinity Nitrate transporter ZmNrt1.1, and proteomic composition of primary roots. Maize seedlings were hydroponically cultured with three different types of root systems: an intact root system, embryonic roots only, or primary roots only. In spite of sufficient N supply, root growth restriction stimulated compensatory growth of remaining roots, as indicated by the increased lateral root number and root density. On the other hand, there was no significant difference in (15)NO(3)(-) influx between control and primary root plants; neither in ZmNrt1.1 expression levels in primary roots of different treatments. Our data suggested that increased N uptake by maize seedlings experiencing root growth restriction is attributed to root morphological adaptation, rather than explained by the variation in N uptake activity. Eight proteins were differentially accumulated in embryonic and primary root plants compared to control plants. These differentially accumulated proteins were closely related to signal transduction and increased root growth.

Cloning and functional analysis of the peanut iron transporter AhIRT1 during iron deficiency stress and intercropping with maize.

In previous research, iron-deficiency symptoms in peanut (Arachis hypgaea) were alleviated during anthesis by intercropping with maize. This benefit was associated with increased phytosiderophore secretion by maize and increased Fe(III)-chelate reductase activity by peanut. In the present study, we isolated the full-length cDNA of AhIRT1 (iron-regulated transporter 1) from peanut and characterized how iron deficiency and intercropping affected its iron-transporting ability. Functional complementation with AhIRT1 restored normal growth of the yeast mutant fet3fet4 (defective in both high- and low-affinity iron-uptake systems) under iron-deficiency conditions. Based on transient expression analysis, AhIRT1 was determined to be a membrane protein, which was consistent with a function in iron uptake. In peanut, transcript levels of AhIRT1 increased in both root and shoot under iron-deficiency conditions. In a pot experiment, AhIRT1 transcript levels in intercropped peanut were 10 times greater during anthesis than pre-anthesis, and transcript levels during anthesis were 40% greater in intercropped than in monocropped peanut.