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Introduction: Resprouting is a crucial survival strategy following the loss of branches, being it by natural events or artificially by pruning. The resprouting prediction on a physiological basis is a highly complex approach. However, trained gardeners try to predict a tree’s resprouting after pruning purely based on their empirical knowledge. In this study, we explore how far such predictions can also be made by machine learning. Methods: Table-topped annually pruned Platanus × hispanica trees at a nursery were LiDAR-scanned for two consecutive years. Topological structures for these trees were abstracted by cylinder fitting. Then, new shoots and trimmed branches were labelled on corresponding cylinders. Binary and multiclass classification models were tested for predicting the location and number of new sprouts. Results: The accuracy for predicting whether having or not new shoots on each cylinder reaches 90.8% with the LGBMClassifier, the balanced accuracy is 80.3%. The accuracy for predicting the exact numbers of new shoots with the GaussianNB model is 82.1%, but its balanced accuracy is reduced to 42.9%. Discussion: The results were validated with a separate dataset, proving the feasibility of resprouting prediction after pruning using this approach. Different tree species, tree forms, and other variables should be addressed in further research. [ABSTRACT FROM AUTHOR]

Introduction Silicon (Si) is not an essential element for the growth of plants, but its role in improving the yield by reducing the environmental stresses and crop durability is very important. Despite the abundance of Si in the soil, the amount of dissolved Si available for herbal absorption might be limited. Therefore, using this useful element through foliar application can be effective in the growth of the plants such as eggplant. On the other hand, profitability is dependent on the high yield and quality per unit area in producing greenhouse vegetables.Materials and Methods To study the effect of foliar application of Si fertilizer from the source of Khazra Chelated Silicon (commercial brand, contains 2% chelated silicon for plant at pH 3 to 11 and no silicate in the form of salt) in different stages of the growth of eggplant and bush pruning in hybrid eggplant, a greenhouse experiment was conducted in a soil environment as a split-plot in a randomized complete block design with 3 replications in Minab region. In this experiment, the consumption of Si fertilizer as the main factor at 4 levels (0 and 2 per thousand in the vegetative phase, 2 per thousand in the reproductive phase and 2 per thousand at the two vegetative and reproductive phases) and the type of pruning as a sub-factor in 4 levels (two-branch training system, three-branch training system, four-branch training system and no training system) were applied. Eggplant seeds were transplanted in an environment containing sphagnum in November and then at four-leaf stage they were transferred to the main field in December. The distance between the rows was one meter and the distance between the plants in the row was 45 cm. After positioning the plant in the greenhouse and reaching the appropriate growth, pruning and foliar application of Si fertilizer began and in the treatment without fertilizer, spraying with pure water was used. Silicon fertilizer was sprayed on the eggplant leaves according to the instructions recommended by the manufacturer every eight days. Once the eggplants reached a suitable reproductive growth and started fruiting, fruit thickness, fruit length, the weight of each fruit, the number of the fruits on each plant, and finally the yield of each plant were recorded in each pick. To evaluate the effect of Si on the durability of eggplant after picking, 3 fruits harvested from each treatment were first weighed by a digital scale, and then kept at room temperature for 2 weeks and after two weeks, the weight of each fruit was measured again. Based on two recorded weights, the percentage of fruit weight loss was calculated. Furthermore, to evaluate the effect of silicon fertilizer and plant pruning on resistance to fungal diseases, scores, from 1 to 9, were given to the plants in each treatment. Plants without any fungal infection were assigned a score of 1, and completely infected plants were assigned a score of 9.Results and Discussion The results showed that pruning improved the fruit length, the plant height, the fruit weight in each plant, and the plant yield and reduced the percentage of fungal infection in the plant. Moreover, the silicon foliar application positively affected the number of the fruits in each plant and increased the durability of fruits after harvesting. Silicon foliar application in the two vegetative and reproductive stages together and plant pruning in a four-branch way resulted in maximum plant yield, improved crop quality, and improved greenhouse management. The intensity of fungal infection was affected by different levels of silicon, plant pruning, and the interaction of the two factors at a probability level of five percent. Comparison of interaction averages showed that pruning and foliar application in vegetative and reproductive stages together significantly reduced fungal infection in the greenhouse. The highest intensity of fungal infection was related to plants without pruning and lack of silicon. Pruning improves airflow and modulates humidity in the plant canopy and as a result eliminates the proper conditions for fungal growth. Silicon also prevents fungal penetration into plant cells by depositing in the plant cell wall and strengthening plant tissue, and as a result inhibits fungal growth in the plant. Marschner (2012) reported that silicon would increase resistance to fungal and bacterial diseases and pests. Plant diseases are a major threat to agricultural production because they cause a serious loss of yield and quality of the crop. Numerous studies have reported that Si is effective in controlling diseases caused by fungal and bacterial pathogens in various plant species. Furthermore, given that one of the challenges for producers to deliver high-quality crops to the target market is their durability after harvesting, it seems that silicon foliar application at the two vegetative and reproductive stages might be effective to overcome such a challenge. Choosing a training system depends to a large extent on the ease of work in the greenhouse, the production system, and the superiority of the production rate. On the other hand, a significant increase in yield and crop quality was reported for barley, rice, sugarcane, tomato, cucumber, soybean, and bamboo due to Si consumption during the growth. Another important feature of silicon is that it increases plant damage under biological and non-biological stress conditions as well as resistance against stress.Conclusion According to the results, four-branch training system of eggplant and also silicon foliar application in the two vegetative and reproductive phases of greenhouse eggplant improved the yield components and increased the durability after harvesting. Pruning the plant into four branches also improved yields. Therefore, using silicon with four-branch training system is recommended, especially in traditional greenhouses without proper ventilation systems.

IntroductionManaging the degradation and loss of Sudanian woodlands to ensure sustainable use for mainly firewood, poles, and timber is receiving increasing attention, and it requires tailored strategies to improve stand productivity. This study determined: (i) tree response (density, recruitment, and height growth) to selective stem thinning and branch pruning; and (ii) tree species that are most affected by selective stem thinning and branch pruning in the Sudanian woodland of Benin.MethodsThree homogeneous vegetation units of 80 m × 80 m each were identified, representing the three stages of woodland development (WDS): early and young WDS of 3 and 3–5 years old, respectively, and the advanced to mature secondary WDS of 6–9 years old. Three random blocks of 20 m × 20 m each, subdivided into four 10 m × 10 m treatment plots, were delineated per vegetation unit. Each of these plots was randomly assigned a treatment (T): T1—no thinning and no pruning; T2—30% thinning; T3—60% thinning; T4—100% thinning. Branch pruning was applied to all remaining stems in T2 and T3. Tree species, stem abundance and height of individuals = 1 m were recorded every 6 months in 2015 and 2016.Results and discussionThinning intensity significantly influence tree density both in WDS 2 and 3 with the 30% thinning showing the best tree density (3355.56 ± 250.19 stems/ha and 3255.56 ± 772.68 stems/ha, respectively). However, more stem recruitment was observed without thinning and pruning (883.33 ± 212.13 stems/ha), and 30% thinning and pruning (383.33 ± 164.99 stems/ha). There was a significant increase in tree height growth both in WDS 2 and 3 with an increasing thinning intensity. The two most affected tree species were Terminalia avicennioides and Pteleopsis suberosa with an average growth in height after 6 months of 105.5 ± 1.05 mm (for 30% thinning and pruning in stage 3), and 61.75 ± 0.67 mm (for 60% thinning and pruning in stage 3), respectively. Overall, moderate stem thinning (30%) at WDS 1 and more severe thinning at WDS 2 and 3 resulted in the best tree height growth and recruitment.

Introduction: Resprouting is a crucial survival strategy following the loss of branches, being it by natural events or artificially by pruning. The resprouting prediction on a physiological basis is a highly complex approach. However, trained gardeners try to predict a tree's resprouting after pruning purely based on their empirical knowledge. In this study, we explore how far such predictions can also be made by machine learning., Methods: Table-topped annually pruned Platanus × hispanica trees at a nursery were LiDAR-scanned for two consecutive years. Topological structures for these trees were abstracted by cylinder fitting. Then, new shoots and trimmed branches were labelled on corresponding cylinders. Binary and multiclass classification models were tested for predicting the location and number of new sprouts., Results: The accuracy for predicting whether having or not new shoots on each cylinder reaches 90.8% with the LGBMClassifier, the balanced accuracy is 80.3%. The accuracy for predicting the exact numbers of new shoots with the GaussianNB model is 82.1%, but its balanced accuracy is reduced to 42.9%., Discussion: The results were validated with a separate dataset, proving the feasibility of resprouting prediction after pruning using this approach. Different tree species, tree forms, and other variables should be addressed in further research., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Shu, Yazdi, Rötzer and Ludwig.)

Ras is a central cellular hub protein controlling multiple cell fates. How Ras interacts with a variety of potential effector proteins is relatively unexplored, with only some key effectors characterized in great detail. Here, we have used homology modeling based on X-ray and AlphaFold2 templates to build structural models for 54 Ras-effector complexes. These models were used to estimate binding affinities using a supervised learning regressor. Furthermore, we systematically introduced Ras "branch-pruning" (or branchegetic) mutations to identify 200 interface mutations that affect the binding energy with at least one of the model structures. The impacts of these branchegetic mutants were integrated into a mathematical model to assess the potential for rewiring interactions at the Ras hub on a systems level. These findings have provided a quantitative understanding of Ras-effector interfaces and their impact on systems properties of a key cellular hub. [Display omitted] • Structural models of 54 Ras-effector complexes were generated • Predicted affinities for Ras RBD interactions span 4 orders of magnitude • Branchegetic Ras mutations bind only a subset of RBDs and cause network rewiring • Tissue-specific competition model predicts Ras RBD rewiring into distinct states Ras proteins play an important role in cancer. Junk and Kiel report a mathematical model describing the competition of effector proteins for binding to Ras based on binding affinities predicted from structural models. They also explore the structural impact of interface mutations and their potential for rewiring the system. [ABSTRACT FROM AUTHOR]

There have been mixed reports of the link between salt uptake and transpiration in plants. An accumulation of Na and CI in plants exposed to salinity may be linked to total plant transpiration because both ions are absorbed by roots from the soil and transported to shoots in the transpiration stream. Therefore, despite its potential negative effects on plant productivity, a reduction of transpiration area may be beneficial to plants growing in salt-affected sites. In this study, we examined the effects of seedling size and branch pruning on total transpiration and accumulation of Na and CI in Pinus leiophylla seedlings. The seedlings were divided into two different height groups (short and tall), subjected to the branch pruning treatment, and then treated with 150 mM NaCl that was added to the soil for 30 days. Total transpiration was significantly reduced by branch pruning with short seedlings having the lowest total transpiration levels. After 30 days of treatment, NaCl significantly decreased total transpiration, stomatal conductance, net photosynthesis, root hydraulic conductance, and plant dry weights. The concentration of Na and CI in shoots increased in all NaCl-treated plants, but short seedlings of the pruned group had lower ion concentration levels and exhibited lower needle necrosis, and greater recovery of the measured variables compared with the remaining experimental groups. We conclude that plant transpiration is a significant factor affecting the uptake and transport of Na and CI in P. leiophylla seedlings and that branch pruning may be an effective treatment in increasing the chances of survival with the short seedlings after planting in salt-affected areas [ABSTRACT FROM AUTHOR]

We describe a multiple description (MD) video coding technique using 3D-Set Partitioning in Hierarchical Tree algorithm (3D-SPIHT). Multiple description coding (MDC) is a way of coding video information into multiple bitstreams and is useful in a number of scenarios including peer-to-peer video streaming and video transmission over error-prone networks. We propose a modified tree structure for 3D-SPIHT that is more efficient for MD coding. We also propose a branch-pruning technique to generate multiple descriptions. We then show how rate-distortion optimization can be incorporated into the MDC framework to develop an efficient MD video coding system. Experimental results show that the proposed MD system achieves significant improvement in performance compared to existing methods. [ABSTRACT FROM AUTHOR]

A curve skeleton is a compact representation of 3D objects and has numerous applications. It can be used to describe an object's geometry and topology. In this paper, we introduce a novel approach for computing curve skeletons for volumetric representations of the input models. Our algorithm consists of three major steps: 1) using iterative least squares optimization to shrink models and, at the same time, preserving their geometries and topologies, 2) extracting curve skeletons through the thinning algorithm, and 3) pruning unnecessary branches based on shrinking ratios. The proposed method is less sensitive to noise on the surface of models and can generate smoother skeletons. In addition, our shrinking algorithm requires little computation, since the optimization system can be factorized and stored in the precomputational step. We demonstrate several extracted skeletons that help evaluate our algorithm. We also experimentally compare the proposed method with other well-known methods. Experimental results show advantages when using our method over other techniques. [ABSTRACT FROM AUTHOR]