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Background Leaves and needles are pivotal plant tissues in regulating carbon input, nutrient uptake, and biogeochemical cycling within ecosystems. However, the ecological strategy of broadleaved and coniferous tree species affecting the endophytic colonization of leaf/needle for enabling a nutrient release of subsequent litter is not fully known. The bacterial community assembly, functions, and interactions (summarized as attributes) inhabiting senescing leaves and needles in eleven common tree species of Central European forests were investigated using next generation sequencing. Results Endophytic bacterial attributes varied significantly among tree species and between tree types (broadleaved and coniferous trees). Deterministic processes (nitrogen (N) related factors) governed the endophytic bacterial community assemblages in broadleaved tree species, which were dominated by ureolytic bacteria that potentially caused the increase in inorganic N. In turn, stochasticity (homogenizing dispersal and dispersal limitation) predominantly controlled the community assembly of bacteria inhabiting in needles. Compared with broadleaved, coniferous trees exhibited both more diverse bacterial taxa enlarging the capacity to cope with changing environmental factors. Conclusions Our results reveal the endophytic colonization patterns that initiate litter decomposition during leaf and needle maturation and demonstrate variations between tree types.

Bark is an essential component of tree branches, yet its role in controlling branch-leached dissolved organic matter (DOM) characteristics remains unknown in forests. Here, we collected branches (about 1.5 cm in diameter) of two evergreen coniferous trees, two deciduous broadleaf trees, and three evergreen broadleaf trees from a subtropical forest in southern China, and subsequently used a bark removal experiment to determine the effects of bark on branch-derived DOM quantity and bioavailability. Regardless of tree type, the presence of bark reduced tree branch-leached dissolved organic carbon (DOC), dissolved total nitrogen (DTN), and dissolved total phosphorus (DTP) productions. Moreover, DOC, DTN, and DTP productions leached from the branches containing bark were always much lower than the expected values summed from barks and the branches without bark. The presence of bark increased DOM aromaticity in the broadleaf tree branch leachates but reduced DOM aromaticity in the coniferous tree branch leachates. During 42 days of incubation, the presence of bark decreased broadleaf tree branch-leached DOM bioavailability and the relative increments of aromaticity, whereas the opposite trends were observed for the coniferous tree branch-leached DOM. Tree branch-derived DOM bioavailability correlated negatively with SUVA₂₅₄ values, but exhibited no relationship with either DOC: DTN ratio or DOC:DTP ratio. These observations highlight that tree bark can prevent DOM leaching from branches and regulate branch-leached DOM bioavailability via its effect on DOM aromaticity in subtropical forests.

Background Many tree structures are found in nature and organisms. Such trees are believed to be constructed on the basis of certain rules. We have previously developed grammar-based compression methods for ordered and unordered single trees, based on bisection-type tree grammars. Here, these methods find construction rules for one single tree. On the other hand, specified construction rules can be utilized to generate multiple similar trees. Results Therefore, in this paper, we develop novel methods to discover common rules for the construction of multiple distinct trees, by improving and extending the previous methods using integer programming. We apply our proposed methods to several sets of glycans and RNA secondary structures, which play important roles in cellular systems, and can be regarded as tree structures. The results suggest that our method can be successfully applied to determining the minimum grammar and several common rules among glycans and RNAs. Conclusions We propose integer programming-based methods MinSEOTGMul and MinSEUTGMul for the determination of the minimum grammars constructing multiple ordered and unordered trees, respectively. The proposed methods can provide clues for the determination of hierarchical structures contained in tree-structured biological data, beyond the extraction of frequent patterns.

In July 2020, the Joint Video Experts Team has published the versatile video coding (VVC) standard. The VVC encoder enhances the coding efficiency compared with his predecessor high-efficiency video coding encoder, thanks to the improved coding modules and the new proposed techniques such as the new block partitioning structure called quadtree with nested multi-type tree (QTMT). However, QTMT induces a significant increase in encoding time mainly at the rate distortion optimization level (RDO) which causes an enormous computational complexity. Instead of RDO-QTMT partition process, a deep-QTMT partition approach based on a fast convolution neural network-ternary tree (CNN-TT) is proposed to predict the best intra-QTMT decision tree in order to reduce the encoding time. A database is initially established containing CU-based TT partition depths with several video contents. Then, a CNN-TT model is developed under three-levels provided by the TT structure to early determine the QTMT partition at 32 × 32. Different threshold values are fixed for each level according to the CNN-TT predicted probabilities to reach a balance between the encoding complexity and the coding efficiency. The experimental results prove that our deep-QTMT partition approach saves a significant encoder time on average between 23% and 58% with an acceptable RD performance.

Accurate classification of individual tree types is a key component in forest inventory, biodiversity monitoring, and ecological modeling. This study evaluates and compares multiple Machine Learning (ML) and Deep Learning (DL) approaches for tree type classification based on Airborne Laser Scanning (ALS) data. A mixed-species forest in southeastern Austria, Europe, served as the test site, with spruce, pine, and a grouped class of broadleaf species as target categories. To examine the impact of data representation, ALS point clouds were transformed into four distinct structures: 1D feature vectors, 2D raster profiles, 3D voxel grids, and unstructured 3D point clouds. A comprehensive dataset, combining field measurements and manually annotated aerial data, was used to train and validate 45 ML and DL models. Results show that DL models based on 3D point clouds achieved the highest overall accuracy (up to 88.1%), followed by multi-view 2D raster and voxel-based methods. Traditional ML models performed well on 1D data but struggled with high-dimensional inputs. Spruce trees were classified most reliably, while confusion between pine and broadleaf species remained challenging across methods. The study highlights the importance of selecting suitable data structures and model types for operational tree classification and outlines potential directions for improving accuracy through multimodal and temporal data fusion.

Versatile Video Coding (VVC) achieves impressive coding gain improvement (about 40%+) over the preceding High-Efficiency Video Coding (HEVC) technology at the cost of extremely high computational complexity. Such an extremely high complexity increase is a great challenge for power-constrained applications, such as Internet of video things. In the case of intra coding, VVC utilizes the brute-force recursive search for both the partition structure of the coding unit (CU), which is based on the quadtree with nested multi-type tree (QTMT), and 67 intra prediction modes, compared to 35 in HEVC. As a result, we offer optimization strategies for CU partition decision and intra coding modes to lessen the computational overhead. Regarding the high complexity of the CU partition process, first, CUs are categorized as simple, fuzzy, and complex based on their texture characteristics. Then, we train two random forest classifiers to speed up the RDO-based brute-force recursive search process. One of the classifiers directly predicts the optimal partition modes for simple and complex CUs, while another classifier determines the early termination of the partition process for fuzzy CUs. Meanwhile, to reduce the complexity of intra mode prediction, a fast hierarchical intra mode search method is designed based on the texture features of CUs, including texture complexity, texture direction, and texture context information. Extensive experimental findings demonstrate that the proposed approach reduces complexity by up to 77% compared to the latest VVC reference software (VTM-23.1). Additionally, an average coding time saving of 70% is achieved with only a 1.65% increase in BDBR. Furthermore, when compared to state-of-the-art methods, the proposed method also achieves the largest time saving with comparable BDBR loss. These findings indicate that our method is superior to other up-to-date methods in terms of lowering VVC intra coding complexity, which provides an elective solution for power-constrained applications.

Versatile Video Coding (VVC) achieves higher compression efficiency than the previous High Efficiency Video Coding (HEVC) standard by employing advanced coding tools, including Quad Tree (QT) and Multi-Type Tree (MTT) block partitioning, extended intra prediction modes, and affine motion compensation. Among these tools, the QT-MTT hierarchical partitioning structure significantly increases encoder complexity, since Rate-Distortion Optimization (RDO) must be performed over an exponentially growing number of partition candidates. To mitigate this complexity, a quantization-adaptive early termination method is proposed that combines neural network-based and rule-based partitioning strategies. The proposed decision mechanism significantly reduces the number of Coding Unit (CU) partition candidates, which directly lowers the number of required RDO evaluations and overall encoder complexity. Experimental results demonstrate that the proposed method achieves a 38.28% reduction in encoding time with only a 0.85% increase in Bjøntegaard Delta Bitrate (BD-BR) under the VVC common test conditions. These results indicate that the proposed method effectively balances computational complexity and rate-distortion performance.

Aim Leaf litter decomposition in freshwater ecosystems is a vital process linking ecosystem nutrient cycling, energy transfer and trophic interactions. In comparison to terrestrial ecosystems, in which researchers find that litter traits predominantly regulate litter decomposition worldwide, the dominant factors controlling its decomposition in aquatic ecosystems are still debated, with global patterns not well documented. Here, we aimed to explore general patterns and key drivers (e.g., litter traits, climate and water characteristics) of leaf litter decomposition in streams worldwide. Location Global. Time period 1977–2018. Major taxa studied Leaf litter. Methods We synthesized 1,707 records of litter decomposition in streams from 275 studies. We explored variations in decomposition rates among climate zones and tree functional types and between mesh size groups. Regressions were performed to identify the factors that played dominant roles in litter decomposition globally. Results Litter decomposition rates did not differ among tropical, temperate and cold climate zones. Decomposition rates of litter from evergreen conifer trees were much lower than those of deciduous and evergreen broadleaf trees, attributed to the low quality of litter from evergreen conifers. No significant differences were found between decomposition rates of litter from deciduous and evergreen broadleaf trees. Additionally, litter decomposition rates were much higher in coarse‐ than in fine‐mesh bags, which controled the entrance of decomposers of different body sizes. Multiple regressions showed that litter traits (including lignin, C:N ratio) and elevation were the most important factors in regulating leaf litter decomposition. Main conclusions Litter traits predominantly control leaf litter decomposition in streams worldwide. Although further analyses are necessary to explore whether commonalities of the predominant role of litter traits in decomposition exist in both aquatic and terrestrial ecosystems, our findings could contribute to the use of trait‐based approaches in modelling the decomposition of litter in streams globally and exploring mechanisms of land–water–atmosphere carbon fluxes.

The H.266/Versatile Video Coding (VVC) standard was developed to address the growing demand for compressing ultra-high-definition video content, supporting resolutions ranging from 4K to 8K and beyond. H.266/VVC improves coding efficiency by introducing a flexible quadtree with nested multi-type tree (QT-MTT) partitioning and various advanced coding tools. However, these improvements substantially increase the encoding complexity. To address this issue, we propose a perception-driven and object-aware algorithm that accelerates the MTT process in H.266/VVC intra coding. Our method integrates pixel-level saliency detection with object bounding box detection. Specifically, visually distinguishable (VD) pixels are identified using a just noticeable distortion (JND) model based on average background luminance, while detected-object regions are extracted using a YOLO object detection network. These two types of perceptual information are combined to guide adaptive encoding decisions. For each frame, a perception-driven pixel map labeled with VD pixels and a YOLO-based object map are generated. Within the MTT framework, partitioning decisions are determined jointly by standard deviation metrics derived from VD pixels and detected-object region coverage. By incorporating flexible threshold settings, the proposed method can meet different users’ requirements. In this paper, we performed experiments under three threshold settings. The experimental results demonstrate that the proposed method reduces H.266/VVC intra coding time by 27.94% to 43.11%, with BDBR increases of only 1.02% to 1.53%, thus achieving an appropriate trade-off between encoding speed and coding efficiency.

The latest international video coding standard, H.266/Versatile Video Coding (VVC), supports high-definition videos, with resolutions from 4 K to 8 K or even larger. It offers a higher compression ratio than its predecessor, H.265/High Efficiency Video Coding (HEVC). In addition to the quadtree partition structure of H.265/HEVC, the nested multi-type tree (MTT) structure of H.266/VVC provides more diverse splits through binary and ternary trees. It also includes many new coding tools, which tremendously increases the encoding complexity. This paper proposes a fast intra coding algorithm for H.266/VVC based on visual perception analysis. The algorithm applies the factor of average background luminance for just-noticeable-distortion to identify the visually distinguishable (VD) pixels within a coding unit (CU). We propose calculating the variances of the numbers of VD pixels in various MTT splits of a CU. Intra sub-partitions and matrix weighted intra prediction are turned off conditionally based on the variance of the four variances for MTT splits and a thresholding criterion. The fast horizontal/vertical splitting decisions for binary and ternary trees are proposed by utilizing random forest classifiers of machine learning techniques, which use the information of VD pixels and the quantization parameter. Experimental results show that the proposed algorithm achieves around 47.26% encoding time reduction with a Bjøntegaard Delta Bitrate (BDBR) of 1.535% on average under the All Intra configuration. Overall, this algorithm can significantly speed up H.266/VVC intra coding and outperform previous studies.