Unique Tree Research Articles

On the Correctness of Maximum Parsimony for Data with Few Substitutions in the NNI Neighborhood of Phylogenetic Trees

AbstractEstimating phylogenetic trees, which depict the relationships between different species, from aligned sequence data (such as DNA, RNA, or proteins) is one of the main aims of evolutionary biology. However, tree reconstruction criteria like maximum parsimony do not necessarily lead to unique trees and in some cases even fail to recognize the “correct” tree (i.e., the tree on which the data was generated). On the other hand, a recent study has shown that for an alignment containing precisely those binary characters (sites) which require up to two substitutions on a given tree, this tree will be the unique maximum parsimony tree. It is the aim of the present paper to generalize this recent result in the following sense: We show that for a tree T with n leaves, as long as $$k<\frac{n}{8}+\frac{11}{9}-\frac{1}{18}\sqrt{9\cdot \left( \frac{n}{4}\right) ^2+16}$$ k < n 8 + 11 9 - 1 18 9 · n 4 2 + 16 (or, equivalently, $$n>9k-11+\sqrt{9k^2-22k+17}$$ n > 9 k - 11 + 9 k 2 - 22 k + 17 , which in particular holds for all $$n\ge 12k$$ n ≥ 12 k ), the maximum parsimony tree for the alignment containing all binary characters which require (up to or precisely) k substitutions on T will be unique in the NNI neighborhood of T and it will coincide with T, too. In other words, within the NNI neighborhood of T, T is the unique most parsimonious tree for the said alignment. This partially answers a recently published conjecture affirmatively. Additionally, we show that for $$n\ge 8$$ n ≥ 8 and for k being in the order of $$\frac{n}{2}$$ n 2 , there is always a pair of phylogenetic trees T and $$T'$$ T ′ which are NNI neighbors, but for which the alignment of characters requiring precisely k substitutions each on T in total requires fewer substitutions on $$T'$$ T ′ .

TTMGNet: Tree Topology Mamba-Guided Network Collaborative Hierarchical Incremental Aggregation for Change Detection

Change detection (CD) identifies surface changes by analyzing bi-temporal remote sensing (RS) images of the same region and is essential for effective urban planning, ensuring the optimal allocation of resources, and supporting disaster management efforts. However, deep-learning-based CD methods struggle with background noise and pseudo-changes due to local receptive field limitations or computing resource constraints, which limits long-range dependency capture and feature integration, normally resulting in fragmented detections and high false positive rates. To address these challenges, we propose a tree topology Mamba-guided network (TTMGNet) based on Mamba architecture, which combines the Mamba architecture for effectively capturing global features, a unique tree topology structure for retaining fine local details, and a hierarchical feature fusion mechanism that enhances multi-scale feature integration and robustness against noise. Specifically, the a Tree Topology Mamba Feature Extractor (TTMFE) leverages the similarity of pixels to generate minimum spanning tree (MST) topology sequences, guiding information aggregation and transmission. This approach utilizes a Tree Topology State Space Model (TTSSM) to embed spatial and positional information while preserving the global feature extraction capability, thereby retaining local features. Subsequently, the Hierarchical Incremental Aggregation Module is utilized to gradually align and merge features from deep to shallow layers to facilitate hierarchical feature integration. Through residual connections and cross-channel attention (CCA), HIAM enhances the interaction between neighboring feature maps, ensuring that critical features are retained and effectively utilized during the fusion process, thereby enabling more accurate detection results in CD. The proposed TTMGNet achieved F1 scores of 92.31% on LEVIR-CD, 90.94% on WHU-CD, and 77.25% on CL-CD, outperforming current mainstream methods in suppressing the impact of background noise and pseudo-change and more accurately identifying change regions.

Roost characteristics and fidelity of silver‐haired bat maternity colonies in a floodplain cottonwood forest

AbstractThe maternal roosting behavior of migratory, tree‐dwelling bat species remains poorly understood. From 2016 to 2020 we radio‐tagged 53 adult female silver‐haired bats (Lasionycteris noctivagans) and tracked them to their day roosts to evaluate roosting habits in a cottonwood (Populus) forest in the floodplain of the Bitterroot River in western Montana. We investigated roost tree characteristics of maternity colonies such as canopy closure, tree size, and decay stage, as well as the daily roost fidelity of tagged bats. Additionally, we marked 55 bats with passive integrated transponder (PIT) tags to assess interannual fidelity to summer habitat. We tracked bats to 94 unique roost trees. Based on previous studies, we expected silver‐haired bat maternity colonies to roost in tall, large diameter trees. We modeled roosting probability as a function of tree height, day of year, and the interaction of these terms. We found bats preferred taller trees earlier in the year, but overall roosting probability decreased throughout the season and tree height had lesser influence on roosting probability later in the year. Bats used tall trees with high canopy closure in early stages of decay during periods of pregnancy and lactation, while the effect of these tree attributes lessened later in the season, perhaps after lactation when energetic constraints were reduced for adult females to prepare for migration by entering torpor during the day. Bats used an average of 5.2 roost trees during the young rearing period and remained in the same roost for an average of 1.5 consecutive days. While daily roost fidelity was low, we documented interannual fidelity to summer habitat from nine individuals. We found silver‐haired bat roosting preferences vary with time, are tree‐specific, and policies targeting conservation of the species should manage flood regimes to promote cottonwood establishment, height growth, and a mosaic of riparian habitats.

Molecular-properties based formulation guidance tree for amorphous and supersaturable mesoporous silica preparations of poorly soluble compounds

A huge majority of new chemical entities (NCEs) advancing through the drug discovery pipeline often have poor aqueous solubility. This requires formulation scientists to search for solubility enhancement strategies, within the constraints of time and material. To address these challenges, a strategic platform formulation is often required for a rapid compound screening to enable early exploratory PK and toxicology studies. Through this work, we present an option of a material-sparing, high yielding and solubility-enabling amorphous API and HPMCAS-L co-loaded mesoporous silica-based formulation. The usability of this platform formation strategy was assessed for a physico-chemically diverse set of eleven compounds. The formulation approach was successful in stabilizing the model compounds mesoporous silica. Additionally, through the presence of HPMCAS-L, the precipitation risk in supersaturable aqueous environment was significantly reduced. Finally, this manuscript provides fundamental, computational and experimental molecular-properties based formulation guidance tree to a priori gauge the (1) possibility of generating solid-state stable amorphous formulations and (2) sustaining in vitro supersaturation in extreme non-sink dissolution conditions. This unique and conceptual formulation guidance tree is believed to be extremely beneficial to drug discovery formulators to triage NCEs and streamline solubility-enabling formulation efforts.

Mapping Natural Populus euphratica Forests in the Mainstream of the Tarim River Using Spaceborne Imagery and Google Earth Engine

Populus euphratica is a unique constructive tree species within riparian desert areas that is essential for maintaining oasis ecosystem stability. The Tarim River Basin contains the most densely distributed population of P. euphratica forests in the world, and obtaining accurate distribution data in the mainstream of the Tarim River would provide important support for its protection and restoration. We propose a new method for automatically extracting P. euphratica using Sentinel-1 and 2 and Landsat-8 images based on the Google Earth Engine cloud platform and the random forest algorithm. A mask of the potential distribution area of P. euphratica was created based on prior knowledge to save computational resources. The NDVI (Normalized Difference Vegetation Index) time series was then reconstructed using the preferred filtering method to obtain phenological parameter features, and the random forest model was input by combining the phenological parameter, spectral index, textural, and backscattering features. An active learning method was employed to optimize the model and obtain the best model for extracting P. euphratica. Finally, the map of natural P. euphratica forests with a resolution of 10 m in the mainstream of the Tarim River was obtained. The overall accuracy, producer’s accuracy, user’s accuracy, kappa coefficient, and F1-score of the map were 0.96, 0.98, 0.95, 0.93, and 0.96, respectively. The comparison experiments showed that simultaneously adding backscattering and textural features improved the P. euphratica extraction accuracy, while textural features alone resulted in a poor extraction effect. The method developed in this study fully considered the prior and posteriori information and determined the feature set suitable for the P. euphratica identification task, which can be used to quickly obtain accurate large-area distribution data of P. euphratica. The method can also provide a reference for identifying other typical desert vegetation.

Novel AI-driven Malaria Prediction for Optimizing Public Health Management

Malaria is an unmoving real common well-being apprehension in Asian nations like the Republic of India, where the state characterizes approximately 55% of the session events cutting-edge the infirmaries. They are usually distinguished by the absence of appropriate therapeutic care provision and the often late and error-prone diagnosis of the condition. In particular, commonly used devices such as the Rapid Diagnosis Test (RDT) are not completely dependable. They are primarily notable for their failure to provide adequate medical treatment and their tendency to diagnose the disease late and incorrectly. For example, widely used devices like the RDT are not consistent. To improve public health management actions, this work offers a unique augmented tree with penguin search optimization (AT+PSO) methodology for malaria forecasting. The suggested method combines the PSO algorithm with the augmented tree model, also known as random forest (RF). In the preprocessing stage, raw data samples are subjected to data normalization. Then, we applied the PSO to improve the characteristics of the RF model after successfully predicting malaria with the RF. The Python program is used to implement the suggested technique and analyze performance using a range of measures, including accuracy (0.988), sensitivity (0.987), specificity (0.991), F1-score (0.988), and MCC (0.975). In summary, our suggested approach produced the best results in terms of accuracy as opposed to other current strategies for predicting malaria to improve governance of public health.

Rocky outcrops form islands of high and unique tree biodiversity within an ocean of grass in Serengeti National Park

AbstractAfrican savannahs are characterised by a high plant diversity, partly resulting from a high turnover in community compositions across space. However, it is poorly understood what is driving this spatial turnover in plant communities. Here, we investigate to which extent the presence of rocky outcrops (also called kopjes) explains the community composition of trees in an African savannah, and how we can understand the responses of tree species to rocky outcrops by their functional traits. Along a precipitation gradient, we visited 24 sites in Serengeti National Park (Tanzania). At each site, we characterised tree communities, as well as their functional traits, in both a kopje and an adjacent open savannah plot (matrix plot). We found that kopjes harboured elevated tree abundances and species richness. Their dominant trees were more often evergreen, had a higher specific leaf area, a lower leaf nitrogen content and a lower spine density, than dominant trees in the savannah matrix. Differences in tree communities between kopjes and savannah matrix plots were generally the largest at sites with low precipitation. Our results indicate that kopjes are strong drivers of tree biodiversity, possibly due to locally increased soil moisture and low fire frequencies. The uniqueness of kopje tree communities may have important implications for higher trophic levels and ecosystem functioning.

Establishment of Novel Simple Sequence Repeat (SSR) Markers from Chimonanthus praecox Transcriptome Data and Their Application in the Identification of Varieties.

Chimonanthus praecox, a member of the Calycanthaceae family, is a unique, traditional, and famous flowering economic tree species in China. Despite the existence of several varieties, only a few cultivars have been formally named. Currently, expression sequence tag-simple sequence repeat (EST-SSR) markers are extensively used to identify different species and varieties; a large number of microsatellites can be identified from transcriptome databases. A total of 162,638 unigenes were assembled using RNA-seq; 82,778 unigenes were annotated using the Nr, Nt, Swiss-Prot, Pfam, GO, KOG, and KEGG databases. In total, 13,556 SSR loci were detected from 11,691 unigenes, with trinucleotide repeat motifs being the most abundant among the six repeat motifs. To develop the markers, 64,440 pairs of SSR primers with polymorphism potential were designed, and 75 pairs of primers were randomly selected for amplification. Among these markers, seven pairs produced amplified fragments of the expected size with high polymorphism. Using these markers, 12 C. praecox varieties were clustered into two monophyletic clades. Microsatellites in the transcriptome of C. praecox exhibit rich types, strong specificity, and great polymorphism potential. These EST-SSR markers serve as molecular technical methods for identifying different varieties of C. praecox and facilitate the exploration of a large number of candidate genes associated with important traits.

Two Carya Species, Carya hunanensis and Carya illinoinensis, Used as Rootstocks Point to Improvements in the Heat Resistance of Carya cathayensis.

Grafting as a crucial horticultural technique has been widely used in the cultivation of Carya cathayensis (Chinese hickory), which is a unique and important economic tree in the northeast of Zhejiang Province and the south of Anhui Province. However, the existing literature lacks research on the potential impact of various rootstocks on the thermal tolerance of Chinese hickory. The objectives of this study were to evaluate heat tolerance in four distinct groups of Chinese hickory, including C. cathayensis grafted onto Carya hunanensis and Carya illinoinensis, one self-grafted group (C. cathayensis grafted onto C. cathayensis), and one non-grafted group (C. cathayensis). We examined photosynthesis parameters, phytohormones, and differentially expressed genes in the four various hickory groups subjected to 25 °C, 35 °C, and 40 °C heat stress (HS). The results demonstrated that grafting onto C. hunanensis and C. illinoinensis exhibited a higher net photosynthetic rate and stomatal conductance, lower intercellular CO2 concentration, and smaller changes in plant hormone content compared to self-grafted and non-grafted group under HS. The transcriptome results revealed that the majority of differentially expressed genes (DEGs) associated with photosynthetic pathways exhibited downregulation under HS, while the degree of variation in grafted groups using C. hunanensis and C. illinoinensis as rootstocks was comparatively lower than that observed in self-grafted and non-grafted groups. The alteration in the expression patterns of DEGs involved in plant hormone synthesis and metabolism under HS corresponded to changes in plant hormone contents. Overall, Chinese hickory grafted onto C. hunanensis and C. illinoinensis exhibited enhanced resistance to high-temperature stress at the juvenile stage.

Estimation of Picea Schrenkiana Canopy Density at Sub-Compartment Scale by Integration of Optical and Radar Satellite Images

This study proposes a novel approach to estimate canopy density in Picea Schrenkiana var. Tianschanica forest sub-compartments by integrating optical and radar satellite data. This effort is aimed at enhancing methodologies for forest resource surveys and monitoring, particularly vital for the sustainable development of semi-arid mountainous areas with fragile ecological environments. The study area is the West Tianshan Mountain Nature Reserve in Xinjiang, which is characterized by its unique dominant tree species, Picea Schrenkiana. A total of 411 characteristic factors were extracted from Gaofen-2 (GF-2) sub-meter optical satellite imagery, Gaofen-3 (GF-3) multi-polarization synthetic aperture radar satellite imagery, and digital elevation model (DEM) data. Consequently, 17 characteristic parameters were selected based on their correlation with canopy density data to construct an estimation model. Three distinct models were developed, including a multiple stepwise regression model (a linear approach), a Back Propagation (BP) neural network model (a neural network-based method), and a Cubist model (a decision tree-based technique). The results indicate that combining optical and radar image characteristics significantly enhances accuracy, with an Average Absolute Percentage Precision (AAPP) value improvement in estimation accuracy from 76.50% (with optical image) and 78.50% (with radar image) to 78.66% (with both). Of the three models, the BP neural network model achieved the highest overall accuracy (79.19%). At the sub-component scale, the BP neural network model demonstrated superior accuracy in low canopy density estimation (75.37%), whereas the Cubist model, leveraging radar image characteristics, excelled in medium density estimations (87.46%). Notably, the integrated Cubist model combining optical and radar data achieved the highest accuracy for high canopy density estimation (89.17%). This study highlights the effectiveness of integrating optical and radar data for precise canopy density assessment, contributing significantly to ecological resource monitoring methodologies and environmental assessments.

Koala density, habitat, conservation, and response to logging in eucalyptus forest; a review and critical evaluation of call monitoring

ABSTRACT This study is the second in a series that examines the habitat requirements and response to logging of koalas (Phascolarctos cinereus) inhabiting tall eucalypt forests of north-east NSW. It presents the findings of koala population and habitat monitoring surveys in Pine Creek State Forest and Bongil Bongil National Park using a combination of call-counting and direct observation (spotlighting). The 6400 ha study area was mapped into 6 zones of increasing koala habitat quality by ground survey of forest structure and floristics on a 200 m grid. The accuracy of habitat definition and mapping was tested by stratified transect counts of koala calls and sightings over two consecutive years (1997–98). Average koala density increased steeply and significantly, from 0.02 – 0.20 koalas/hectare, with increasing mapped habitat quality based on increasing forest age, structural complexity, local food tree species diversity, history of prior koala occurrence and decreased past logging intensity. This relationship was driven primarily by breeding females, with the number of male koala calls weakly or uncorrelated with koala sightings and mapped habitat quality. Male koalas were more widely and uniformly distributed than females, including areas of low quality, plantation, and intensively logged forest. This finding explains the discrepancy between our results and those of other recent studies which concluded that koalas are tolerant of intensive logging based on modelling of calling male koalas and reliance on an untested assumption that male calling is indicative of female breeding success. Koala density in a subset of the highest quality habitat was relatively stable at 0.28 koalas/ha (3 hectares/koala) over the long term (1997–98 and 2012–2023). Key characteristics of the forest koala population, including low stable density, large home ranges, preference for high food tree diversity and locally unique food trees (including Allocasuarina torulosa and Syncarpia glomulifera), are not adequately explained by existing koala habitat models. We present a new paradigm to explain regional variation in koala distribution, habitat and foraging preferences based on variations in foliage chemistry (toxicity and nutritional value) determined by the duration and stability of local plant-koala interactions in response to past fire, hunting, predation and logging disturbance history. We hypothesize that koala density in stable forest populations is regulated at low levels by a combination of selected and induced increases in leaf toxicity and decreases in leaf nutrition that limit koala browsing to benign levels of about 1-2% of annual leaf production. Large home ranges, complex mature forest structure, high food tree diversity and a specialized or diverse gut microbiome may be essential to allow females to rotate and change food trees frequently to minimize induced toxicity and select individual leaves with sufficient nutrients to support breeding and lactation with minimal risk of predation. High density koala populations (> 0.6/ha) occur primarily in areas where koalas have been introduced or re-introduced to planted habitats and natural areas where aboriginal hunters and dingoes were historically present but are now absent, and where food trees have not been selected for resistance to koala browsing pressure.

Systematic review and meta-analysis of Murray's law in the coronary arterial circulation.

Murray's law has been viewed as a fundamental law of physiology. Relating blood flow ([Formula: see text]) to vessel diameter (D) ([Formula: see text]·∝·D3), it dictates minimum lumen area (MLA) targets for coronary bifurcation percutaneous coronary intervention (PCI). The cubic exponent (3.0), however, has long been disputed, with alternative theoretical derivations, arguing this should be closer to 2.33 (7/3). The aim of this meta-analysis was to quantify the optimum flow-diameter exponent in human and mammalian coronary arteries. We conducted a systematic review and meta-analysis of all articles quantifying an optimum flow-diameter exponent for mammalian coronary arteries within the Cochrane library, PubMed Medline, Scopus, and Embase databases on 20 March 2023. A random-effects meta-analysis was used to determine a pooled flow-diameter exponent. Risk of bias was assessed with the National Institutes of Health (NIH) quality assessment tool, funnel plots, and Egger regression. From a total of 4,772 articles, 18 were suitable for meta-analysis. Studies included data from 1,070 unique coronary trees, taken from 372 humans and 112 animals. The pooled flow diameter exponent across both epicardial and transmural arteries was 2.39 (95% confidence interval: 2.24-2.54; I2 = 99%). The pooled exponent of 2.39 showed very close agreement with the theoretical exponent of 2.33 (7/3) reported by Kassab and colleagues. This exponent may provide a more accurate description of coronary morphometric scaling in human and mammalian coronary arteries, as compared with Murray's original law. This has important implications for the assessment, diagnosis, and interventional treatment of coronary artery disease.

Establishment of an Efficient Protoplast Isolation and Transfection Method for Eucommia ulmoides Oliver.

Eucommia ulmoides Oliver is a unique high-quality natural rubber tree species and rare medicinal tree species in China. The rapid characterization of E. ulmoides gene function has been severely hampered by the limitations of genetic transformation methods and breeding cycles. The polyethylene glycol (PEG)-mediated protoplast transformation system is a multifunctional and rapid tool for the analysis of functional genes in vivo, but it has not been established in E. ulmoides. In this study, a large number of highly active protoplasts were isolated from the stems of E. ulmoides seedlings by enzymatic digestion, and green fluorescent protein expression was facilitated using a PEG-mediated method. Optimal enzymatic digestion occurred when the enzyme was digested for 10 h in an enzymatic solution containing 2.5% Cellulase R-10 (w/v), 0.6% Macerozyme R-10 (w/v), 2.5% pectinase (w/v), 0.5% hemicellulase (w/v), and 0.6 mol/L mannitol. The active protoplast yield under this condition was 1.13 × 106 protoplasts/g fresh weight, and the protoplast activity was as high as 94.84%. This study established the first protoplasm isolation and transient transformation system in hard rubber wood, which lays the foundation for subsequent functional studies of E. ulmoides genes to achieve high-throughput analysis, and provides a reference for future gene function studies of medicinal and woody plants.

Variational Bayesian phylogenies through matrix representation of tree space.

In this article, we study the distance matrix as a representation of a phylogeny by way of hierarchical clustering. By defining a multivariate normal distribution on (a subset of) the entries in a matrix, this allows us to represent a distribution over rooted time trees. Here, we demonstrate tree distributions can be represented accurately this way for a number of published tree distributions. Though such a representation does not map to unique trees, restriction to a subspace, in particular one we call a "cube", makes the representation bijective at the cost of not being able to represent all possible trees. We introduce an algorithm "cubeVB" specifically for cubes and show through well calibrated simulation study that it is possible to recover parameters of interest like tree height and length. Although a cube cannot represent all of tree space, it is a great improvement over a single summary tree, and it opens up exciting new opportunities for scaling up Bayesian phylogenetic inference. We also demonstrate how to use a matrix representation of a tree distribution to get better summary trees than commonly used maximum clade credibility trees. An open source implementation of the cubeVB algorithm is available from https://github.com/rbouckaert/cubevb as the cubevb package for BEAST 2.

Health Benefits and uses of Neem Leaves

Neem is a very unique tree and neem leaves are the most complex leaves on the planet. The neem tree has over 130 different biologically active compounds, and the neem leaf is one of the most complex leaves you can find on the planet. According to the ancient science of Ayurveda texts describe how neem treats skin disorders, hair problems, enhances appetite, boosts digestion, kindles the fire in the belly, improves breathing, helps manage diabetic conditions, aids healing of wounds and relieves nausea. Neem has been declared the “tree of the 21st century” by the United Nations. The US National academy of science also recognized neem’s medicinal value in its 1992 report entitled “Neem: A tree for solving global problems.” Keywords: Biologically active, Azadirachta indica, Diabetes, Hypertension, Metabolic syndrome, Neem.

Widely Targeted Metabolomics and Network Pharmacology Reveal the Nutritional Potential of Yellowhorn (Xanthoceras sorbifolium Bunge) Leaves and Flowers.

Yellowhorn (Xanthoceras sorbifolium Bunge) is a unique oilseed tree in China with high edible and medicinal value. However, the application potential of yellowhorn has not been adequately explored. In this study, widely targeted metabolomics (HPLC-MS/MS and GC-MS) and network pharmacology were applied to investigate the nutritional potential of yellowhorn leaves and flowers. The widely targeted metabolomics results suggested that the yellowhorn leaf contains 948 non-volatile metabolites and 638 volatile metabolites, while the yellowhorn flower contains 976 and 636, respectively. A non-volatile metabolite analysis revealed that yellowhorn leaves and flowers contain a variety of functional components beneficial to the human body, such as terpenoids, flavonoids, alkaloids, lignans and coumarins, phenolic acids, amino acids, and nucleotides. An analysis of volatile metabolites indicated that the combined action of various volatile compounds, such as 2-furanmethanol, β-icon, and 2-methyl-3-furanthiol, provides the special flavor of yellowhorn leaves and flowers. A network pharmacology analysis showed that various components in the flowers and leaves of yellowhorn have a wide range of biological activities. This study deepens our understanding of the non-volatile and volatile metabolites in yellowhorn and provides a theoretical basis and data support for the whole resource application of yellowhorn.

Tree abundance, species richness, or species mix? Exploring the relationship between features of urban street trees and pedestrian volume in Jinan, China

Accumulating evidence has confirmed that urban greenery, especially street trees, is beneficial to walking behaviors. Existing street greenery exposure-walking behavior studies have focused on the quantity of greenery, which is often measured by normalized difference vegetation index (NDVI) or green view index (GVI). However, some important qualities of street trees, the dominant component of urban greenery, were often overlooked, due to the labor-intensive and expensive nature of on-site surveys. To address this issue, we proposed a cutting-edge deep learning technique to identify street tree species at the individual tree level. We established a citywide dataset of all street trees with species information via 185,831 Baidu Street View images (BSV) in Jinan, China. Population-level walking intensity, measured by pedestrian volume, was retrieved from BSV images using Baidu AI. We further adopted spatial regression models to investigate the association between pedestrian volume and street tree characteristics, including street tree abundance (number of street trees), species richness (number of unique tree species) and species mix (the degree of diversity of tree species). The built environment and urban greenery covariates were adjusted in the models. The results indicate that the street tree abundance and species mix are positively associated with pedestrian volume. Species richness is not associated with it. Besides, spatial mismatch is identified between abundance and species mix of street trees in the study area. Hence, to facilitate walking behavior and deliver related health benefits, it is necessary to develop fine-grained measures of street greenery features.

Fusion of Dense Airborne LiDAR and Multispectral Sentinel-2 and Pleiades Satellite Imagery for Mapping Riparian Forest Species Biodiversity at Tree Level.

Multispectral and 3D LiDAR remote sensing data sources are valuable tools for characterizing the 3D vegetation structure and thus understanding the relationship between forest structure, biodiversity, and microclimate. This study focuses on mapping riparian forest species in the canopy strata using a fusion of Airborne LiDAR data and multispectral multi-source and multi-resolution satellite imagery: Sentinel-2 and Pleiades at tree level. The idea is to assess the contribution of each data source in the tree species classification at the considered level. The data fusion was processed at the feature level and the decision level. At the feature level, LiDAR 2D attributes were derived and combined with multispectral imagery vegetation indices. At the decision level, LiDAR data were used for 3D tree crown delimitation, providing unique trees or groups of trees. The segmented tree crowns were used as a support for an object-based species classification at tree level. Data augmentation techniques were used to improve the training process, and classification was carried out with a random forest classifier. The workflow was entirely automated using a Python script, which allowed the assessment of four different fusion configurations. The best results were obtained by the fusion of Sentinel-2 time series and LiDAR data with a kappa of 0.66, thanks to red edge-based indices that better discriminate vegetation species and the temporal resolution of Sentinel-2 images that allows monitoring the phenological stages, helping to discriminate the species.

Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies.

A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree structure and activation function. Effective initialization of its model parameters is crucial for its learning performance. This work proposes a novel initialization method specifically designed to improve the performance of DNM in classifying high-dimensional data, notable for its simplicity, speed, and straightforward implementation. Extensive experiments on benchmark datasets show that the proposed method outperforms traditional and recent initialization methods, particularly in datasets consisting of high-dimensional data. In addition, valuable insights into the behavior of DNM during training and the impact of initialization on its learning performance are provided. This research contributes to the understanding of the initialization problem in deep learning and provides insights into the development of more effective initialization methods for other types of neural network models. The proposed initialization method can serve as a reference for future research on initialization techniques in deep learning.

Phenological growth stages of Xanthoceras sorbifolium Bunge: Codification and description according to the BBCH scale

Xanthoceras sorbifolium Bunge is a kind of unique oil tree species found in northern China that has high ecological, ornamental, and economic value. Its seed oil is not only a high-quality edible oil well supplied with unsaturated fatty acids but also a superior raw material for biodiesel production. In the present study, the different phenological growth stages of X. sorbifolium were characterized according to the Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale. Eight principal growth stages were described for the bud, leaf and shoot development; inflorescence emergence and flowering; fruit development and maturity, together with senescence and beginning of dormancy. Meanwhile forty-two secondary growth stages were described in detail. Illustrative pictures with a codification were provided to better define and describe these phenological growth stages. Phenological records from two growing seasons were used to create a schematic representation of the principal growth stages. This study provides a beneficial reference for general orchard management, scientific research, and cultivation of new cultivars.