Complex Morphology Research Articles

Optical modeling of atmospheric black carbon aerosol ensembles with complex particle morphology

Black carbon (BC) aerosol is one of the most important factor in global warming. BC radiative forcing remains unconstrained, mainly because of the uncertain parameterizations of its absorption and scattering properties in the atmosphere. The single sphere model is widely used in current climate assessment of BC aerosols due to its computational convenience, however, their complex morphologies in particle level are excessively simplified which leads to computed inaccuracy. In this study, we present a dynamic model for optical calculations of BC aerosol ensembles considering their complex fractal aggregate morphologies with the constraint of max monomer numbers (N s, max) and radius (a max). We show that the simulation accuracy of the dynamic model with suitable values of N s, max and a max may achieve ∼95% while the computation time may reduce to ∼6%. We find that optical properties of BC aerosol ensembles can be simulated for higher accuracy or faster calculation by performing different selections of monomer numbers and radius in their size distributions. This method enables extensive and accurate optical calculations of BC particles with complex morphologies, which would be useful for the remote sensing inversion and the assessment of climate.

Linguistic correlates of societal variation: A quantitative analysis.

Traditionally, many researchers have supported a uniformitarian view whereby all languages are of roughly equal complexity, facilitated by internal trade-offs between complexity at different levels, such as morphology and syntax. The extent to which the speakers' societies influence the trade-offs has not been well studied. In this paper, we focus on morphology and syntax, and report significant correlations between specific linguistic and societal features, in particular those relating to exoteric (open) vs. esoteric (close-knit) society types, characterizable in terms of population size, mobility, communication across distances, etc. We conduct an exhaustive quantitative analysis drawing upon WALS, D-Place, Ethnologue and Glottolog, finding some support for our hypothesis that languages spoken by exoteric societies tend towards more complex syntaxes, while languages spoken by esoteric societies tend towards more complex morphologies.

The expression of texture in P’urhepecha

Alongside taste and smell, touch has long been considered one of the ‘lower’ senses in much of western thought (Classen 1997). However, more recent research indicates that this ranking is not absolute, but that the cultural importance of the senses and their role in constructing worldview is relative, and thus variable (San Roque et al. 2015). Winter (2019: 191) also remarks that touch is high in semantic complexity because it is a frequent source domain in cross-modal language use, for instance, rough voice. Nevertheless, the language of touch has largely been ignored in linguistic description (cf. Essegbey 2013). This paper specifically investigates the language of texture in P’urhepecha, an isolate spoken in western Mexico, focusing on terms obtained by employing the “texture booklet” (Majid et al. 2007). Responses revealed two main morphosyntactic strategies: (i) terms formed from a root that expresses a texture, further subdivided into adjectival and verbal forms, and (ii) a variety of comparison constructions that can be broadly translated by ‘like’ in English. Ten roots were employed overall, but three of them dominated: ch’era- ‘rough’, sanu- (and its variant sunu-) ‘woolly’, and pitsï- (and its variant pichi-) ‘smooth’. These describe all ten of the stimulus materials, whereas the minor, less frequent roots, had narrower reference. While further investigation is needed to gain a better understanding of this lexical domain, our preliminary study of texture terms in P’urhepecha adds to the very few sources that have investigated this area of linguistic inquiry, and also deepens our knowledge of the complex morphology and contact-induced features of the language (cf. Chamoreau 2012).

Complex left appendage morphology is associated with Embolic Stroke of Undetermined Source.

Variations in the left atrial appendage (LAA) morphology are associated with different embolic risk in patients with atrial fibrillation (AF). Data are scarce regarding the association between LAA morphology and Embolic stroke of undetermined source (ESUS). Using cardiac computed tomography (CCT) scans, LAA morphology was categorized as either chicken wing (CW), cactus, windsock, or cauliflower. Furthermore, we examined the presence of large secondary lobes arising from the main lobe, considering their existence as indicative of a complex LAA morphology. LAA morphologies were compared between ESUS (n = 134) and AF patients (n = 120); and between ESUS patients with (n = 24) and without (n = 110) subsequent AF diagnosis during long-term follow-up. ESUS patients had a significantly higher prevalence of cauliflower morphology compared to AF group (52% vs 34%, respectively, p = 0.01); however, no significant difference was found between the groups when categorizing LAA morphology to either CW or non-CW. ESUS patients had significantly higher prevalence of large secondary lobes compared with AF patients (50% vs 29%, respectively, p = 0.001). When comparing ESUS patients with and without AF diagnosis during follow-up (20-48 months of follow-up, median 31 months), there were no significant differences in the prevalence of the "classical" morphologies, but large secondary lobes were significantly more prevalent among those without subsequent AF diagnosis. ESUS patients have a high prevalence of complex LAA morphology, which might be associated with an increased risk for thrombus formation even in the absence of AF.

Root and canal morphology of the maxillary first molar: A micro-computed tomography-focused observation of literature with illustrative cases. Part 2: Internal root morphology

The complexity of root canal morphology can influence cleaning and shaping of the root canal system and, ultimately, treatment outcomes. Undiscovered root canal morphology can greatly reduce the prognosis of endodontic treatment due to the preservation of offending organisms and their by products. The maxillary first molar has been identified as one of the most challenging teeth to treat endodontically due to its complex internal anatomy, the presence of additional and accessory canals and intricate root canal configurations. This paper is the second of two that provide a micro computed tomography (micro-CT)-focused overview of available literature on various aspects of the root and canal morphology of the maxillary first permanent molar. The aim of this paper is to provide an overview of relevant aspects of the internal root morphology in different populations. The content is supported by illustrative micro-CT images and clinical cases or images.

Planetary Nebula NGC 2818: Revealing its complex 3D morphology

ABSTRACT We carry out an advanced morpho-kinematic analysis of the Planetary Nebula (PN) NGC 2818, whose complex morphology is described by a basic bipolar component, filamentary structures and a knotty central region. We performed an upgrated 3D Morpho-kinematic (MK) model by employing the shape software, combining for the first time in PNe optical 2D spatially resolved echelle spectra and Fabry–Perot data cubes. The best-fitting 3D model of NGC 2818 successfully reconstructs the main morphology, considering one bipolar component, radial filamentary structures, and an equatorial component as the geometrical locus of the group of cometary knots. The model shows that the equatorial component has the lower expansion velocity of the system at 70 ± 20 km s−1. The velocity of the bipolar component is 120 ± 20 km s−1, while all the filamentary structures were found to expand at higher velocities of 180 ± 20 km s−1. Moreover, Fabry–Perot data revealed for the first time a north-eastern filament expanding at a mean velocity of 80 ± 20 km s−1, while its equivalent counterpart in the south-western region was confirmed by a new detected substructure in the echelle data. A new detected knotty structure at velocity −40 ± 20 km s−1 is also reported, as expelled material from the fragmented eastern lobe of the nebula. We interpret the overall structure of NGC 2818 as the result of the evolution of a binary system that underwent the common envelope phase, in conjunction with the ejections of a magnetized jet, misaligned with respect to the symmetry axis of the bipolar/elliptical shell.

Propagation and complex morphology of hydraulic fractures in lamellar shales based on finite-discrete element modeling

We explore the controls of stress magnitude and orientation relative to bedding on the resulting morphology/topology of hydraulic fractures using a combined finite-discrete element method (FDEM). Behavior is shown conditioned by the ratio of principal stresses λ=σ3/σ1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda ={\\sigma }_{3}/{\\sigma }_{1}$$\\end{document} and relative inclination of the bedding. When the lateral pressure coefficient (λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document}) is less than 0.67, hydraulic fractures predominantly initiate as tensile fractures along the wellbore, aligning with the maximum principal stress direction. Conversely, for λ≥0.67\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda \\ge 0.67$$\\end{document}, shear cracks are favored to initiate for the minor stress difference, leading to a less predictable initiation and extension direction. Simultaneously, diminished stress differences correspond to elevated reservoir breakdown pressures, displaying a linear correlation with lateral pressure coefficients and little influenced by equivalent bedding orientation. Bedding plane orientation significantly impacts the mode and morphology of hydraulic fracture propagation. Bedding parallel to the direction of the minimum principal stress (σ3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\sigma }_{3}$$\\end{document}) favors layer-penetrating and bifurcated fractures, whereas inclined bedding facilitates the emergence of numerous steering-type and capture-type fractures. Especially at steeper inclinations (β=60∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta =60^\\circ$$\\end{document}), hydraulic fractures readily extend along the bedding surface, inducing macroscopic shear slip failure. Under high-stress disparities, the breakdown pressure exhibits greater sensitivity to bedding inclination, and its influence pattern aligns with the variations in tensile strength, typically reaching maximum and minimum values at bedding inclination angles of 0° and 60°, respectively.

Adaptive Feature Medical Segmentation Network: an adaptable deep learning paradigm for high-performance 3D brain lesion segmentation in medical imaging.

In neurological diagnostics, accurate detection and segmentation of brain lesions is crucial. Identifying these lesions is challenging due to its complex morphology, especially when using traditional methods. Conventional methods are either computationally demanding with a marginal impact/enhancement or sacrifice fine details for computational efficiency. Therefore, balancing performance and precision in compute-intensive medical imaging remains a hot research topic. We introduce a novel encoder-decoder network architecture named the Adaptive Feature Medical Segmentation Network (AFMS-Net) with two encoder variants: the Single Adaptive Encoder Block (SAEB) and the Dual Adaptive Encoder Block (DAEB). A squeeze-and-excite mechanism is employed in SAEB to identify significant data while disregarding peripheral details. This approach is best suited for scenarios requiring quick and efficient segmentation, with an emphasis on identifying key lesion areas. In contrast, the DAEB utilizes an advanced channel spatial attention strategy for fine-grained delineation and multiple-class classifications. Additionally, both architectures incorporate a Segmentation Path (SegPath) module between the encoder and decoder, refining segmentation, enhancing feature extraction, and improving model performance and stability. AFMS-Net demonstrates exceptional performance across several notable datasets, including BRATs 2021, ATLAS 2021, and ISLES 2022. Its design aims to construct a lightweight architecture capable of handling complex segmentation challenges with high precision. The proposed AFMS-Net addresses the critical balance issue between performance and computational efficiency in the segmentation of brain lesions. By introducing two tailored encoder variants, the network adapts to varying requirements of speed and feature. This approach not only advances the state-of-the-art in lesion segmentation but also provides a scalable framework for future research in medical image processing.

PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation

In Cone Beam Computed Tomography (CBCT) images, accurate tooth segmentation is crucial for oral health, providing essential guidance for dental procedures such as implant placement and difficult tooth extractions (impactions). However, due to the lack of a substantial amount of dental data and the complexity of tooth morphology in CBCT images, the task of tooth segmentation faces significant challenges. This may lead to issues such as overfitting and training instability in existing algorithms, resulting in poor model generalization. Ultimately, this may impact the accuracy of segmentation results and could even provide incorrect diagnostic and treatment information. In response to these challenges, we introduce PPA-SAM, an innovative dual-encoder segmentation network that merges the currently popular Segment Anything Model (SAM) with the 3D medical segmentation network, VNet. Through the use of adapters, we achieve parameter reuse and fine-tuning, enhancing the model’s adaptability to specific CBCT datasets. Simultaneously, we utilize a three-layer convolutional network as both a discriminator and a generator for adversarial training. The PPA-SAM model seamlessly integrates the high-precision segmentation performance of convolutional networks with the outstanding generalization capabilities of SAM models, achieving more accurate and robust three-dimensional tooth segmentation in CBCT images. Evaluation of a small CBCT dataset demonstrates that PPA-SAM outperforms other networks in terms of accuracy and robustness, providing a reliable and efficient solution for three-dimensional tooth segmentation in CBCT images. This research has a positive impact on the management of dentofacial conditions from oral implantology to orthognathic surgery, offering dependable technological support for future oral diagnostics and treatment planning.

Early synapsids neurosensory diversity revealed by CT and synchrotron scanning.

Non-mammaliaform synapsids (NMS) represent the closest relatives of today's mammals among the early amniotes. Exploring their brain and nervous system is key to understanding how mammals evolved. Here, using CT and Synchrotron scanning, we document for the first time three extreme cases of neurosensory and behavioral adaptations that probe into the wide range of unexpected NMS paleoneurological diversity. First, we describe adaptations to low-frequency hearing and low-light conditions in the non-mammalian cynodont Cistecynodon parvus, supporting adaptations to an obligatory fossorial lifestyle. Second, we describe the uniquely complex and three-dimensional maxillary canal morphology of the biarmosuchian Pachydectes elsi, which suggests that it may have used its cranial bosses for display or low-energy combat. Finally, we introduce a paleopathology found in the skull of Moschognathus whaitsi. Since the specimen was not fully grown, this condition suggests the possibility that this species might have engaged in playful fighting as juveniles-a behavior that is both social and structured. Additionally, this paper discusses other evidence that could indicate that tapinocephalid dinocephalians were social animals, living and interacting closely with one another. Altogether, these examples evidence the wide range of diversity of neurological structures and complex behavior in NMS.

Analysis of multi-scale failure behavior of SiCf/SiC composite clad tube under flexible clamping damage

The intricate spatial fiber winding structure of SiCf/SiC ceramic matrix composite (CMC) cladding tubes makes it difficult to investigate in depth the inherent evolutionary mechanism of damage failure under specific operating conditions. Therefore, in this study, based on X-ray computed tomography (X-CT) reconstruction and cross scale numerical simulation methods, combined with clamping failure experiments under specific conditions for the cladding tubes, we conduct an in-depth analysis of the material's damage, failure, and mechanical behavior in different clamped regions. The research indicates that variations in the clamped regions have a minimal impact on the overall mechanical feedback of the material, and the maximum clamping load remains essentially around 500 N. The tube damage exhibits a unique " regional progressive damage evolution mechanism," where stress differentials of 108.49–115.33% appear between the inner and outer surfaces in different regions, leading to variations in the initiation and propagation of cracks within these areas. Furthermore, based on the minimum potential energy principle and semi-geodesic line theory, the intricate macroscopic tube structure formed by the spatial stacking and winding of SiCf/SiC cladding tube fibers is accurately reconstructed, which is combined with macroscale and microscale local feature models to capture the fibre/matrix morphology of the material with dynamic mechanical feedbacks, and experimentally validated in this work. The results indicate that the numerical model can effectively characterize the complex mechanical damage morphology and progressive failure lows of SiCf/SiC CMC at multiscale. Simultaneously, it is observed that stress concentrations in the fiber crossover region result in a load-bearing capacity 112–283% higher than in other areas. This phenomenon often leads to the first occurrence of failure in this region after clamping overload, exhibiting more pronounced damage characteristics.

Graph Representation Learning for Large-Scale Neuronal Morphological Analysis.

The analysis of neuronal morphological data is essential to investigate the neuronal properties and brain mechanisms. The complex morphologies, absence of annotations, and sheer volume of these data pose significant challenges in neuronal morphological analysis, such as identifying neuron types and large-scale neuron retrieval, all of which require accurate measuring and efficient matching algorithms. Recently, many studies have been conducted to describe neuronal morphologies quantitatively using predefined measurements. However, hand-crafted features are usually inadequate for distinguishing fine-grained differences among massive neurons. In this article, we propose a novel morphology-aware contrastive graph neural network (MACGNN) for unsupervised neuronal morphological representation learning. To improve the retrieval efficiency in large-scale neuronal morphological datasets, we further propose Hash-MACGNN by introducing an improved deep hash algorithm to train the network end-to-end to learn binary hash representations of neurons. We conduct extensive experiments on the largest dataset, NeuroMorpho, which contains more than 100 000 neurons. The experimental results demonstrate the effectiveness and superiority of our MACGNN and Hash-MACGNN for large-scale neuronal morphological analysis.

Preparation of transparent AlON ceramics with controlled shapes by a novel spontaneous coagulation casting and pressureless sintering method

Aluminum oxynitride (AlON) exhibits excellent mechanical properties and good optical transparency in the ultraviolet to near-infrared range and can be used in military and civilian fields. However, the troublesome molding of AlON, especially for complex shapes, limits its promotion and application. Therefore, transparent AlON ceramics with complex morphologies were prepared for the first time by a novel spontaneous coagulation casting (SCC) and pressureless sintering method. In this study, we report a simple carbothermal reduction and nitridation (CRN) method for synthesizing high-purity AlON powder, which is especially suitable for wet casting without anti-hydration treatment. The dispersion and gelation behavior of AlON slurries by isobutene maleic anhydride copolymer (Isobam104) and tetramethylammonium hydroxide (TMAH) were extensively investigated. With the addition of 0.9 wt% Isobam104 and 15 mL/L TMAH, the AlON slurries can achieve a solids loading of up to 45 vol% with the lowest viscosity (460.4 mPa·s-10 r/min) and the AlON green body is free of cracks and deformation during drying and demolding processes. Finally, the AlON green bodies with square and polygonal irregular morphologies were fabricated via a simple SCC method. High transparent AlON ceramics of a maximum transmittance up to 77.3% at 2000 nm and 72.2% at 632 nm were obtained by pressureless sintered at 2000 °C for 6 h. The SCC method has great potential in preparing ceramics with controllable shapes.

Three-Dimensional Imaging of the Patellofemoral Joint Improves Understanding of Trochlear Anatomy and Pathology and Planning of Realignment

Three-dimensional (3D) modeling using digital or printed models provides a unique perspective that caters to cognitive spatial ability in a way that can add to our understanding and mental representations of human anatomy. This is particularly useful in the setting of trochlear dysplasia, where the morphology of the groove can exhibit substantial variability and complexity. Using 3D reformatted images and models, a pragmatic understanding of how morphology influences patellofemoral pathology can be gleaned. Further, this perspective facilitates cognition of what patellar tracking may look like after realignment procedures. Using 3D modeling, concepts such as patella alta, trochlear depth, lateralization of the patellar entry point, trochlear curvature, and the presence of a proximal trochlear spur can help afford a better understanding of how trochlear anatomy may influence tracking while also providing insight as to the ideal tracking path. The use of 3D has recently emerged as a useful tool in multiple surgical subspecialties, particularly in situations involving surgical planning or complex anatomy. Given the complexity and variation in trochlear morphology in patients with trochlear dysplasia who develop either patellar instability or focal overloading, 3D modeling is well-suited to provide a perspective that can add to our understanding of trochlear dysplasia, and potentially even how we diagnose and treat it. Level of EvidenceLevel V, expert opinion.

TEXT GENERATION MODELS FOR PARAPHRASE ON KAZAKH LANGUAGE

This study delves into the relatively unexplored domain of natural language processing for the Kazakh language—a language with limited computational resources. The paper dissects the effectiveness of diffusion models and transformers in generating text, specifically paraphrases, which is a critical aspect of machine learning applications such as chatbots, virtual assistants, and automated translation services.
 The researchers methodically adapt these advanced models to understand and generate Kazakh text, tackling the unique challenges posed by the language's complex morphology. The paper is comprehensive in its approach, covering everything from the initial adaptation of the models to the Kazakh language context, to the creation of specialized tokenizer tools, to the translation and preparation of datasets for effective training.
 Through rigorous testing and performance analysis, the study identifies the strengths and weaknesses of each model type. This is critical as it informs the direction of future research and model development, with the goal of enhancing the fluency and accuracy of automated Kazakh text generation. The paper also discusses the broader impact of its findings, suggesting that the methodologies and insights gained could inform similar efforts in other low-resource languages, thereby contributing to the global field of NLP.
 The research concludes with reflections on the implications of their findings for the ongoing development of machine learning technologies, asserting the potential of these technologies to accommodate the intricacies of any language, given the right approach and resources. This work not only advances the technical capabilities for Kazakh text generation but also serves as a testament to the potential of machine learning to bridge language gaps and foster greater digital inclusivity.

Populatie en de verspreiding van taalverandering

The morphosyntactic structure of languages largely depends on the demographic structure of the speech community. Larger languages in high-contact communities tend to be morphologically less complex. Complex morphology is harder to acquire for adults, so morphology tends to become simpler with the increasing proportion of L2 speakers. Differences in morphosyntactic complexity are the result of diachronic processes, so the propagation of these processes should occur more rapidly in cities with a larger population, since the growth of cities is often due to immigration, yielding an influx of L2 learners. This hypothesis is tested on two alternations in the recent history of Dutch: the deflection of veel and the hortative alternation. For both changes I build a logistic regression model with the change as the dependent variable and time as the independent variable in interaction with the population size. The results for the hortative alternation indicate that language change is indeed faster in larger cities compared to smaller towns. However, the deflection of veel does not confirm this.

Advanced Synthesis and Characterization of CdO/CdS/ZnO Heterostructures for Solar Energy Applications.

This study introduces an innovative method for synthesizing Cadmium Oxide /Cadmium Sulfide/Zinc Oxide heterostructures (CdO/CdS/ZnO), emphasizing their potential application in solar energy. Utilizing a combination of electrochemical deposition and oxygen annealing, the research provides a thorough analysis of the heterostructures through scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) spectroscopy, X-ray diffraction (XRD), Raman spectroscopy, and photoluminescence (PL) spectroscopy. The findings reveal a complex surface morphology and a composite structure with significant contributions from hexagonal CdS and cubic CdO phases. The study highlights the uniformity in the distribution of luminescent centers and the crystalline quality of the heterostructures, which is evident from the PL analysis. The redshift observed in the emission peak and the additional peaks in the excitation spectrum indicate intricate optical properties influenced by various factors, including quantum confinement and lattice strain. The research demonstrates these heterostructures' potential in enhancing solar cells' efficiency and applicability in optoelectronic devices. This comprehensive characterization and analysis pave the way for future optimization and application in efficient and sustainable solar energy solutions.

Lusca: FIJI (ImageJ) based tool for automated morphological analysis of cellular and subcellular structures

The human body consists of diverse subcellular, cellular and supracellular structures. Neurons possess varying-sized projections that interact with different cellular structures leading to the development of highly complex morphologies. Aiming to enhance image analysis of complex biological forms including neurons using available FIJI (ImageJ) plugins, Lusca, an advanced open-source tool, was developed. Lusca utilizes machine learning for image segmentation with intensity and size thresholds. It performs particle analysis to ascertain parameters such as area/volume, quantity, and intensity, in addition to skeletonization for determining length, branching, and width. Moreover, in conjunction with colocalization measurements, it provides an extensive set of 29 morphometric parameters for both 2D and 3D analysis. This is a significant enhancement compared to other scripts that offer only 5–15 parameters. Consequently, it ensures quicker and more precise quantification by effectively eliminating noise and discerning subtle details. With three times larger execution speed, fewer false positive and negative results, and the capacity to measure various parameters, Lusca surpasses other existing open-source solutions. Its implementation of machine learning-based segmentation facilitates versatile applications for different cell types and biological structures, including mitochondria, fibres, and vessels. Lusca’s automated and precise measurement capability makes it an ideal choice for diverse biological image analyses.

Differences in vocal brain areas and astrocytes between the house wren and the rufous-tailed hummingbird.

The house wren shows complex song, and the rufous-tailed hummingbird has a simple song. The location of vocal brain areas supports the song's complexity; however, these still need to be studied. The astrocytic population in songbirds appears to be associated with change in vocal control nuclei; however, astrocytic distribution and morphology have not been described in these species. Consequently, we compared the distribution and volume of the vocal brain areas: HVC, RA, Area X, and LMAN, cell density, and the morphology of astrocytes in the house wren and the rufous-tailed hummingbird. Individuals of the two species were collected, and their brains were analyzed using serial Nissl- NeuN- and MAP2-stained tissue scanner imaging, followed by 3D reconstructions of the vocal areas; and GFAP and S100β astrocytes were analyzed in both species. We found that vocal areas were located close to the cerebral midline in the house wren and a more lateralized position in the rufous-tailed hummingbird. The LMAN occupied a larger volume in the rufous-tailed hummingbird, while the RA and HVC were larger in the house wren. While Area X showed higher cell density in the house wren than the rufous-tailed hummingbird, the LMAN showed a higher density in the rufous-tailed hummingbird. In the house wren, GFAP astrocytes in the same bregma where the vocal areas were located were observed at the laminar edge of the pallium (LEP) and in the vascular region, as well as in vocal motor relay regions in the pallidum and mesencephalon. In contrast, GFAP astrocytes were found in LEP, but not in the pallidum and mesencephalon in hummingbirds. Finally, when comparing GFAP astrocytes in the LEP region of both species, house wren astrocytes exhibited significantly more complex morphology than those of the rufous-tailed hummingbird. These findings suggest a difference in the location and cellular density of vocal circuits, as well as morphology of GFAP astrocytes between the house wren and the rufous-tailed hummingbird.

Towards urban wind utilization: The spatial characteristics of wind energy in urban areas

Urban wind energy is gaining increasing recognition for its environmentally friendly nature, particularly in the context of energy shortages. Predicting wind speed in urban environments is challenging due to the varying roughness and drag caused by obstacles on the ground. The complexity of urban morphology significantly disrupts the wind field and hampers the utilization of wind energy. This study aimed to investigate the diverse impacts of highly heterogeneous urban environment on the urban wind energy by innovatively establishing spatially varying power law wind profiles. Firstly, long-term LIDAR observations were conducted to measure the vertical wind profile in the study area. Then, a high-resolution LCZ map was created and integrated into the Weather Research and Forecasting (WRF) model to reproduce the urban wind field, which was validated using the observational data. Finally, the spatial characteristics of the urban wind field and wind energy were analyzed based on 3 representative points and the urban LCZ categories. The results show that the average power law exponents of each LCZ classification ranges from 0.29 to 0.75, with the average power law exponent of the high-volume building area being larger than 0.6. The highest wind power density in the study area can reach 343.3W/m2 at 200m height. Further, significant negative correlation coefficients were found between wind power density and building height, as well as building density, with values of approximately −0.6 and −0.35, respectively. Overall, the high-volume-ratio built-up areas with higher roughness had a notable impact on the wind speed by increasing the power law exponents. These spatially varying wind profiles are expected to provide technical support for the utilization of urban wind energy.