Branching Topology Research Articles

The Mitochondrial Genome of Melipona fasciculata (Apidae, Meliponini): Genome Organization and Comparative Analyses, Phylogenetic Implications and Divergence Time Estimations.

The native stingless bee Melipona fasciculata is economically and ecologically important to the Brazilian Northeast, providing a sustainable source of income to family farmers and being considered an effective pollinator in most ecosystems and crops. This study describes, for the first time, the mitogenome of the species and its phylogenetic position. The mitochondrial genome was sequenced using a MiSeq Sequencer (Illumina Inc.) and compared with other GenBank bee mitogenomes. The length of the mitochondrial DNA, excluding most of the control region, is 14,753bp, and contains 13 protein-coding genes (PCGs), 21 transfer RNAs, 2 ribosomal RNAs (16S and 12S), and 1 AT-rich region. The GC-content of the M. fasciculata mitogenome was 13.4%. Of the 36 coding regions, 12 tRNAs and 9 PCGs were encoded in the heavy strand, and 9 tRNAs, 4 PCGs and 2 rRNAs were encoded in the light strand. The relative orientation and gene order was the same as other stingless bee mitogenomes. Phylogenetic inference produced well-resolved relationships with high statistical support for concordant branch topologies, under different optimization schemes and model parameters, within and among Melipona, Bombus, Apis, and related clades of Hymenoptera. In general, our divergence time estimates, which were based on the concatenated gene sequences (PCGs + rRNAs) from various groups, overlapped estimations captured by Bayesian analysis from different studies. The divergence time among Melipona species was estimated to occur during the Oligocene, approximately 24 Mya (95% HPD 14-36 Mya). Our results represent a valuable addition to help understanding not only the taxonomy and evolution of Brazilian stingless bee species, but also to uncover historical dispersal and isolation patterns in Meliponinae.

Revisit compressible plane Couette and Poiseuille instability: Spectrum features of disturbances and dominant instability

Understanding the instability of compressible shear flows is of both academic interest and practical importance for predicting transition in such flows. In this paper, the linear stability of compressible plane Couette flow and Poiseuille flow in temporal mode is revisited, with emphasis on spectrum features of disturbances and dominant instability at different Mach numbers. The disturbance spectra of different discrete modes are presented, including acoustic modes (i.e., even modes II, IV, etc. and odd modes I, III, etc.) and viscous modes. The results show that as wavenumber increases, the even modes are first synchronized with the viscous branch when their phase velocities are a bit larger than zero, and subsequently synchronized with odd modes when their phase velocities coincide with each other. The synchronizations cause branching of the dispersion curves, and the even modes have several maxima of growth rate. The low-wavenumber maximum (with phase velocity close to zero) is associated with viscous instability and the higher-wavenumber maxima correspond to inviscid instability. Depending on the flow parameters, the branching topology may involve multimodes, which is different from the spectrum branching observed in hypersonic boundary layer. In compressible Couette flow, the mode-II viscous and inviscid instabilities dominate the instability at different Mach numbers. In compressible plane Poiseuille flow, multiple instability modes coexist, including the viscous Tollmien-Schlichting (TS) mode, the acoustic viscous instability modes II and IV and acoustic inviscid instability modes I and III. As Mach number increases (i.e., from incompressible to hypersonic), the mode TS, II, IV, and III would successively dominate the Poiseuille-flow instability. These results yield a comprehensive understanding of the disturbance spectrum and modal instability in compressible plane Couette and Poiseuille flows.

Reducing tree volume overestimation in quantitative structure models using modeled branch topology and direct twig measurements

Abstract Quantitative Structure Models (QSMs) are fit to tree point clouds to represent the topology of trees as a network of cylinders. QSMs allow for the calculation of metrics difficult to measure without destructive sampling, including total tree volume. Current limitations in terrestrial laser scanning technology make small branches difficult to accurately resolve, causing overestimation of small branch volume in QSMs, which can translate into overestimating tree biomass. We present a new method called Real Twig to correct overestimated small branch and twig cylinders in QSMs. Real Twig differs from current methods by using twig diameters measured directly from corresponding tree species to model a unique taper for every path in the QSM, using the QSM’s inherent branching topology, but without relying on predefined mathematical or allometric relationships. To test Real Twig, we generated QSMs for different sets of trees that had detailed dry mass and density measurements obtained via felling after scanning. QSM-based biomass estimates were obtained by multiplying the tree’s QSM-based volume estimate by the tree’s specific basic density value. We trained our method with high-quality data consisting of five northern red oak (Quercus rubra L.) and five red maple (Acer rubrum L.) trees, using two different versions of TreeQSM, a widely used algorithm for generating QSMs. We further tested our method on three publicly available datasets, including managed forests and large tropical trees, collected with both phase-shift or time-of-flight sensors. QSMs corrected with our Real Twig method showed a very large improvement in tree biomass estimation, with a relative mean error of −1.2%, a relative root mean square error of 10.5%, and a concordance correlation coefficient of 0.999, compared to a relative mean error 76.8%, a relative root mean square error of 48.7%, and a concordance correlation coefficient of 0.982, when using the standard outputs of TreeQSM.

Distribution network topology identification method based on state estimation with mixed integer programming and structural equation model

Distribution network topology identification (DNTI) is an important prerequisite for distribution system operation. Frequent topology changes and limited measurement equipment make identifying the correct topology unavoidably challenging. To this end, this paper proposes a state estimation method for distribution network topology identification using a small amount of phasor measurement unit (PMU) measurement data. Firstly, this paper introduces the application of the structural equation model (SEM) in distribution networks and explains the relationship between branch currents and topology. Then, auxiliary variables are introduced for the feasible domain problem present in the algorithm, which improves the solvability of the algorithm. In addition, this paper reconsiders the operation mode constraints in terms of nodes from the perspective of distribution network operation mode. The Structural Equation Modeling-based Mixed Integer Programming (SEM-MIP) method proposed in this paper can be solved using existing commercial solvers, and its effectiveness has been verified by simulation in IEEE 33-node test system and IEEE 123-node test system.

The K129 capsular polysaccharide produced by Acinetobacter baumannii MAR 15–4076 has the same composition as K84 but differs in the linkage between units altering the overall branching topology

Capsular polysaccharide (CPS) is a heteroglycan that coats the cell surface of most isolates of the important Gram-negative bacterial pathogen, Acinetobacter baumannii. Strain MAR 15–4076, a clinical isolate recovered in Russia in 2015, was found to carry the KL129 sequence at the CPS biosynthesis K locus. The CPS was isolated from the strain and studied by sugar analysis, Smith degradation, one- and two-dimensional 1H and 13C NMR spectroscopy. It was composed of branched pentasaccharide units that include a →3)-α-l-Rhap-(1 → 3)-α-l-Rhap-(1 → 3)-β-d-GlcpNAc-(1→ mainchain and α-d-ManpNAc-(1 → 3)-l-Rhap side branch. Though the pentasaccharide units are identical to those that make up the K84 CPS produced by A. baumannii LUH5540, the units are linked differently via the substitution of an alternate l-Rhap residue, resulting in a difference in the overall topology of the CPS. This was due to the replacement of the Wzy polymerase gene encoded at the K locus.

Asynchronous Ring Opening of Cyclic Carbonate and Glycidyl Ether Induced Phase Evolution Towards Heat-Free and Rapid-Bonding Superior Epoxy Adhesive.

Structural adhesives that do not require heating are in high demand in the automotive and electronics industries. However, it remains a challenge to develop robust adhesives that rapidly achieve super adhesion near ambient temperature. Herein, a room-temperature curable, fast-bonding, and super strong epoxy-based structural adhesive was designed from the perspective of cross-scale structure, which lies in threefold pivotal aspects: (i) high branching topology of glycerol carbonate-capped polyurethane (PUGC) increases the kinetics of the ring-opening reaction, contributing to fast crosslinking and the formation of abundant urethane and hydroxyl moieties; (ii) asynchronous crosslinking of epoxy and PUGC synergistically induces phase separation of PUGC within the epoxy resin and the resulting PUGC domains surrounded by interpenetrated shell serves to efficiently toughen the matrix; (iii) abundant dynamic hydrogen bonds including urethane and hydroxyl moieties, along with the elastomeric PUGC domains, dissipate energy of shearing force. As a result, the adhesive strength rapidly grows to 16 MPa within 4 hours, leveling off to 21 MPa after 7 hours, substantially outperforming commercial room-temperature curable epoxy adhesives. The results of this study could advance the field of high-performance adhesives and provide valuable insights into designing materials for efficient curing at room temperature.

Asynchronous Ring Opening of Cyclic Carbonate and Glycidyl Ether Induced Phase Evolution Towards Heat‐Free and Rapid‐Bonding Superior Epoxy Adhesive

AbstractStructural adhesives that do not require heating are in high demand in the automotive and electronics industries. However, it remains a challenge to develop robust adhesives that rapidly achieve super adhesion near ambient temperature. Herein, a room‐temperature curable, fast‐bonding, and super strong epoxy‐based structural adhesive was designed from the perspective of cross‐scale structure, which lies in threefold pivotal aspects: (i) high branching topology of glycerol carbonate‐capped polyurethane (PUGC) increases the kinetics of the ring‐opening reaction, contributing to fast crosslinking and the formation of abundant urethane and hydroxyl moieties; (ii) asynchronous crosslinking of epoxy and PUGC synergistically induces phase separation of PUGC within the epoxy resin and the resulting PUGC domains surrounded by interpenetrated shell serves to efficiently toughen the matrix; (iii) abundant dynamic hydrogen bonds including urethane and hydroxyl moieties, along with the elastomeric PUGC domains, dissipate energy of shearing force. As a result, the adhesive strength rapidly grows to 16 MPa within 4 hours, leveling off to 21 MPa after 7 hours, substantially outperforming commercial room‐temperature curable epoxy adhesives. The results of this study could advance the field of high‐performance adhesives and provide valuable insights into designing materials for efficient curing at room temperature.

Variation-aware directed graph convolutional networks for skeleton-based action recognition

Directed Graph convolutional networks (DGCNs) have been indeed gaining attention and being applied in skeleton-based action recognition tasks to capture the hierarchical relationships of skeleton via directed graph topology. However, they typically pay the same attention to regions with variations and regions with relative statics of the skeleton, which leads to low accuracy when recognizing fine-grained actions. To this end, we design an innovative variation-aware directed graph convolutional network (VA-DGCN) to focus on the regions where the variation takes place. VA-DGCN comprises a variation-aware directed spatial convolution (VDSC) module and a multi-scale contrastive temporal convolution (MCTC) module. Specifically, in order to capture subtle variations in fine-grained actions, VDSC introduce the average posture of the action sequence as the static anchor. In VDSC, the channel-specific topology branch is designed to model the different kinematic properties of different channels to extract global features, to which global-attention graph convolution is added for unnaturally connected joints. Subsequently, we identify the regions with variations by comparing global features acquired from action sequence and average posture. In temporal, MCTC comprises multiple branches for extracting temporal features at different scales. Moreover, to maximize the mutual information between branches, we introduce contrastive learning to drive the module to learn more meaningful action representations. We conduct extensive experiments on three public datasets to validate the feasibility and efficacy of our proposed VA-DGCN.

Low-frequency somatic mutations are heritable in tropical trees Dicorynia guianensis and Sextonia rubra

Somatic mutations potentially play a role in plant evolution, but common expectations pertaining to plant somatic mutations remain insufficiently tested. Unlike in most animals, the plant germline is assumed to be set aside late in development, leading to the expectation that plants accumulate somatic mutations along growth. Therefore, several predictions were made on the fate of somatic mutations: mutations have generally low frequency in plant tissues; mutations at high frequency have a higher chance of intergenerational transmission; branching topology of the tree dictates mutation distribution; and exposure to UV (ultraviolet) radiation increases mutagenesis. To provide insights into mutation accumulation and transmission in plants, we produced two high-quality reference genomes and a unique dataset of 60 high-coverage whole-genome sequences of two tropical tree species, Dicorynia guianensis (Fabaceae) and Sextonia rubra (Lauraceae). We identified 15,066 de novo somatic mutations in D. guianensis and 3,208 in S. rubra, surprisingly almost all found at low frequency. We demonstrate that 1) low-frequency mutations can be transmitted to the next generation; 2) mutation phylogenies deviate from the branching topology of the tree; and 3) mutation rates and mutation spectra are not demonstrably affected by differences in UV exposure. Altogether, our results suggest far more complex links between plant growth, aging, UV exposure, and mutation rates than commonly thought.

FLUID FLOW THROUGH ISOMERIC CONSTRUCTAL NETWORKS OF TUBES

The branching topology of tree networks has a considerable influence on the distribution of fluid flow inside them. Fluid flow asymmetry (an unequal distribution of fluid flow between the daughter tubes) can arise in geometrically symmetric branches. It is important to be able to pinpoint the reason for this, which is still not fully understood. This study compares tree flow network designs with the same number of tubes of equal sizes but attached to one another in various directions, i.e., network isomers. The flow resistance and fluid flow distribution assessment within the networks are calculated based on the computational fluid dynamics results. This study shows, among other results, that the flow asymmetries are more noticeable at higher bifurcation levels, and the performance of tree designs is highly dependent on how the tubes are arranged in the network, especially how they are aligned at different levels of bifurcation. Practical guidelines that can immediately produce significant insights into the relationship between the incidence of asymmetry in the flow and alignments of the tubes between levels are defined. The findings of this study will be useful to designers in improving the design and management of these networks.

The energy dissipation topology and characteristics of GaInSn liquid metal and ZnO resistors based on high-voltage DC circuit breakers

High voltage direct current (HVDC) circuit breakers play a very crucial role in HVDC transmission systems. Energy dissipation circuit branches are the main part of the HVDC breaker. The volume and size of energy dissipation branches are the bottlenecks of the HVDC breaker, and the existing energy dissipation schemes can not meet the lightweight requirements. In this work, a combined energy dissipation scheme based on gallium indium tin (GaInSn) liquid metal and ZnO resistors is proposed for the energy dissipation branch of a circuit breaker. Firstly, the self-shrinking of GaInSn liquid metal mechanism is analyzed and the energy dissipation characteristics at different stages are investigated. The variation patterns of short circuit current, arc voltage, resistance and other parameters in the process of liquid metal arcing and energy dissipation are obtained. Secondly, the circuit model of liquid metal in arcing and energy dissipation process is established. The equivalent nonlinear time-varying conductance of the arcing process of the liquid metal dissipative element is simulated by combining the volt-ampere characteristic curves of the liquid metal dissipative module. Finally, the topology of energy dissipation circuit branches combined by GaInSn liquid metal and ZnO resistors is proposed. At the same time, the HVDC breaker model combined by a hybrid DC circuit breaker and energy dissipation circuit branches is constructed. The energy dissipation ratio and distribution law of GaInSn liquid metal and ZnO resistors in the composite type of circuit breaker are verified. The composite dissipation improves the overall energy dissipation density of the circuit breaker compared to the conventional ZnO resistors energy dissipation.

Spatial variability in topology, connectivity and permeability of deformation band networks: Insights from the Jurassic Entrada Sandstone, Utah

Networks of deformation bands have the potential to act as baffles to fluid flow, yet little work has been devoted to addressing their network properties, such as topology and connectivity. Motivated by this, we investigate the two-dimensional spatial variability of areal intensity, node and branch topology, and network connectivity within select deformation band networks in the Jurassic Entrada Sandstone (Utah). The results highlight distinct topological signatures for deformation band networks, dominated by Y-nodes, and IC- and CC-branches. Low proportion of isolated II-branches reflects the evolution of deformation bands through bifurcation and abutment (Y-nodes) to form interconnected networks. A key observation is that network connectivity and topology exhibit great spatial variability within one and the same deformation band network. We evaluate effective permeabilities across the deformation band network, incorporating a topological measure of network connectivity into the permeability calculations. Results highlight that deformation band networks with >1.5 connections per branch can significantly reduce effective permeabilities, whereas networks with low connectivity may offer pathways for tortuous fluid flow.

Maximum Power Control Method for Two-branch Online Energy Extraction System

Aiming at the problem that the fluctuation of the current on the cable causes the change of the parameters in the magnetic core, that is, the excitation inductance and the excitation resistance change, and the energy extraction system cannot maintain the resonance and the maximum power output, an online power extraction method combining the dual branch topology and the resonance power control is proposed. By controlling the PWM rectifier circuit, the load impedance is changed so that it is always equivalent to the matching impedance of CT. The parallel resonance state is maintained when the transmission line current changes in a wide range, so that more active power flows to the energy-taking branch to achieve the maximum energy-taking power and assure the normal operation of the energy source. The correctness of the proposed method is verified by Maxwell/Simulink software simulation.

A Next-Generation 3D Tissue-Engineered Model of the Human Brain Microvasculature to Study the Blood-Brain Barrier.

More than a billion people are affected by neurological disorders, and few have effective therapeutic options. A key challenge that has prevented promising preclinically proven strategies is the translation gap to the clinic. Humanized tissue engineering models that recreate the brain environment may aid in bridging this translational gap. Here, we showcase the methodology that allows for the practical fabrication of a comprehensive microphysicological system (MPS) of the blood-brain barrier (BBB). Compared to other existing 2D and 3D models of the BBB, this model features relevant cytoarchitecture and multicellular arrangement, with branching and network topologies of the vascular bed. This process utilizes 3D bioprinting with digital light processing to generate a vasculature lumen network surrounded by embedded human astrocytes. The lumens are then cellularized with primary human brain microvascular endothelial cells and pericytes. To initiate mechanotransduction pathways and complete maturation, vascular structures are continuously perfused for 7 days. Constructs are validated for complete endothelialization with viability dyes prior to functional assessments that include barrier integrity (permeability) and immune-endothelial interactions. This MPS has applications for the study of novel therapeutics, toxins, and elucidating mechanisms of pathophysiology.

Phylogenomic branch length estimation using quartets.

Branch lengths and topology of a species tree are essential in most downstream analyses, including estimation of diversification dates, characterization of selection, understanding adaptation, and comparative genomics. Modern phylogenomic analyses often use methods that account for the heterogeneity of evolutionary histories across the genome due to processes such as incomplete lineage sorting. However, these methods typically do not generate branch lengths in units that are usable by downstream applications, forcing phylogenomic analyses to resort to alternative shortcuts such as estimating branch lengths by concatenating gene alignments into a supermatrix. Yet, concatenation and other available approaches for estimating branch lengths fail to address heterogeneity across the genome. In this article, we derive expected values of gene tree branch lengths in substitution units under an extension of the multispecies coalescent (MSC) model that allows substitutions with varying rates across the species tree. We present CASTLES, a new technique for estimating branch lengths on the species tree from estimated gene trees that uses these expected values, and our study shows that CASTLES improves on the most accurate prior methods with respect to both speed and accuracy. CASTLES is available at https://github.com/ytabatabaee/CASTLES.

Data science analysis of Vassiliev invariants and knot similarity based on distributed machine learning

INTRODUCTION: Knot theory has a long history, and as a branch of topology, it has received extensive attention. At present, the scientific analysis of data based on the similarity of Vassiliev invariants and knots under machine learning technology is the focus of the mathematical community. However, at present, there are some difficulties in the research work on the similarity of Vassiliev invariants and knots. These difficulties not only delay the progress of Vassiliev invariants research, but also slow down the speed of knot similarity research.OBJECTIVES: However, with the acceleration of the intelligent process, various intelligent technologies have been applied in the research of mathematics, biology and physics, providing excellent help for the research of many disciplines. Therefore, machine learning technology could be used to carry out new research on Vassiliev invariants and knot similarity.METHODS: Traditional knot analysis technology was combined with machine learning technology to find a more efficient and stable way of exploring Vassiliev invariants and knot similarity. his paper proposed a research method of data scientific analysis based on Vassiliev invariants and knot similarity under machine learning technology. Its purpose was to combine traditional knot research methods with machine learning technology to improve the efficiency of knot research. The algorithm proposed in this paper was the knot Vassiliev invariant analysis algorithm based on machine learning, which could use the intelligent and efficient analysis algorithm of machine learning technology to process the data of complex knots. This algorithm has improved the accuracy of the analysis of knot characteristics, and reduced the analysis time and the memory consumption at runtime.RESULTS: By testing the similarity between the Vassiliev invariant based on machine learning and the knot, the results showed that the analysis accuracy of the traditional Vassiliev invariant computing technology for the chiral characteristics, the number of intersections and the number of knots in the knot image was 84.25%, 83.27% and 85.56% respectively. The accuracy of knot Vassiliev invariant analysis algorithm based on machine learning for these indicators was 91.87%, 92.66% and 92.12% respectively. Obviously, the knot Vassiliev invariant analysis algorithm based on machine learning was superior to the traditional knot computing technology, and its analysis results were more excellent.CONCLUSION: In general, the research topic proposed in this paper has been proved to be of practical value. This research result proved that machine learning technology could play an excellent role in the current knot research, which correspondingly expanded the research direction of Vassiliev invariants and knot similarity.

Scaling properties of RNA as a randomly branching polymer.

Formation of base pairs between the nucleotides of a ribonucleic acid (RNA) sequence gives rise to a complex and often highly branched RNA structure. While numerous studies have demonstrated the functional importance of the high degree of RNA branching-for instance, for its spatial compactness or interaction with other biological macromolecules-RNA branching topology remains largely unexplored. Here, we use the theory of randomly branching polymers to explore the scaling properties of RNAs by mapping their secondary structures onto planar tree graphs. Focusing on random RNA sequences of varying lengths, we determine the two scaling exponents related to their topology of branching. Our results indicate that ensembles of RNA secondary structures are characterized by annealed random branching and scale similarly to self-avoiding trees in three dimensions. We further show that the obtained scaling exponents are robust upon changes in nucleotide composition, tree topology, and folding energy parameters. Finally, in order to apply the theory of branching polymers to biological RNAs, whose length cannot be arbitrarily varied, we demonstrate how both scaling exponents can be obtained from distributions of the related topological quantities of individual RNA molecules with fixed length. In this way, we establish a framework to study the branching properties of RNA and compare them to other known classes of branched polymers. By understanding the scaling properties of RNA related to its branching structure, we aim to improve our understanding of the underlying principles and open up the possibility to design RNA sequences with desired topological properties.

Synthesis and characterization of eco‐friendly poly(ε‐caprolactone) plasticizer facilitating phthalate‐free polyvinyl chloride with novel star/net‐shaped structures

AbstractPotential migration and toxicity limited applications of traditional plasticizers like bis(2‐ethylhexyl) phthalate (DEHP) in food packing and medical machinery and so forth. Safe and feasible bio‐based plasticizers have recently moved into the limelight of research hotspot. Here, we designed series polyester plasticizers of various branches with star‐shaped poly(ε‐caprolactone) (SPCLs) and net‐shaped poly(ε‐caprolactone) (NPCLs) structures by one‐pot solvent‐free synthesis with glycidol and polyglycerol as initiators. Structures of SPCLs and NPCLs were further verified by FTIR, 1H NMR, and 13C NMR. Moreover, the properties of the plasticizers and plasticized PVC were evaluated by differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), thermogravimetric analysis (TGA) and tensile test. PVC films plasticized with the SPCLs or NPCLs exhibited increased plasticizing effect, compatibility, flexibility and cold resistance. The lowest Tg value of plasticized PVC samples (PVC/NPCL1‐C4) was −45.3°C, which made the plasticization efficiency reach 122.5%. In particular, PVC/SPCLs‐C4 (SPCL2‐C4) had 107% higher elongation at break than PVC/DEHP. Meanwhile, the initial decomposition temperature Ti value of the PVC blends maximum increased from 240 to 328°C when DEHP was completely replaced by SPCLs or NPCLs, indicating improved thermal stability of the plasticizer. Overall, “green” plasticizers with fining topological branch design would be promising to partially replace petroleum‐based plasticizers.

Genome structures resolve the early diversification of teleost fishes

Accurate species phylogenies are a prerequisite for all evolutionary research. Teleosts are the largest and most diversified group of extant vertebrates, but relationships among their three oldest extant lineages remain unresolved. On the basis of seven high-quality new genome assemblies in Elopomorpha (tarpons, eels), we revisited the topology of the deepest branches of theteleost phylogeny using independent gene sequence and chromosomal rearrangement phylogenomic approaches. These analyses converged to a single scenario that unambiguously places the Elopomorpha and Osteoglossomorpha (arapaima, elephantnose fish) in a monophyletic sister group to all other teleosts, i.e., the Clupeocephala lineage (zebrafish, medaka). This finding resolves more than 50 years of controversy on the evolutionary relationships of these lineages and highlights the power of combining different levels of genome-wide information to solve complex phylogenies.

High-Resolution Transcriptomic Landscape of the Human Submandibular Gland

Saliva-secreting and transporting cells are part of the complex cellular milieu of the human salivary gland, where they play important roles in normal glandular physiology and diseased states. However, comprehensive molecular characterization, particularly at single-cell resolution, is still incomplete, in part due to difficulty in procuring normal human tissues. Here, we perform an in-depth analysis of male and female adult human submandibular gland (SMG) samples by bulk RNA sequencing (RNA-seq) and examine the molecular underpinnings of the heterogeneous cell populations by single-cell (sc) RNA-seq. Our results from scRNA-seq highlight the remarkable diversity of clusters of epithelial and nonepithelial cells that reside in the SMG that is also faithfully recapitulated by deconvolution of the bulk-RNA data sets. Our analyses reveal complex transcriptomic heterogeneity within both the ductal and acinar subpopulations and identify atypical SMG cell types, such as mucoacinar cells that are unique to humans and ionocytes that have been recently described in the mouse. We use CellChat to explore ligand–receptor interactome predictions that likely mediate crucial cell–cell communications between the various cell clusters. Finally, we apply a trajectory inference method to investigate specific cellular branching points and topology that offers insights into the dynamic and complex differentiation process of the adult SMG. The data sets and the analyses herein comprise an extensive wealth of high-resolution information and a valuable resource for a deeper mechanistic understanding of human SMG biology and pathophysiology.