Sparse Branching Research Articles

What Factors Are Limiting Financial Inclusion and Development in Peru? Empirical Evidence

Despite recent efforts in Peru to boost financial inclusion, significant issues of exclusion persist, especially among vulnerable groups. This article aims to identify and analyze areas at risk of financial exclusion using a multifaceted methodology: the Financial Access Survey (FAS) for comparative analysis (Peru versus other countries and regions), geographical-distribution analysis, and the Access to Cash Index (ACI) methodology. Findings reveal that remote rural areas of Peru, particularly those inland, as well as mountainous or jungle regions, face higher risks of financial exclusion due to low digital literacy, limited digital banking usage, sparse branch and ATM networks, and inadequate transportation infrastructure. These insights can inform targeted public policies to enhance financial inclusion in Peru, as well as the development.

LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network

Recently, regression-based methods, which predict parameterized text shapes for text localization, have gained popularity in scene text detection. However, the existing parameterized text shape methods still have limitations in modeling arbitrary-shaped texts due to ignoring the utilization of text-specific shape information. Moreover, the time consumption of the entire pipeline has been largely overlooked, leading to a suboptimal overall inference speed. To address these issues, we first propose a novel parameterized text shape method based on low-rank approximation. Unlike other shape representation methods that employ data-irrelevant parameterization, our approach utilizes singular value decomposition and reconstructs the text shape using a few eigenvectors learned from labeled text contours. By exploring the shape correlation among different text contours, our method achieves consistency, compactness, simplicity, and robustness in shape representation. Next, we propose a dual assignment scheme for speed acceleration. It adopts a sparse assignment branch to accelerate the inference speed, and meanwhile, provides ample supervised signals for training through a dense assignment branch. Building upon these designs, we implement an accurate and efficient arbitrary-shaped text detector named LRANet. Extensive experiments are conducted on several challenging benchmarks, demonstrating the superior accuracy and efficiency of LRANet compared to state-of-the-art methods. Code is available at: https://github.com/ychensu/LRANet.git

Rethinking the necessity of image fusion in high-level vision tasks: A practical infrared and visible image fusion network based on progressive semantic injection and scene fidelity

Image fusion aims to integrate complementary characteristics of source images into a single fused image that better serves human visual observation and machine vision perception. However, most existing image fusion algorithms primarily focus on improving the visual appeal of fused images. Although there are some semantic-driven methods that consider semantic requirements of downstream applications, none of them have demonstrated the potential of image-level fusion compared to feature-level fusion, which fulfills high-level vision tasks directly on multi-modal features rather than on a fused image. To overcome these limitations, this paper presents a practical infrared and visible image fusion network based on progressive semantic injection and scene fidelity constraints, termed PSFusion. First of all, the sparse semantic perception branch extracts sufficient semantic features, which are then progressively integrated into the fusion network using the semantic injection module to fulfill the semantic requirements of high-level vision tasks. The scene fidelity path within the scene restoration branch is devised to ensure that the fusion features contain complete information for reconstructing the source images. Additionally, the contrast mask and salient target mask are employed to construct the fusion loss to maintain impressive visual effects of fusion results. In particular, we provide quantitative and qualitative analyses to demonstrate the potential of image-level fusion compared to feature-level fusion for high-level vision tasks. With the rapid advancement of large-scale models, image-level fusion can expeditiously leverage the advantages of multi-modal data and state-of-the-art (SOTA) unimodal segmentation to achieve superior performance. Furthermore, extensive comparative experiments demonstrate the superiority of our PSFusion over SOTA image-level fusion alternatives in terms of visual appeal and high-level semantics. Even under harsh circumstances, our method offers satisfactory fusion results to serve subsequent high-level vision applications. The source code is available at https://github.com/Linfeng-Tang/PSFusion.

Multi-Sensor Fusion Tomato Picking Robot Localization and Mapping Research

To address the current problem of unsatisfactory localization and map building of an intelligent picking robot mobile platform in an unstructured picking environment, a multi-sensor fusion localization and map building method is proposed to fuse the visual odometer position estimation results with IMU and wheel odometer. And the pre-fusion processing of wheel odometer data is proposed to solve the problem of large errors of wheel odometers on uneven ground. The multi-sensor fusion scheme is proposed to fuse vision and laser SLAM methods to build a more information-rich point cloud and raster fusion map for the problem of non-closure caused by sparse branches. The experimental results show that the multi-sensor fusion SLAM method can obtain more accurate, richer, and more robust maps.

Tree Branch Skeleton Extraction from Drone-Based Photogrammetric Point Cloud

Calculating the complex 3D traits of trees such as branch structure using drones/unmanned aerial vehicles (UAVs) with onboard RGB cameras is challenging because extracting branch skeletons from such image-generated sparse point clouds remains difficult. This paper proposes a skeleton extraction algorithm for the sparse point cloud generated by UAV RGB images with photogrammetry. We conducted a comparison experiment by flying a UAV from two altitudes (50 m and 20 m) above a university orchard with several fruit tree species and developed three metrics, namely the F1-score of bifurcation point (FBP), the F1-score of end point (FEP), and the Hausdorff distance (HD) to evaluate the performance of the proposed algorithm. The results show that the average values of FBP, FEP, and HD for the point cloud of fruit tree branches collected at 50 m altitude were 64.15%, 69.94%, and 0.0699, respectively, and those at 20 m were 83.24%, 84.66%, and 0.0474, respectively. This paper provides a branch skeleton extraction method for low-cost 3D digital management of orchards, which can effectively extract the main skeleton from the sparse fruit tree branch point cloud, can assist in analyzing the growth state of different types of fruit trees, and has certain practical application value in the management of orchards.

Mouse Mammary Gland Whole Mount Density Assessment across Different Morphologies Using a Bifurcated Program for Image Processing

Mammographic density is associated with increased breast cancer risk. Conventional visual assessment of murine mouse models does not include quantified total density analysis. A bifurcated method was sufficient to obtain relative density scores on a broad range of two-dimensional whole mount images that contained both normal and abnormal findings. Image processing techniques, including a ridge operator and a gaussian denoising method, were used to isolate background away from mammary epithelium and use mean pixel intensity to represent mammary density on genetically engineered mouse models for breast cancer in mice 4 to 29 months of age. The bifurcated method allowed for application of an optimal image processing approach for the structural elements present in the whole mount images. Gaussian denoising was the optimal approach when more dense lobular growth and tertiary branching dominate and a ridge operator when epithelial growth was more sparse and secondary branching was the more dominant structural feature. The two processing approaches were combined in a single experimental flow program using an initial image density measurement as the decision point between the two approaches. Higher density was associated with lobular growth, tertiary branching, fibrotic stroma, and presence of cancer. The significance of the study is development of a readily accessible program for digital assessment of mammary gland whole mount density across a range of mammary gland morphologies.

Conversion of sheath blight susceptible indica and japonica rice cultivars into moderately resistant through expression of antifungal β-1,3-glucanase transgene from Trichoderma spp.

Rice is an important food crop for three billion people worldwide. The crop is vulnerable to several diseases. Sheath blight caused by fungal pathogen Rhizoctonia solani is a significant threat to rice cultivation accounting for up to 50% yield losses. The pathogen penetrates leaf blades and sheaths, leading to plant necrosis; and major disease resistance gene against the pathogen is not available. This study describes development of sheath blight resistant transgenic indica and japonica rice cultivars through introduction of antifungal β-1,3-glucanase transgene cloned from Trichoderma. The transgene integration and expression in transformed T0 rice plants was examined by PCR, RT-PCR, qRT-PCR demonstrating up to 5-fold higher expression as compared to non-transgenic plants. The bioassay of T0, T1 and homozygous T2 progeny plants with virulent R. solani isolate revealed that plants carrying high level of β-1,3-glucanase expression displayed moderately resistant reaction to the pathogen. The optical micrographs of leaf sheath cells from moderately resistant plant after pathogen inoculation displayed presence of a few hyphae with sparse branching; on the contrary, pathogen hyphae in susceptible non-transgenic plant cells were present in abundance with profuse hyphal branching and forming prominent infection cushions. The disease severity in T2 progeny plants was significantly less as compared to non-transgenic plants confirming role of β-1,3-glucanase in imparting resistance.

Tucana B: A Potentially Isolated and Quenched Ultra-faint Dwarf Galaxy at D ≈ 1.4 Mpc* *This paper includes data gathered with the 6.5 m Magellan Telescope at Las Campanas Observatory, Chile.

We report the discovery of Tucana B, an isolated ultra-faint dwarf galaxy at a distance of D = 1.4 Mpc. Tucana B was found during a search for ultra-faint satellite companions to the known dwarfs in the outskirts of the Local Group, although its sky position and distance indicate the nearest galaxy to be ∼500 kpc distant. Deep ground-based imaging resolves Tucana B into stars, and it displays a sparse red giant branch consistent with an old, metal-poor stellar population analogous to that seen in the ultra-faint dwarf galaxies of the Milky Way, albeit at fainter apparent magnitudes. Tucana B has a half-light radius of 80 ± 40 pc and an absolute magnitude of mag ( L ⊙), which is again comparable to the Milky Way’s ultra-faint satellites. There is no evidence for a population of young stars, either in the optical color–magnitude diagram or in GALEX archival ultraviolet imaging, with the GALEX data indicating for star formation on ≲100 Myr timescales. Given its isolation and physical properties, Tucana B may be a definitive example of an ultra-faint dwarf that has been quenched by reionization, providing strong confirmation of a key driver of galaxy formation and evolution at the lowest mass scales. It also signals a new era of ultra-faint dwarf galaxy discovery at the extreme edges of the Local Group.

New genus and species of black coral from the SW Pacific and Antarctica (Cnidaria: Anthozoa: Antipatharia: Schizopathidae).

A new genus, Diplopathes, in the family Schizopathidae, and three new species are described from the Southwest Pacific and Antarctic region based on morphological data. The new genus superficially resembles Telopathes in being branched and having simple, bilateral pinnules, but differs in having strictly alternately arranged pinnules, and in having small polyps 4 mm or less in transverse diameter. Mitochondrial DNA placed Diplopathes and Telopathes in separate clades within the Schizopathidae, thus supporting the significance of seemingly subtle anatomical differences. The new species are: D. antarctica, with sparse branching, pinnules of up to 7 cm long, and polypar spines up to 0.045 mm tall; D. multipinnata, with dense branching, pinnules up to 3 cm long, and polypar spines up to 0.1 mm tall; and D. tuatoruensis, with very sparse branching, pinnules up to 10 cm long, and polypar spines up to 0.1 mm. Interestingly, the three new species do not form a monophyletic clade based on mitochondrial DNA. We propose and discuss two hypotheses to explain the results of the phylogenetic reconstruction, including that molecular and physical change are uncoupled or that we have uncovered another example of morphological convergence in unrelated species.

Pegasus V/Andromeda XXXIV–a newly discovered ultrafaint dwarf galaxy on the outskirts of Andromeda

ABSTRACT We report the discovery of an ultrafaint dwarf in the constellation of Pegasus. Pegasus V (Peg V)/Andromeda XXXIV was initially identified in the public imaging data release of the DESI Legacy Imaging Surveys and confirmed with deep imaging from Gemini/GMOS-N. The colour–magnitude diagram shows a sparse red giant branch (RGB) population and a strong overdensity of blue horizontal branch stars. We measure a distance to Peg V of $D=692^{+33}_{-31}$ kpc, making it a distant satellite of Andromeda with MV = −6.3 ± 0.2 and a half-light radius of rhalf = 89 ± 41 pc. It is located ∼260 kpc from Andromeda in the outskirts of its halo. The RGB is well fitted by a metal-poor isochrone with [Fe/H] = −3.2, suggesting it is very metal poor. This, combined with its blue horizontal branch, could imply that it is a reionization fossil. This is the first detection of an ultrafaint dwarf outside the deep Pan-Andromeda Archaeological Survey area, and points to a rich, faint satellite population in the outskirts of our nearest neighbour.

Structured Binary Neural Networks for Image Recognition

In this paper, we propose to train binarized convolutional neural networks (CNNs) that are of significant importance for deploying deep learning to mobile devices with limited power capacity and computing resources. Previous works on quantizing CNNs often seek to approximate the floating-point information of weights and/or activations using a set of discrete values. Such methods, termed value approximation here, typically are built on the same network architecture of the full-precision counterpart. Instead, we take a new “structured approximation” view for network quantization — it is possible and valuable to exploit flexible architecture transformation when learning low-bit networks, which can achieve even better performance than the original networks in some cases. In particular, we propose a “group decomposition” strategy, termed GroupNet, which divides a network into desired groups. Interestingly, with our GroupNet strategy, each full-precision group can be effectively reconstructed by aggregating a set of homogeneous binary branches. We also propose to learn effective connections among groups to improve the representation capability. To improve the model capacity, we propose to dynamically execute sparse binary branches conditioned on input features while preserving the computational cost. More importantly, the proposed GroupNet shows strong flexibility for a few vision tasks. For instance, we extend the GroupNet for accurate semantic segmentation by embedding the rich context into the binary structure. The proposed GroupNet also shows strong performance on object detection. Experiments on image classification, semantic segmentation, and object detection tasks demonstrate the superior performance of the proposed methods over various quantized networks in the literature. Moreover, the speedup and runtime memory cost evaluation comparing with related quantization strategies is analyzed on GPU platforms, which serves as a strong benchmark for further research.

Lateral root elongation enhances nitrogen-use efficiency in maize genotypes at the seedling stage.

BACKGROUNDMaize plants show great variation in root morphological response to nitrogen (N) deficit, and such alterations often determine N‐use efficiency (NUE) plants. This study assessed genotypic variation in root morphology and NUE in selected 20 maize genotypes with contrasting root system size grown in a semi‐hydroponic phenotyping system for 38 days under control (4 mmol L−1 NO3 −) and low N (LN) (40 μmol L−1) for 38 days after transplanting.RESULTSMaize genotypes exhibited different responses to LN stress in each of the 28 measured shoot and root traits. The 20 genotypes were assigned into one of the three groups: N‐efficient (eight genotypes), medium (four genotypes), and N‐inefficient (eight genotypes), based on shoot dry weight ratio (the ratio of shoot dry weight at LN and control) ± one standard error. In response to LN stress, the N‐inefficient genotypes had significant reduction in biomass production by ~58% in shoots and ~64% in roots, while the N‐efficient genotypes maintained their biomass. Under LN supply N‐efficient genotypes showed a plasticity response that would result in both sparse lateral branching and increased root elongation as a whole or at each growth strata, and N efficiency positively correlated with lateral or axial root elongation and root elongation at different depths.CONCLUTSIONThe total lateral root length was the main contributor to the improved N foraging and utilization in maize under LN conditions, followed by axial root length. Total lateral root length can be considered in breeding programs for producing maize cultivars with high NUE at the early seedling stage. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Taxonomic revision of Dictyosiphon (Ectocarpales s.l., Phaeophyceae) from the north-western Pacific, with descriptions of D. asiaticus sp. nov. and D. sparsus sp. nov

ABSTRACT Dictyosiphon asiaticus sp. nov. and D. sparsus sp. nov. are newly described from Japan and the north-western Pacific. Molecular phylogenies based on the mitochondrial cox1 and cox3 and chloroplast atpB, psaA, psbA and rbcL DNA sequences revealed that Dictyosiphon comprises at least seven lineages worldwide. Japanese Dicytosiphon species formed two clades with high support and were sister to a clade including specimens from the cold-water regions of the northern Pacific, the Arctic, and the north-western Atlantic. Specimens of one of the Japanese clades (= D. asiaticus sp. nov.) were morphologically similar to D. foeniculaceus (generitype described from Scotland, UK), having densely branched thalli with fine branches, and were epiphytic on Scytosiphon spp. and Chordaria spp. Specimens of the other Japanese clade (= D. sparsus sp. nov.) were morphologically similar to D. chordaria, having somewhat thicker, sparse branches with blunt tips, and were epiphytic on Chordaria spp. We assign the third clade distributed on both sides of the North Pacific to D. sinicola, which was described from Washington, USA, for the following reasons: Dictyosiphon specimens in the region having relatively densely branched thalli with fine tips have been identified as D. foeniculaceus, and D. sinicola was distinguished from them by its remarkably larger thallus, while anatomically they are very similar. However, D. foeniculaceus is not widely distributed in the region, with only one genetic lineage (species) reported, so that those two taxa are considered to be ecotypes of the same species, and D. sinicola is the valid name for the taxon.

A Sparse Branch and Bound Optimization of Noisy Weighted DAG Modification Under Constraints: A Method for Monocular Data Association to Multiple Laser Planes

The line segments of multiple (and nearly parallel) planar laser sources appearing in monocular video are objectively associated to their respective laser sources. Such association is a precursor to 3D surface digitization where each laser plane develops a 3D surface. In this work, the detected laser line segments are modeled as a weighted directed acyclic graph (wDAG), in which the graph nodes represent the line segments and their maximum path hopes from any source node represents the laser source label. However, due to noise and occlusions, the structure of the wDAG will normally vary from that expected in the ideal cases. Furthermore, it is essential to robustness to incorporate the association information of the previous frames in the new frame associations. The proposed work objectively modifies the current frame’s wDAG under these constraints so the resulting wDAG becomes in an appropriate structure for the labeling of the detected line segments. Branch and bound (BnB) optimization has been a good choice for global optimization of combinatorial problems. Rather than approaching the optimization as a pure (or full) combinatorial problem, the proposed prioritizations of the branch modification actions are nearly optimal. The high priority actions will very likely lead to an optimal modification. This aspect is exploited by the proposed sparse variant of BnB (SBnB) optimization. It significantly reduces the number of the branches to be considered, in addition to those contributed by the bounding part of the BnB optimization. The proposed approach was tested on numerous monocular videos and plausible association results were efficiently achieved.

Scaling of roughness and porosity in thin film deposition with mixed transport mechanisms and adsorption barriers.

Thin film deposition with particle transport mixing collimated and diffusive components and with barriers for adsorption are studied using numerical simulations and scaling approaches. Biased random walks on lattices are used to model the particle flux and the analogy with advective-diffusive transport is used to define a Peclet number P that represents the effect of the bias towards the substrate. An aggregation probability that relates the rates of adsorption and of the dominant transport mechanism plays the role of a Damkohler number D, where D≲1 is set to describe moderate to low adsorption rates. Very porous deposits with sparse branches are obtained with P≪1, whereas low porosity deposits with large height fluctuations at short scales are obtained with P≫1. For P≳1 in which the field bias is intense, an initial random deposition is followed by Kardar-Parisi-Zhang (KPZ) roughening. As the transport is displaced from those limiting conditions, the interplay of the transport and adsorption mechanisms establishes a condition to produce films with the smoothest surfaces for a constant deposited mass: with low adsorption barriers, a balance of random and collimated flux is required, whereas for high barriers the smoothest surfaces are obtained with P∼D^{1/2}. For intense bias, the roughness is shown to be a power law of P/D, whose exponent depends on the growth exponent β of the KPZ class, and the porosity has a superuniversal scaling as (P/D)^{-1/3}. We also study a generalized ballistic deposition model with slippery particle aggregation that shows the universality of these relations in growth with dominant collimated flux, particle adsorption at any point of the deposit, and negligible adsorbate diffusion, in contrast with the models where aggregation is restricted to the outer surface.

A case of canine cutaneous pythiosis in Thailand

Introduction Pythium insidiosum causes pythiosis in humans and animals in tropical and subtropical climates. The clinical manifestations in humans are mostly systemic, vascular or ocular forms, in contrast to animals, which are cutaneous, subcutaneous and gastrointestinal forms. The highest incidence of human cases is reported in Thailand, however, no canine pythiosis has been documented yet.Case presentation A female, mixed-breed, stray dog showed severe extensive ulcerative haemorrhagic dermatitis at the perineum involving the anus and tail. On cytology, there were sparse branching septate fungal hyphae. The tissue samples were subjected to polymerase chain reaction and gene sequencing for fungal identification.Conclusion The results of the internal transcribed spacer 1 and 2 (ITS1 and ITS2) gene had 99 % homology to Pythium insidiosum (accession no. FJ17396) and the COX2 gene (accession no. GQ451572). The phylogenetic tree of both genes was classified in clade ATH. This is the first fully documented diagnosis of canine cutaneous pythiosis in Thailand.

Trissopathes (Anthozoa: Antipatharia) in the north-east Atlantic, with a description of T. grasshoffi sp. nov.

A new species of antipatharian (black coral) in the genus Trissopathes is described. It is common in the bathyal zone of the north-east Atlantic, including the Bay of Biscay, Celtic Slope and adjacent banks and seamounts. The species is often observed in underwater photographs from untrawled parts of carbonate mounds in the area. Trissopathes grasshoffi sp. nov. can be easily distinguished from its three congeners by having relatively sparse branching, a higher density of primary pinnules, and 3-6 secondary anterolateral pinnules as well as by the shape and size of its spines. The present state of knowledge of Trissopathes is reviewed.

Cluster-wise learning network for multi-person pose estimation

In this paper, we propose a cluster-wise feature aggregation network that exploits multi-level contextual association for multi-person pose estimation. The recent popular approach for pose estimation is extracting the local maximum response from each detection heatmap that trained for a specific keypoint type. To exploit more contextual information, our network simultaneously learns complementary semantic information to encourage the detected keypoints subject to a certain contextual constraint. Specifically, our network uses dense and sparse branches to generate paired multi-peak detection heatmaps for clusters of keypoints. To enhance the feature passing through the network, we aggregate information from different branches. The in-branch aggregation enriches the detection features in each branch by absorbing the holistic human region attention. The cross-branch aggregation further strengthens the detection features by fusing global and local context information between dense and sparse branches. We demonstrate competitive performance of our network on the benchmark dataset for multi-person pose estimation.

Multiple Patterns of Axonal Collateralization of Single Layer III Neurons of the Rat Presubiculum.

The presubiculum plays a key role in processing and integrating spatial and head-directional information. Layer III neurons of the presubiculum provide strong projections to the superficial layers of the medial entorhinal cortex (MEC) in the rat. Our previous study revealed that the terminal distribution of efferents from layer III cells of the presubiculum was organized in a band-like fashion within the MEC, and the transverse axis of these zones ran parallel to the rhinal fissure. Identifying axonal branching patterns of layer III neurons of the presubiculum is important to further elucidate the functional roles of the presubiculum. In the present study, we visualized all axonal processes and terminal distributions of single presubicular layer III neurons in the rat, using in vivo injection of a viral vector expressing membrane-targeted palmitoylation site-attached green fluorescent protein (GFP). We found that layer III of the rat presubiculum comprised multiple types of neurons (n = 12) with characteristic patterns of axonal collateralization, including cortical projection neurons (n = 6) and several types of intrinsic connectional neurons (n = 6). Two of six cortical projection neurons provided two or three major axonal branches to the MEC and formed elaborate terminal arbors within the superficial layers of the MEC. The width and axis of the area of their terminal distribution resembled that of the band-like terminal field seen in our massive-scale observation. Two of the other four cortical projection neurons gave off axonal branches to the MEC and also to the subiculum, and each of the other two neurons sent axons to the subiculum or parasubiculum. Patterns of axonal arborization of six intrinsic connectional neurons were distinct from each other, with four neurons sending many axonal branches to both superficial and deep layers of the presubiculum and the other two neurons showing sparse axonal branches with terminations confined to layers III–V of the presubiculum. These data demonstrate that layer III of the rat presubiculum consists of multiple types of cortical projection neurons and interneurons, and also suggest that inputs from a single presubicular layer III neuron can directly affect a band-like zone of the MEC.

Laurencia longiramea sp. nov. for Brazil and an emendation of the generic delineation of Corynecladia (Ceramiales, Rhodophyta)

ABSTRACTStudies of the Laurencia complex based on phylogenetic and DNA barcoding analyses revealed a new species, Laurencia longiramea Cassano, G.N. Santos, J.M.C. Nunes, M.C. Oliveira & M.T. Fujii sp. nov., on the Brazilian coast. The rbcL phylogenetic analysis resolved L. longiramea as sister to Laurencia species from the North Atlantic (L. pyramidalis, L. viridis and L. obtusa) and the Caribbean Sea and Gulf of Mexico (L. intricata), with a divergence of 2.4%–3.7%. Using the DNA barcode COI-5P, L. longiramea was closest to L. pacifica from the United States, with a divergence of 4.2%. The new species has the typical features of the genus Laurencia and is characterised by: sparse branching, axes smaller than 1 mm diameter, often long branchlets, lenticular thickenings always present and abundant, and absence of cortical cells projected near branch apices. Morphologically, L. longiramea is similar to Laurencia filiformis. However, an rbcL sequence of L. filiformis from the type locality (Western Australia) confirmed that they are distinct species, with 4.3% divergence. Examination of Laurencia clavata from Brazil, deposited in the University of São Paulo (SPF) herbarium, were misidentified and corresponded to the new species, L. longiramea. In addition, the rbcL and COI-5P sequences of L. clavata from the type locality (Australia) did not join with L. longiramea. Laurencia clavata formed a well-supported clade with the two species of Coronaphycus. Priority supported the recognition of Corynecladia over Coronaphycus, requiring the new combinations Corynecladia elata (C.Agardh) comb. nov. and Corynecladia nova (Metti) comb. nov. Based on our results, Corynecladia clavata does not occur in the Brazilian marine flora.