Subtle Morphological Differences Research Articles

Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model.

In agriculture, especially wheat cultivation, farmers often use multi-variety planting strategies to reduce monoculture-related harvest risks. However, the subtle morphological differences among wheat varieties make accurate discrimination technically challenging. Traditional variety classification methods, reliant on expert knowledge, are inefficient for modern intelligent agricultural management. Numerous existing classification models are computationally complex, memory-intensive, and difficult to deploy on mobile devices effectively. This study introduces G-PPW-VGG11, an innovative lightweight convolutional neural network model, to address these issues. G-PPW-VGG11 ingeniously combines partial convolution (PConv) and partially mixed depthwise separable convolution (PMConv), reducing computational complexity and feature redundancy. Simultaneously, incorporating ECANet, an efficient channel attention mechanism, enables precise leaf information capture and effective background noise suppression. Additionally, G-PPW-VGG11 replaces traditional VGG11's fully connected layers with two pointwise convolutional layers and a global average pooling layer, significantly reducing memory footprint and enhancing nonlinear expressiveness and training efficiency. Rigorous testing showed G-PPW-VGG11's superior performance, with an impressive 93.52% classification accuracy and only 1.79MB memory usage. Compared to VGG11, G-PPW-VGG11 showed a 5.89% increase in accuracy, 35.44% faster inference, and a 99.64% reduction in memory usage. G-PPW-VGG11 also surpasses traditional lightweight networks in classification accuracy and inference speed. Notably, G-PPW-VGG11 was successfully deployed on Android and its performance evaluated in real-world settings. The results showed an 84.67% classification accuracy with an average time of 291.04ms per image. This validates the model's feasibility for practical agricultural wheat variety classification, establishing a foundation for intelligent management. For future research, the trained model and complete dataset are made publicly available.

ALL classification using neural ensemble and memetic deep feature optimization.

Acute lymphoblastic leukemia (ALL) is a fatal blood disorder characterized by the excessive proliferation of immature white blood cells, originating in the bone marrow. An effective prognosis and treatment of ALL calls for its accurate and timely detection. Deep convolutional neural networks (CNNs) have shown promising results in digital pathology. However, they face challenges in classifying different subtypes of leukemia due to their subtle morphological differences. This study proposes an improved pipeline for binary detection and sub-type classification of ALL from blood smear images. At first, a customized, 88 layers deep CNN is proposed and trained using transfer learning along with GoogleNet CNN to create an ensemble of features. Furthermore, this study models the feature selection problem as a combinatorial optimization problem and proposes a memetic version of binary whale optimization algorithm, incorporating Differential Evolution-based local search method to enhance the exploration and exploitation of feature search space. The proposed approach is validated using publicly available standard datasets containing peripheral blood smear images of various classes of ALL. An overall best average accuracy of 99.15% is achieved for binary classification of ALL with an 85% decrease in the feature vector, together with 99% precision and 98.8% sensitivity. For B-ALL sub-type classification, the best accuracy of 98.69% is attained with 98.7% precision and 99.57% specificity. The proposed methodology shows better performance metrics as compared with several existing studies.

Quantitative imaging and semiotic phenotyping of mitochondrial network morphology in live human cells.

The importance of mitochondria in tissue homeostasis, stress responses and human diseases, combined to their ability to transition between various structural and functional states, makes them excellent organelles for monitoring cell health. There is therefore a need for technologies to accurately analyze and quantify changes in mitochondrial organization in a variety of cells and cellular contexts. Here we present an innovative computerized method that enables accurate, multiscale, fast and cost-effective analysis of mitochondrial shape and network architecture from confocal fluorescence images by providing more than thirty features. In order to facilitate interpretation of the quantitative results, we introduced two innovations: the use of Kiviat-graphs (herein named MitoSpider plots) to present highly multidimensional data and visualization of the various mito-cellular configurations in the form of morphospace diagrams (called MitoSigils). We tested our fully automated image analysis tool on rich datasets gathered from live normal human skin cells cultured under basal conditions or exposed to specific stress including UVB irradiation and pesticide exposure. We demonstrated the ability of our proprietary software (named MitoTouch) to sensitively discriminate between control and stressed dermal fibroblasts, and between normal fibroblasts and other cell types (including cancer tissue-derived fibroblasts and primary keratinocytes), showing that our automated analysis captures subtle differences in morphology. Based on this novel algorithm, we report the identification of a protective natural ingredient that mitigates the deleterious impact of hydrogen peroxide (H2O2) on mitochondrial organization. Hence we conceived a novel wet-plus-dry pipeline combining cell cultures, quantitative imaging and semiotic analysis for exhaustive analysis of mitochondrial morphology in living adherent cells. Our tool has potential for broader applications in other research areas such as cell biology and medicine, high-throughput drug screening as well as predictive and environmental toxicology.

Abstract 2305: Label-free high dimensional single cell morphological profiling of different hematological malignancies by ghost cytometry

Abstract Characterizing the heterogeneity in human leukemias is critical for understanding mechanisms of disease onset, uncovering biomarkers for disease diagnosis, and guiding research into novel treatment approaches. While extensive research focus has been placed on characterizing the molecular heterogeneity in leukemia by modern omics technologies, comparatively less emphasis has been placed on studying the diversity in holistic morphological changes that occur in single cells in the context of disease. Here, we report on a novel approach to understanding and characterizing different hematological malignancies using ghost cytometry, a recently developed high-content flow cytometric method that leverages high-speed, artificial intelligence (AI) driven morphological characterization and analysis of single cells. We performed morphometric profiling on human bone marrow samples from patients with T and B-cell acute lymphoblastic leukemia (ALL, n=5), acute myeloid leukemia (AML, n=5), multiple myeloma (MM, n=5) and healthy bone marrow mononuclear cell (BM-MNC) controls (n=6). Morphological profiles from individual cells were analyzed by unsupervised machine learning (universal manifold approximation and projection, UMAP) and compared within disease subsets and against controls. We identified disease-specific cell populations from a mixture of healthy control and disease samples without any labeling, based on completely label-free information. For validation of the specific cell subsets, we overlaid known cell surface markers (e.g. CD45 for blast cells, CD10/CD19 for B-ALL, CD33/CD34 for AML, CD38/CD138 for MM). The intensity of CD45 was dimmer and disease-specific markers were highly expressed in the disease-specific cell population. We also identified a unique cell subpopulation in the disease-specific cell population that could not be distinguished by existing markers. We also analyzed three human myeloma cell lines (AMO1, KMS, and L363) for comparison to the primary MM samples. In the UMAP analysis using LF-GC data, in all three types of cell lines, Bortezomib-resistant cell lines exhibited different distributions from the control parental cell lines. In addition, we observed distinct morphological profiles between primary myeloma samples and myeloma cell lines. Here we present a novel cell characterization approach for human hematological diseases that leverages AI-based, label-free, high-speed morphological characterization of single cells. We demonstrate that the approach can be used to identify subtle morphological differences in blast cells from a range of blood cancer subtypes. Application of morphological profiling and AI can be used to identify measurable, disease-related changes in these diseases that have diagnostic, drug screening, and therapeutic monitoring potential. Citation Format: Asako Tsubouchi, Juho J. Miettinen, Keisuke Wagatsuma, Satoru Akai, Yuri An, Mika Uematsu, Philipp Sergeev, Dimitrios Tsallos, Markus J. Vähä-Koskela, Sadao Ota, Caroline A. Heckman. Label-free high dimensional single cell morphological profiling of different hematological malignancies by ghost cytometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2305.

Field-based measurement tools to distinguish clonal Typha taxa and estimate biomass: a resource for conservation and restoration.

Two species of clonal Typha [T. latifolia (native) and T. angustifolia (exotic)] hybridize to form the highly invasive, heterotic (high vigor) T. × glauca in North American wetlands leading to increased primary production, litter accumulation, and biodiversity loss. Conservation of T. latifolia has become critical as invasive Typha has overwhelmed wetlands. In the field, Typha taxa identification is difficult due to subtle differences in morphology, and molecular identification is often unfeasible for managers. Furthermore, improved methods to non-destructively estimate Typha biomass is imperative to enhance ecological impact assessments. To address field-based Typha ID limitations, our study developed a predictive model from 14 Typha characters in 7 northern Michigan wetlands to accurately distinguish Typha taxa (n = 33) via linear discriminant analysis (LDA) of molecularly identified specimens. In addition, our study developed a partial least squares regression (PLS) model to predict Typha biomass from field collected measurements (n = 75). Results indicate that two field measurements [Leaf Counts, Longest Leaf] can accurately differentiate the three Typha taxa and advanced-generation hybrids. The LDA model had a 100% correct prediction rate of T. latifolia. The selected PLS biomass prediction model (sqrt[Typha Dry Mass] ~ log[Ramet Area at 30 cm] + Inflorescence Presence + Total Ramet Height + sqrt[Organic Matter Depth]) improved upon existing simple linear regression (SLR) height-to-biomass predictions. The rapid field-based Typha identification and biomass assessment tools presented in this study advance targeted management for regional conservation of T. latifolia and ecological restoration of wetlands impacted by invasive Typha taxa.

Molecular Diversity of the Genus Plagiorchis Lühe, 1899 in Snail Hosts of Central Europe with Evidence of New Lineages

Cercariae of Plagiorchis spp. are frequently reported in European freshwater snails, but their true diversity is difficult to estimate due to subtle differences in morphology. We molecularly characterized 67 isolates of Plagiorchis cercariae collected from four lymnaeid snail hosts, Ampullaceana balthica, Ampullaceana lagotis, Radix auricularia and Lymnaea stagnalis in freshwater ecosystems in the Czech Republic and Poland. Based on mitochondrial cox1 and nuclear 28S sequences, ten species or species-level lineages were identified, including the first molecular evidence of P. vespertilionis from snail hosts and two species-level lineages reported for the first time. Previously undescribed species and species-level lineages are characterized morphometrically. We confirm the overlapping spatial distribution of Plagiorchis spp. in their snail hosts from Central Europe with those from Western and sub-Arctic Europe. Our results increase the known diversity of Plagiorchis spp. in Europe to 25 species/lineages in snails, but further research is needed to establish links between life cycle stages and to assess the host specificity of these parasites.

Ecological Speciation without Morphological Differentiation? A New Cryptic Species of Diodontus Curtis (Hymenoptera, Pemphredonidae) from the Centre of Europe.

Upon exploring the mitotype diversity of the aphid-hunting wasp, Diodontus tristis, we revealed specimens with highly divergent mitotypes from two localities in Lithuania and nesting in clayey substrate, while the specimens with typical mitotypes were found nesting in sandy sites. The comparison of inter- and intra-specific distances and application of delimitation algorithms supported the species status of the clay-nesting populations. Using a set of DNA markers that included complete or partial sequences of six mitochondrial genes, three markers of ribosomal operon, two homeobox genes, and four other nuclear genes, we clarified the phylogenetic relationships of the new cryptic species. The endosymbiotic bacteria infestation was checked, considering the option that the divergent populations may represent clades isolated by Wolbachia infection; however, it did not demonstrate any specificity. We found only subtle morphological differences in the new clay-nesting species, D. argillicola sp. nov.; the discriminant analysis of morphometric measurements did not reliably segregate it as well. Thus, we provide the molecular characters of the cryptic species, which allow confident identification, its phylogenetic position within the genus, and an updated identification key for the D. tristis species group.

Wide-spread dispersal in a deep-sea brooding polychaete: the role of natural history collections in assessing the distribution in quill worms (Onuphidae, Annelida)

BackgroundModern integrative taxonomy-based annelid species descriptions are detailed combining morphological data and, since the last decades, also molecular information. Historic species descriptions are often comparatively brief lacking such detail. Adoptions of species names from western literature in the past led to the assumption of cosmopolitan ranges for many species, which, in many cases, were later found to include cryptic or pseudocryptic lineages with subtle morphological differences. Natural history collections and databases can aid in assessing the geographic ranges of species but depend on correct species identification. Obtaining DNA sequence information from wet-collection museum specimens of marine annelids is often impeded by the use of formaldehyde and/or long-term storage in ethanol resulting in DNA degradation and cross-linking.ResultsThe application of ancient DNA extraction methodology in combination with single-stranded DNA library preparation and target gene capture resulted in successful sequencing of a 110-year-old collection specimen of quill worms. Furthermore, a 40-year-old specimen of quill worms was successfully sequenced using a standard extraction protocol for modern samples, PCR and Sanger sequencing. Our study presents the first molecular analysis of Hyalinoecia species including the previously known species Hyalinoecia robusta, H. tubicloa, H. artifex, and H. longibranchiata, and a potentially undescribed species from equatorial western Africa morphologically indistinguishable from H. tubicola. The study also investigates the distribution of these five Hyalinoecia species. Reassessing the distribution of H. robusta reveals a geographical range covering both the Atlantic and the Indian Oceans as indicated by molecular data obtained from recent and historical specimens.ConclusionOur results represent an example of a very wide geographical distribution of a brooding deep-sea annelid with a complex reproduction strategy and seemingly very limited dispersal capacity of its offspring, and highlights the importance of molecular information from museum specimens for integrative annelid taxonomy and biogeography.

Application of unimodal probability distribution models for morphological phenotyping of budding yeast.

Morphological phenotyping of the budding yeast Saccharomyces cerevisiae has helped to greatly clarify the functions of genes and increase our understanding of cellular functional networks. It is necessary to understand cell morphology and perform quantitative morphological analysis (QMA) but assigning precise values to morphological phenotypes has been challenging. We recently developed the Unimodal Morphological Data image analysis pipeline for this purpose. All true values can be estimated theoretically by applying an appropriate probability distribution if the distribution of experimental values follows a unimodal pattern. This reliable pipeline allows several downstream analyses, including detection of subtle morphological differences, selection of mutant strains with similar morphology, clustering based on morphology, and study of morphological diversity. In addition to basic research, morphological analyses of yeast cells can also be used in applied research to monitor breeding and fermentation processes and control the fermentation activity of yeast cells.

Phylogenetic and population genetic analyses of Thrips tabaci Lindeman (Thysanoptera: Thripidae) on Allium host in India.

Onion thrips (Thrips tabaci) is a complex of cryptic species with subtle morphological differences and distinct genetic backgrounds; thus, species identification using traditional methods remains challenging. The existence of different haplotypes and genotypes within a species can significantly influence various aspects of its biology, including host preference, reproductive capacity, resistance to pesticides, and vector competence for plant viruses. Understanding the genetic diversity and population structure of cryptic species within T. tabaci will not only aid in the development of more effective control strategies tailored to specific genetic variants but also in monitoring population dynamics, tracking invasive species, and implementing quarantine measures to prevent the spread of economically damaging thrips biotypes. This study aims to explore intraspecies genetic diversity and molecular evolutionary relationships of the mitochondrial cytochrome oxidase gene subunit I (mtCOI) in T. tabaci populations from India. To capture diversity within the Indian T. tabaci populations, amplicon sequencing was performed for the thrips mtCOI gene from eight diverse localities in India. A total of 48 sequences retrieved for the mtCOI gene from the NCBI Nucleotide database were analysed. Multiple insertions and deletions were detected at various genomic positions across the populations from different localities, with the highest variation observed in the 300-400 genome position range. Molecular diversity analyses identified 30 haplotypes within the population, with certain subpopulations exhibiting higher gene flow. Analysis of single nucleotide polymorphism patterns within the mtCOI gene across diverse Indian locales revealed significant intrapopulation genetic heterogeneity and its potential repercussions on gene functionality. Elevated F statistics (Fst) values in the northern-western subpopulations suggested high genetic variability, particularly evident in haplotype networks originating mainly from the northern region, notably Delhi. While most populations displayed stable and ancient evolutionary histories, thrips populations from northern, western, and north-eastern regions indicated rapid growth.

Contrasting patterns of genetic and phenotypic divergence of two sympatric congeners, Phragmites australis and P. hirsuta, in heterogeneous habitats.

Habitat heterogeneity leads to genome-wide differentiation and morphological and ecological differentiation, which will progress along the speciation continuum, eventually leading to speciation. Phragmites hirsuta and Phragmites australis are sympatric congeners that coexist in saline-alkaline meadow soil (SAS) and sandy soil (SS) habitats of the Songnen Meadow. The results provided genetic evidence for two separate species of reeds. Genetic diversity and spatial genetic structure supported the specialist-generalist variation hypothesis (SGVH) in these two sympatric reed species, suggesting that P. australis is a generalist and P. hirsuta is a habitat specialist. When we compared these different species with respect to phenotypic and genetic variation patterns in different habitats, we found that the phenotypic differentiation of P. australis between the two habitats was higher than that of P. hirsuta. Multiple subtle differences in morphology, genetic background, and habitat use collectively contribute to ecological success for similar congeners. This study provided evidence of the two reed congeners, which should contribute to their success in harsh environments.

Label-Free High Dimensional Single Cell Morphological Profiling of Different Hematological Malignancies By Ghost Cytometry

Characterizing the heterogeneity in human leukemias is critical for understanding mechanisms of disease onset, uncovering biomarkers for disease diagnosis, and guiding research into novel treatment approaches. While extensive research focus has been placed on characterizing the molecular heterogeneity in leukemia by modern 'omics technologies, comparatively less emphasis has been placed on studying the diversity in holistic morphological changes that occur in single cells in the context of disease. Here, we report on a novel approach to understanding and characterizing different hematological malignancies using ghost cytometry, a recently developed high-content flow cytometric method that leverages high-speed, artificial intelligence (AI) driven morphological characterization and analysis of single cells. We performed morphometric profiling on human bone marrow samples from patients with T and B-cell acute lymphoblastic leukemia (ALL, n=5), acute myeloid leukemia (AML, n=5), multiple myeloma (MM, n=5) and healthy bone marrow mononuclear cell (BM-MNC) controls (n=6) (Figure 1). Morphological profiles from individual cells were analyzed by unsupervised machine learning (universal manifold approximation and projection, UMAP) and compared within disease subsets and against controls. We identified disease-specific cell populations from a mixture of healthy control and disease samples without any labeling, based on completely label-free information (Figure 2). For validation of the specific cell subsets, we overlaid known cell surface markers (e.g. CD45 for blast cells, CD10/CD19 for B-ALL, CD33/CD34 for AML, CD38/CD138 for MM). The intensity of CD45 was dimmer and disease-specific markers were highly expressed in the disease-specific cell population. We also identified unique cell populations across blood cell populations that were distinct from the healthy BM-MNC controls. We also analyzed three human myeloma cell lines (AMO1, KMS, and L363) for comparison to the primary MM samples. In the UMAP analysis using LF-GC data, in all three types of cell lines, Bortezomib-resistant cell lines exhibited different distributions from the control parental cell lines. In addition, we observed distinct morphological profiles between primary myeloma samples and myeloma cell lines. Here we present a novel cell characterization approach for human hematological diseases that leverages AI-based, label-free, high-speed morphological characterization of single cells. We demonstrate that the approach can be used to identify subtle morphological differences in blast cells from a range of blood cancer subtypes. Application of morphological profiling and AI can be used to identify measurable, disease-related changes in these diseases that have diagnostic, drug screening, and therapeutic monitoring potential. Keywords: flow cytometry, acute leukemia, multiple myeloma, label-free cell analysis, biomarker, liquid biopsy, ghost cytometry, artificial intelligence, machine learning Figure 1. Research design overview. Bone marrow samples from patients and non-diseased controls will be analyzed by Ghost Cytometry to assess disease by conventional markers and identify novel morphometric subpopulations. Morphological information is acquired as label-free GMI signals without any labeling. The label-free GMI information is transformed into two-dimensional space by UMAP dimensionality reduction method to identify subpopulations of interest. Both samples are stained with known cell surface markers for validation of unsupervised classification. Figure 2. Identification of disease-specific cell subpopulations by unsupervised approach. Using an unsupervised machine learning technique called UMAP, label-free morphologically distinct cellular subpopulations can be identified in B-ALL, AML, MM when compared to healthy donor controls (left panels, inside the dotted circles). For validation of the subpopulations, known cell surface markers (CD45 for blast cells, CD10/CD19 for B-ALL, CD33/CD34 for AML, CD38/CD138 for MM) and cell trace marker (CellTracker) were overlaid. The intensity increases monotonically through the colormaps. The intensity of CD45 was dimmer and disease-specific markers were highly expressed in the disease-specific cell population.

Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer

Breast cancer prognosis and management for both men and women are reliant upon estrogen receptor alpha (ERα) and progesterone receptor (PR) expression to inform therapy. Previous studies have shown that there are sex-specific binding characteristics of ERα and PR in breast cancer and, counterintuitively, ERα expression is more common in male than female breast cancer. We hypothesized that these differences could have morphological manifestations that are undetectable to human observers but could be elucidated computationally. To investigate this, we trained attention-based multiple instance learning prediction models for ERα and PR using H&E-stained images of female breast cancer from the Cancer Genome Atlas (TCGA) (n = 1085) and deployed them on external female (n = 192) and male breast cancer images (n = 245). Both targets were predicted in the internal (AUROC for ERα prediction: 0.86 ± 0.02, p < 0.001; AUROC for PR prediction = 0.76 ± 0.03, p < 0.001) and external female cohorts (AUROC for ERα prediction: 0.78 ± 0.03, p < 0.001; AUROC for PR prediction = 0.80 ± 0.04, p < 0.001) but not the male cohort (AUROC for ERα prediction: 0.66 ± 0.14, p = 0.43; AUROC for PR prediction = 0.63 ± 0.04, p = 0.05). This suggests that subtle morphological differences invisible upon visual inspection may exist between the sexes, supporting previous immunohistochemical, genomic, and transcriptomic analyses.

Neo-Natal Castration Leads to Subtle Differences in Porcine Anterior Cruciate Ligament Morphology and Function in Adolescence.

Female adolescent athletes are at a higher risk of tearing their anterior cruciate ligament (ACL) than male counterparts. While most work related to hormones has focused on the effects of estrogen to understand the increased risk of ACL injury, there are other understudied factors, including testosterone. The purpose of this study was to determine how surgical castration in the male porcine model influences ACL size and function across skeletal growth. Thirty-six male Yorkshire crossbreed pigs were raised to 3 (juvenile), 4.5 (early adolescent), and 6 months (adolescent) of age. Animals were either castrated (barrows) within 2 weeks after birth or were left intact (boars). Posteuthanasia, joint and ACL size were assessed via MRI, and biomechanics were assessed via a robotic testing system. Joint size increased throughout age, yet barrows had smaller joints than boars. ACL cross-sectional area (CSA), length, volume, and in situ stiffness increased with age, as did the percent contribution of the ACL anteromedial (AM) bundle to resisting loads. Boar ACL, AM bundle, and PL bundle volumes were 19%, 25%, and 15% larger than barrows across ages. However, ACL CSA, in situ stiffness, and bundle contribution were similar between boars and barrows. The barrows had smaller temporal increases in AM bundle function than boars, but these data were highly variable. Early and sustained loss in testosterone leads to subtle differences in ACL morphology but may not influence measures associated with increased injury risk, such as CSA or bundle forces in response to applied loads.

Morphogenesis in Trypanosoma cruzi epimastigotes proceeds via a highly asymmetric cell division.

Trypanosoma cruzi is a protist parasite that is the causative agent of Chagas disease, a neglected tropical disease endemic to the Americas. T. cruzi cells are highly polarized and undergo morphological changes as they cycle within their insect and mammalian hosts. Work on related trypanosomatids has described cell division mechanisms in several life-cycle stages and identified a set of essential morphogenic proteins that serve as markers for key events during trypanosomatid division. Here, we use Cas9-based tagging of morphogenic genes, live-cell imaging, and expansion microscopy to study the cell division mechanism of the insect-resident epimastigote form of T. cruzi, which represents an understudied trypanosomatid morphotype. We find that T. cruzi epimastigote cell division is highly asymmetric, producing one daughter cell that is significantly smaller than the other. Daughter cell division rates differ by 4.9 h, which may be a consequence of this size disparity. Many of the morphogenic proteins identified in T. brucei have altered localization patterns in T. cruzi epimastigotes, which may reflect fundamental differences in the cell division mechanism of this life cycle stage, which widens and shortens the cell body to accommodate the duplicated organelles and cleavage furrow rather than elongating the cell body along the long axis of the cell, as is the case in life-cycle stages that have been studied in T. brucei. This work provides a foundation for further investigations of T. cruzi cell division and shows that subtle differences in trypanosomatid cell morphology can alter how these parasites divide.

ESCARGOT: Mapping Vertical Phase Spiral Characteristics Throughout the Real and Simulated Milky Way

The recent discovery of a spiral pattern in the vertical kinematic structure in the solar neighborhood provides a prime opportunity to study nonequilibrium dynamics in the Milky Way from local stellar kinematics. Furthermore, results from simulations indicate that even in a limited volume, differences in stellar orbital histories allow us to trace variations in the initial perturbation across large regions of the disk. We present ESCARGOT, a novel algorithm for studying these variations in both simulated and observed data sets. ESCARGOT automatically extracts key quantities from the structure of a given phase spiral, including the time since perturbation and the perturbation mode. We test ESCARGOT on simulated data and show that it is capable of accurately recovering information about the time since the perturbation occurred as well as subtle differences in phase spiral morphology due to stellar locations in the disk at the time of perturbation. We apply ESCARGOT to kinematic data from data release 3 of the Gaia mission in bins of guiding radius. We show that similar structural differences in morphology occur in the Gaia phase spirals as a function of stellar orbital history. These results indicate that the phase spirals are the product of a complex dynamical response in the disk with large-scale coupling between different regions of phase space.

Sex-based impact of pancreatic islet stressors in GluCreERT2/Rosa26-eYFP mice.

The present study examines differences in metabolic and pancreatic islet adaptative responses following streptozotocin (STZ) and hydrocortisone (HC) administration in male and female transgenic GluCreERT2/Rosa26-eYFP mice. Mice received five daily doses of STZ (50 mg/kg, i.p.) or 10 daily doses of HC (70 mg/kg, i.p.), with parameters assessed on day 11. STZ-induced hyperglycaemia was evident in both sexes, alongside impaired glucose tolerance and reduced insulin concentrations. HC also had similar metabolic effects in male and female mice resulting in classical increases of circulating insulin indicative of insulin resistance. Control male mice had larger pancreatic islets than females and displayed a greater reduction of islet and beta-cell area in response to STZ insult. In addition, female STZ mice had lower levels of beta-cell apoptosis than male counterparts. Following HC administration, female mouse islets contained a greater proportion of alpha cells when compared to males. All HC mice presented with relatively comparable increases in beta- and alpha-cell turnover rates, with female mice being slightly more susceptible to HC-induced beta-cell apoptosis. Interestingly, healthy control female mice had inherently increased alpha-to-beta-cell transdifferentiation rates, which was decreased by HC treatment. The number of glucagon-positive alpha cells altering their lineage to insulin-positive beta cells was increased in male, but not female, STZ mice. Taken together, although there was no obvious sex-specific alteration of metabolic profile in STZ or HC mice, subtle differences in pancreatic islet morphology emphasises the impact of sex hormones on islets and importance of taking care when interpreting observations between males and females.

The post-2016 long-lasting Vulcanian activity of Sabancaya volcano (Peru) and associated aeolian remobilisation of volcanic ash

The characterisation of tephra deposits resulting from almost simultaneous sedimentation and wind remobilisation is complex, and multidisciplinary strategies are required in order to accurately constrain associated processes and eruptive parameters. We present a multifaceted study that aims to characterise the recent eruptive activity and the subsequent aeolian remobilisation of tephra deposits at Sabancaya volcano (Peru), which started erupting in November 2016 with frequent and relatively small explosions (plume heights <6 km above the vent). First, we estimated the bulk volume of tephra deposit produced between November 2016 and August 2018 at 0.04 ± 0.02 km3, and the dense rock equivalent (DRE) volume at 0.02 ± 0.01 km3. This corresponds to a tephra production rate of 1.1 ± 0.5 × 10−3 km3 DRE per month. Second, continuous sampling in a dedicated tephra collector network between April 2018 and November 2019 allowed estimation of the tephra volume at 2.3 ± 1.1 × 10−5 km3 DRE per month, indicating a significant decrease in the mass eruption rate since 2018. Third, by characterising the pulsatory activity through the repose interval between explosions and magma characteristics, Sabancaya's activity was classified as Vulcanian. Finally, aeolian remobilisation phenomena were studied using high-resolution videos, measurements of the airborne concentration of particulate matter with a diameter of ≤10 μm (PM10) and particle physical characterisation. Subtle morphological differences are identified between remobilised particles and those in primary deposits, and we found that particles moving at ground level and above 1.5 m have grainsizes transportable by saltation and suspension, respectively.

Stamen and pollen heteromorphism linked to the division of labour in Melastomataceae species

The occurrence of stamen heteromorphism within pollen flowers (or heteranthery) has been mostly explained by a division of labour hypothesis, which suggests that some anthers produce pollen for pollination (pollinating anthers) while others are specialized in attracting and rewarding floral visitors (feeding anthers). Despite more than 20.000 species presenting stamen heteromorphism, the morphological and functional traits associated with it have been poorly investigated, especially at the pollen level. Here we evaluated the structural and possible functional roles of stamen and pollen heteromorphism in 16 Melastomataceae species belonging to four tribes. We investigated how a number of traits associated with floral heteromorphisms - anther size, pollen quantity and quality, pollen volume and exine ornamentation - might interact with pollination efficacy and pollinator feeding behaviour. Our results showed morphological differences between anthers and pollen grains that might affect pollinating and feeding functions in the studied species. Pollinating anthers were larger and produced more numerous and more viable pollen grains than feeding anthers. Pollen grain volume between the anther sets was mostly similar in the analysed species, with the exception of three species. Three species of tribe Microlicieae and one from the Marcetieae presented differences in pollen exine ornamentation between pollinating and feeding anthers. Overall, our findings support the division of labour hypothesis for some, but not all, species included in our study. We showed that stamen heteromorphism on the 16 studied species may lead to subtle and specific differences in anthers and pollen morphology which may contribute to their functional role.

Evidence using morphology, molecules, and biogeography clarifies the taxonomic status of mole crabs of the genus Emerita Scopoli, 1777 (Anomura, Hippidae) and reveals a new species from the western Atlantic.

Uncertainties regarding the taxonomic status and biogeographical distribution of some species of the genus Emerita from the western Atlantic led to thorough examination of the subtle morphological differences between two coexistent species (E.brasiliensis Schmitt, 1935 and E.portoricensis Schmitt, 1935) along the Brazilian coast and compare them using two genetic markers. The molecular phylogenetic analysis based on sequences of the 16S rRNA and COI genes showed that individuals identified as E.portoricensis were clustered into two clades: one containing representatives from the Brazilian coast and another containing specimens distributed in Central America. Our molecular-based phylogeny, combined with a detailed morphological analysis, revealed the Brazilian population as a new species, which is described here as Emeritaalmeidai Mantelatto & Balbino, sp. nov. The number of species in the genus Emerita is now raised to 12, with five of them occurring in the western Atlantic, five in the Indo-Pacific, and two in the eastern Pacific.