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Рис. 4. Фотографии Bolbolaimus brevis sp. nov., самец (А, В, Г, Е, З, И, К) и самка (Б, Δ, Ж, Α). А, Б — общий виΑ; В, Г, Δ — гоΛова; Е — переΑний конец теΛа; Ж — теΛо в обΛасти вуΛьвы; З, И — теΛо в обΛасти кΛоаки; К, Α — хвост. Масштаб: А, Б — 50 мкм; Е, Ж, К, Α — 10 мкм; В, Г, Δ, З, И — 5 мкм Fig. 4. Light micrograph of Bolbolaimus brevis sp. nov., males (А, В, Г, Е, З, И, К) and female (Б, Δ, Ж, Α). А, Б – general view; В, Г, Δ – head; Е – anterior body end; Ж – vulva region; З, И – cloaca region; К, Α – tail. Scale bars: А, Б – 50 µm; Е, Ж, К, Α – 10 µm; В, Г, Δ, З, И – 5 µm in Description Of Two New Nematoda Species Of The Genus Cobb, 1920 (Nematoda, Desmodorida) From Littoral Of Soutch China Sea At Coast Of Vietnam
Рис. 4. Фотографии Bolbolaimus brevis sp. nov., самец (А, В, Г, Е, З, И, К) и самка (Б, Δ, Ж, Α). А, Б — общий виΑ; В, Г, Δ — гоΛова; Е — переΑний конец теΛа; Ж — теΛо в обΛасти вуΛьвы; З, И — теΛо в обΛасти кΛоаки; К, Α — хвост. Масштаб: А, Б — 50 мкм; Е, Ж, К, Α — 10 мкм; В, Г, Δ, З, И — 5 мкм Fig. 4. Light micrograph of Bolbolaimus brevis sp. nov., males (А, В, Г, Е, З, И, К) and female (Б, Δ, Ж, Α). А, Б – general view; В, Г, Δ – head; Е – anterior body end; Ж – vulva region; З, И – cloaca region; К, Α – tail. Scale bars: А, Б – 50 µm; Е, Ж, К, Α – 10 µm; В, Г, Δ, З, И – 5 µm
Рис. 4. Микроскульптура наружной поверхности глохидиальных створок перловиц Nodularia biwae (A, D – увеличенный фрагмент) и Lanceolaria grayana (B, C – увеличенный фрагмент) иЗ Японии, о-в Хонсю. СканируюЩаЯ ЭлектроннаЯ микроскопиЯ. МасШтаб 1 мкм (А, В) и 2 мкм (C, D). Fig. 4. Microsculpture of external surface of glochidia of mussels Nodularia biwae (A, D – fragment) and Lanceolaria grayana (B, C – fragment) from Honshu Is., Japan. Scanning electron microscopy. Scale bar 1µm (А, В) and 2 µm (C, D). in Morphology of glochidia of the freshwater mussels Nodularia amurensis and Middendorffinaia sujfunensis (Bivalvia: Unionidae: Nodulariinae) from the Russian Far East
Рис. 4. Микроскульптура наружной поверхности глохидиальных створок перловиц Nodularia biwae (A, D – увеличенный фрагмент) и Lanceolaria grayana (B, C – увеличенный фрагмент) иЗ Японии, о-в Хонсю. СканируюЩаЯ ЭлектроннаЯ микроскопиЯ. МасШтаб 1 мкм (А, В) и 2 мкм (C, D). Fig. 4. Microsculpture of external surface of glochidia of mussels Nodularia biwae (A, D – fragment) and Lanceolaria grayana (B, C – fragment) from Honshu Is., Japan. Scanning electron microscopy. Scale bar 1µm (А, В) and 2 µm (C, D).
Curved crease origami at cellular scales enables hyper-extensibility of Lacrymaria olor
<p>Eukaryotic cells undergo dramatic morphological changes during cell division, phagocytosis, and motility. Fundamental limits of cellular morphodynamics such as how fast or how much cellular shapes can change without harm to a living cell remain poorly understood. Here we describe hyper-extensibility in the single-celled protist <em>Lacrymaria olor</em>, a 40 μm cell which is capable of reversible and repeatable extensions (neck-like protrusions) up to 1500 μm in 20 seconds. We discover that a unique and intricate organization of cortical cytoskeleton and membrane enables these hyper-extensions that can be described as the first cellular scale curved crease origami. Furthermore, we show how these topological singularities including dcones and twisted domain walls provide a geometrical control mechanism for the deployment of membrane and microtubule sheets as they repeatably spool thousands of times from the cell body. We lastly build physical origami models to understand how these topological singularities provide a mechanism for the cell to control the hyper-extensile deployable structure. This new geometrical motif where a cell employs curved crease origami to perform a physiological function has wide-ranging implications in understanding cellular morphodynamics and direct applications in deployable micro-robotics.</p>
Dataset for an article "Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization"
<p><span>The dataset contains data from compression mechanical testing of 6 basic truss-based lattice cells with relative density between 0.3 and 0.7 in two directions (parallel and perpendicular to build direction). Additionally, the dataset contains simulation of the experiments by finite element method. Due to a big difference between results from experiment and simulation with nominal material model, parametric material model with Young's modulus set as parameter was used. Resulting Young's moduli that correspond with the experiments are also included. Details can be found in the article: “Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization”.</span></p>
De Jong Gierveld Loneliness Scale: validity, reliability and fairness in Peruvian adults
<p><span><strong><span>Abstract:</span></strong></span></p> <p><strong><span>Background: </span></strong><span>Loneliness, transient or long-lasting, constitutes one of the mental health problems of great public impact with peculiar characteristics. It can be defined as a subjective and unpleasant experience, in turn associated with other serious psychological symptoms. This variable in question has not been addressed in a timely manner, among other reasons due to the scarcity of instruments for specific populations. Although the De Jong Gierveld Loneliness Scale (</span><span>DJGLS), <span>based on Weiss' multidimensional model, has been adapted and validated in different contexts, it is still insufficient in Peru. Precisely, the objective was to determine the psychometric properties of the DJGLS, </span></span><span>its internal structure and factorial invariance.</span></p> <p><strong><span>Methods:</span></strong><span> An online survey of 1248 Peruvians between 18 and 70 years of age (M= 27.37, SD= 11.29) from all regions was used. The validation of the DJGLS was analyzed with Exploratory Factor Analysis </span><span>(EFA), <span>Confirmatory Factor </span>Analysis (CFA), <span>convergent validity, measurement invariance and internal consistency reliability.</span></span></p> <p><strong><span>Results: </span></strong><span>Psychometric properties were found with adequate values in its internal structure by means of the CFA, where it was found that the components of the scale are interrelated and the data matrix is factorizable. Here we present a model of two specific factors and one general factor, which is consistent with theory and has practical utility, revealing acceptable reliability values and invariance between sexes.</span></p> <p><strong><span>Conclusions:</span></strong><span> Adequate psychometric properties, which allow for a better data collection process in further related research, are revealed.</span></p> <p><span> </span></p> <p><strong><span>Keywords</span></strong><span>: loneliness; validity; reliability; fairness; adults.</span></p>
CompanyKG Dataset V2.0: A Large-Scale Heterogeneous Graph for Company Similarity Quantification
<p><strong>CompanyKG</strong> is a heterogeneous graph consisting of 1,169,931 nodes and 50,815,503 undirected edges, with each node representing a real-world company and each edge signifying a relationship between the connected pair of companies.</p> <p><strong>Edges</strong>: We model 15 different inter-company relations as undirected edges, each of which corresponds to a unique edge type. These edge types capture various forms of similarity between connected company pairs. Associated with each edge of a certain type, we calculate a real-numbered weight as an approximation of the similarity level of that type. It is important to note that the constructed edges do not represent an exhaustive list of all possible edges due to incomplete information. Consequently, this leads to a sparse and occasionally skewed distribution of edges for individual relation/edge types. Such characteristics pose additional challenges for downstream learning tasks. Please refer to our paper for a detailed definition of edge types and weight calculations.</p> <p><strong>Nodes</strong>: The graph includes all companies connected by edges defined previously. Each node represents a company and is associated with a descriptive text, such as "<em>Klarna is a fintech company that provides support for direct and post-purchase payments</em> ...". To comply with privacy and confidentiality requirements, we encoded the text into numerical embeddings using four different pre-trained text embedding models: <a href="https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2">mSBERT</a> (multilingual Sentence BERT), <a href="https://platform.openai.com/docs/guides/embeddings/what-are-embeddings">ADA2</a>, <a href="https://github.com/princeton-nlp/SimCSE">SimCSE</a> (fine-tuned on the raw company descriptions) and <a href="https://github.com/EQTPartners/pause">PAUSE</a>.</p> <p><strong>Evaluation Tasks</strong>. The primary goal of CompanyKG is to develop algorithms and models for quantifying the similarity between pairs of companies. In order to evaluate the effectiveness of these methods, we have carefully curated three evaluation tasks:</p> <ul> <li><strong>Similarity Prediction (SP)</strong>. To assess the accuracy of pairwise company similarity, we constructed the SP evaluation set comprising 3,219 pairs of companies that are labeled either as positive (similar, denoted by "1") or negative (dissimilar, denoted by "0"). Of these pairs, 1,522 are positive and 1,697 are negative.</li> <li><strong>Competitor Retrieval (CR)</strong>. Each sample contains one <em>target company</em> and one of its direct competitors. It contains 76 distinct target companies, each of which has 5.3 competitors annotated in average. For a given target company A with <em>N</em> direct competitors in this CR evaluation set, we expect a competent method to retrieve all <em>N</em> competitors when searching for similar companies to A. </li> <li><strong>Similarity Ranking (SR)</strong> is designed to assess the ability of any method to rank <em>candidate companies</em> (numbered 0 and 1) based on their similarity to a <em>query company</em>. Paid human annotators, with backgrounds in engineering, science, and investment, were tasked with determining which candidate company is more similar to the query company. It resulted in an evaluation set comprising 1,856 rigorously labeled ranking questions. We retained 20% (368 samples) of this set as a validation set for model development. </li> <li><strong>Edge Prediction (EP)</strong> evaluates a model's ability to predict future or missing relationships between companies, providing forward-looking insights for investment professionals. The EP dataset, derived (and sampled) from new edges collected between April 6, 2023, and May 25, 2024, includes 40,000 samples, with edges not present in the pre-existing CompanyKG (a snapshot up until April 5, 2023).</li> </ul> <p><strong>Background and Motivation</strong></p> <p>In the investment industry, it is often essential to identify similar companies for a variety of purposes, such as market/competitor mapping and Mergers & Acquisitions (M&A). Identifying comparable companies is a critical task, as it can inform investment decisions, help identify potential synergies, and reveal areas for growth and improvement. The accurate quantification of inter-company similarity, also referred to as <strong>company similarity quantification</strong>, is the cornerstone to successfully executing such tasks. However, company similarity quantification is often a challenging and time-consuming process, given the vast amount of data available on each company, and the complex and diversified relationships among them.</p> <p>While there is no universally agreed definition of company similarity, researchers and practitioners in PE industry have adopted various criteria to measure similarity, typically reflecting the companies' operations and relationships. These criteria can embody one or more dimensions such as industry sectors, employee profiles, keywords/tags, customers' review, financial performance, co-appearance in news, and so on. Investment professionals usually begin with a limited number of companies of interest (a.k.a. seed companies) and require an algorithmic approach to expand their search to a larger list of companies for potential investment. </p> <p>In recent years, transformer-based Language Models (LMs) have become the preferred method for encoding textual company descriptions into vector-space embeddings. Then companies that are similar to the seed companies can be searched in the embedding space using distance metrics like cosine similarity. The rapid advancements in Large LMs (LLMs), such as GPT-3/4 and LLaMA, have significantly enhanced the performance of general-purpose conversational models. These models, such as ChatGPT, can be employed to answer questions related to similar company discovery and quantification in a Q&A format.</p> <p>However, graph is still the most natural choice for representing and learning diverse company relations due to its ability to model complex relationships between a large number of entities. By representing companies as nodes and their relationships as edges, we can form a <strong>Knowledge Graph (KG)</strong>. Utilizing this KG allows us to efficiently capture and analyze the network structure of the business landscape. Moreover, KG-based approaches allow us to leverage powerful tools from network science, graph theory, and graph-based machine learning, such as Graph Neural Networks (GNNs), to extract insights and patterns to facilitate similar company analysis. While there are various company datasets (mostly commercial/proprietary and non-relational) and graph datasets available (mostly for single link/node/graph-level predictions), there is a scarcity of datasets and benchmarks that combine both to create a large-scale KG dataset expressing rich pairwise company relations.</p> <p><strong>Source Code and Tutorial:<br></strong><a href="https://github.com/llcresearch/CompanyKG2"><strong>https://github.com/llcresearch/CompanyKG2</strong></a></p> <p><strong>Paper: to be published<br></strong></p>
Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"
<h1><strong>Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"</strong></h1> <p>This repository contains pre-processed data sets and code scripts to reproduce the data processing and analyses that are presented in the corresponding manuscript. Some minor pre-processing was completed before presenting this -- namely, the land cover raster was clipped to the study area of Knox County, Tennessee, USA, prior to placing in the repository to reduce the file size. </p> <h2><strong>How to use this repository to reproduce results </strong></h2> <p>This repository is designed to support the reproduction of analyses in the associated manuscript. The entire project can be downloaded and stored anywhere on your computer, as long as the file structure is not altered. The project contains folders with all data sets and code scripts necessary for analysis.</p> <p><strong>What you will need: </strong><br> - Installed R and RStudio for purely spatial cluster and global model analyses<br> - Basic understanding of how to open R and run code </p> <p><strong> You do NOT need:</strong><br> - To download or install R packages on your own; that is taken care of within this environment<br> - To write any code <br> - To set up any working directories in R </p> <h3><strong>Important: Using `renv`</strong></h3> <p>Short Version: When you open the R project, run `renv::restore()` and follow the prompts to install the necessary R packages. </p> <p>The R package `renv` was used to create a <strong>project library</strong>, which contains all R packages that are used by the project. The packages in the project library are <strong>the versions used during the original analysis</strong>. This means that if any packages are updated by developers in ways that would change the results of the analysis, this project can still produce the original results because of `renv`. When you open this project for the first time, `renv` will automatically download and install itself and ask you to run `renv::restore()`. <strong>You should run `renv::restore()` to automatically download and install all of the packages within this reproducible environment</strong>. </p> <h2><strong>## Basic step-by-step guide:</strong></h2> <p>- 1. Download the entire repository by clicking "Code -> Download ZIP" on GitHub or by downloading the ZIP file in Zenodo<br>- 2. Extract the ZIP file anywhere on your computer (do not change the structure of the files once extracted)<br>- 3. In RStudio, click *File -> Open Project* and browse to the location where you extracted the repository; in the repository file, open the knoxaedeslandcover R Project file <br>- 4. Open any of the R scripts in the `analysis/` folder<br>- 5. Run the code `renv::restore()` in the script or in the console and follow the prompt to install the packages <br> - Now you can run the R Scripts; start from the top with loading the packages and data, then work your way down line-by-line</p> <h3><strong># `analysis/` Folder</strong></h3> <p>The `analysis/` folder contains scripts for processing data and conducting analyses. Each file is an R script that should be opened in R studio. The first shows how to process and aggregate the various raw data files; if you are only interested in reproducing analyses from the manuscript, you can skip to the second file and work from there. </p> <p><strong><em>## Files within the `analysis/` folder</em></strong></p> <p>The files are numbered in the order that they were run for the original analysis. In this case, none of the analyses are dependent on the others, so they can technically be used in any order. The numbers associated with each file describe the order that the analyses would normally be run. </p> <p> - `(1)dataprep.R` contains the code for cleaning and combining the land cover, climate, and mosquito data -- this includes calculating the land cover percentages at different scales and calculating weekly and timelagged climate values<br> - `(2)summary_analysis.R` contains code for reproducing summary data and creating graphs from the manuscript<br> - `(3)variable_selection.R` contains code for asssessing collinearity and fitting models to identify the best fitting variables for each speceis<br> - `(4)finalmodels.R` contains code for fitting the final models using the selected variables for each species </p> <h3><strong># `data/` Folder</strong></h3> <p>This folder contains several datasets, including one that compiles them all for analyses (`knox_joined`). The raw data are included to show how the data was processed and aggregated, but the individual raw data files are not needed for analyses. See `data dictionary.txt` for a description of all attributes contained within each file. </p> <p><strong><em>## Files within the `data/` folder</em></strong></p> <p> - `knox22_joined.RDS` contains a cleaned and joined version of land cover, climate, and mosquito data in R Data Serialization format, which maintains predefined factor and numeric designations for columns. <br> - `knox22_joined.csv` contains a cleaned and joined version of land cover, climate, and mosquito data in CSV format -- identical to 'knox22_joined.RDS'<br> - `sites22.csv` contains the names, site codes, and coordinates of the study sites<br> - `aedes22_clean.csv` contains the raw mosquito collection data for the study without any climate or land cover information <br> - `NLCD_2019_landcover_clippedtoKnox.tif` contains the NLCD land cover data, already clipped to Knox County, TN, USA<br> - `knox22_temperature.csv` contains raw daily temperatures for the city of Knoxville in 2022<br> - `knox22_rainfall.csv` contains raw daily precipitation for the city of Knoxville watersheds in 2022<br> - `rainfall_stations.csv` contains the descriptions, approximate street addresses, and geographic coordinates for rainfall monitoring sites <br> - `data dictionary.txt` file that defines column names and other data attributes for every dataset </p> <h3><strong># `renv/` Folder</strong></h3> <p>The `renv/` folder contains bits and pieces needed for the `renv` package. Nothing should be altered in this folder. </p> <p> </p> <h2><strong>References for source data </strong></h2> <p> - Some of the data in this repository were originally obtained from open access sources. </p> <p> - Land cover data was obtained from the National Land Cover Database (NLCD) 2019 data product, specifically the "NLCD 2019 Land Cover (CONUS)" product. The original, unclipped raster can be freely downloaded here: https://www.mrlc.gov/data/nlcd-2019-land-cover-conus</p> <p> - Temperature data was downloaded from the United States National Oceanic and Atmospheric Administration (NOAA) weather station for Knoxville, Tennessee. The source data can be downloaded from this site: https://www.weather.gov/mrx/tysclimate</p> <p> - Rainfall data was obtained from the City of Knoxville rainfall data website, located here: https://www.knoxvilletn.gov/government/city_departments_offices/engineering/stormwater_engineering_division/rainfall_data</p> <p> - All mosquito collection data was collected directly by the manuscript authors</p>
Data from: Individual energetics scale up to community coexistence: Movement, metabolism and biodiversity dynamics in fragmented landscapes
<p>Unraveling the intricate mechanisms that govern community coexistence remains a daunting challenge, particularly amidst ongoing environmental change. To understand the response of individual animals to environmental change, physiology and individual metabolism are often studied. However, this perspective is currently largely lacking in community ecology. We argue that the integration of individual metabolism into community theory can offer new insights into coexistence. We present the first individual-based metabolic community model for a terrestrial mammal community to simulate energy dynamics and home range behavior in different environments. Using this model, we investigate how ecologically similar species coexist and maintain their energy balance under food competition. Only if individuals of different species are able to balance their incoming and outgoing energy over the long-term will they be able to coexist. After thoroughly testing and validating the model against real-world patterns such as of home range dynamics and field metabolic rates, we applied it as a case study to scenarios of habitat fragmentation - a widely discussed topic in biodiversity research. First, comparing single-species simulations with community simulations, we find that the effect of habitat fragmentation on populations is strongly context-dependent. While populations of species living alone in the landscape were mostly positively affected by fragmentation, the diversity of a community of species was highest under medium fragmentation scenarios. Under medium fragmentation, energy balance and reproductive investment were also most similar among species. We therefore suggest that similarity in energy balance among species promotes coexistence. We argue that energetics should be part of community ecology theory, as the relative energetic status and reproductive investment can reveal why and under what environmental conditions coexistence is likely to occur. As a result, landscapes can potentially be protected and designed to maximize coexistence. The metabolic community model presented here can be a promising tool to investigate other scenarios of environmental change or other species communities to further disentangle global change effects and preserve biodiversity.</p>
Figures 1–6 in Diagnosis of Spilococcus pacificus (Borchsenius, 1949) and an updated list of scale insects (Hemiptera: Coccomorpha) on pears (Pyrus L.: Rosaceae) in South Korea
Figures 1–6. Spilococcus pacificus (Borchsenius) on pear trees. 1–2) Habitus. 3) Adult female. 4) Head (black arrows: oral rim tubular ducts). 5) Hind leg (black arrow: translucent pores). 6) Anal lobe cerarius (black arrow: anal bar).
Fig. 1 in Hymenopteran parasitoids associated with scale insects (Hemiptera: Coccoidea) in tropical fruit trees in the eastern Amazon, Brazil
Fig. 1. Interactions between species of scale insects and parasitoids with the total number of interactions with each species of host plant (Jun 2014 to Aug 2015) at Maranhão Island, Maranhão, Brazil.
Abundance-diversity relationship as a unique signature of temporal scaling in the fossil record
<p>Species diversity increases with the temporal grain of samples according to the species-time relationship, impacting paleoecological analyses because the temporal grain (time averaging) of fossil assemblages varies by several orders of magnitude. We predict a positive relation between total abundance and sample size-independent diversity (ADR) in fossil assemblages because an increase in time averaging, determined by a decreasing sediment accumulation, should increase abundance and depress species dominance. We demonstrate that, in contrast to negative ARDs of non-averaged living assemblages, the ARD of Holocene fossil assemblages is positive, unconditionally or when conditioned on the energy availability gradient. However, the positive fossil ADR disappears when conditioned on sediment accumulation, suggesting that ADR can be a signature of diversity scaling induced by variable time averaging. Conditioning ADR on sediment accumulation can identify and remove the scaling effect caused by time averaging, providing an avenue for unbiased biodiversity comparisons across space and time.</p>
Data and code from: Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data
<p>We use open source human gut microbiome data to learn a microbial "language" model by adapting techniques from Natural Language Processing (NLP). Our microbial "language" model is trained in a self-supervised fashion (i.e., without additional external labels) to capture the interactions among different microbial species and the common compositional patterns in microbial communities. The learned model produces contextualized taxa representations that allow a single bacteria species to be represented differently according to the specific microbial environment it appears in. The model further provides a sample representation by collectively interpreting different bacteria species in the sample and their interactions as a whole. We show that, compared to baseline representations, our sample representation consistently leads to improved performance for multiple prediction tasks including predicting Irritable Bowel Disease (IBD) and diet patterns. Coupled with a simple ensemble strategy, it produces a highly robust IBD prediction model that generalizes well to microbiome data independently collected from different populations with substantial distribution shift.</p> <p>We visualize the contextualized taxa representations and find that they exhibit meaningful phylum-level structure, despite never exposing the model to such a signal. Finally, we apply an interpretation method to highlight bacterial species that are particularly influential in driving our model's predictions for IBD.</p>
Translaminar Fracture in a Mini-Protruded Compact Tension Specimen: A Dataset of Micro-Scale Tomograms of a Thin-Ply Carbon Fibre-Epoxy Composite acquired via Synchrotron Radiation Computed Tomography During In-Situ Loading
<p>In this study, we developed a scaled-down “mini-protruded compact tension specimen” to facilitate in-situ tensile testing coupled with synchrotron radiation computed tomography (SRCT). This innovative design provides valuable insights into in-situ translaminar damage mechanisms, significantly enhancing the accuracy of data used in finite element models.</p> <p>The specimen is made of HS40 carbon fibres and ThinPreg<sup>TM </sup>736LT epoxy resin, with the layup of [90<sub>2</sub>/0/90<sub>2</sub>/0/90<sub>2</sub>/0/90<sub>2</sub>]. The translaminar fracture experiments were conducted under continuous loading and scanning using ultra-fast SRCT at the Swiss Light Source (SLS) TOMCAT beamline (Paul Scherrer Institut in Villigen, Switzerland). A polychromatic beam with an energy of 24 keV was used. The achieved voxel size was 800 <em>nm</em>, and 1000 projections per scan and 2 <em>ms</em> exposure time were acquired per scan. The GigaFRoST camera served as the detector. The scans were reconstructed into 3D volumes using the SLS’s in-house absorption-based algorithm (Gridrec) for critical loading steps during a test—both before and after a load drop (detailed in the accompanying Excel file). The tensile loading was exerted on the specimen at a rate of 0.2 <em>mm/min</em> until failure during scanning with the Deben CT500.</p>
Figure 14 in Scale morphology and specialized dorsal scales of a new teleosteomorph fish from the Aptian of West Gondwana
Figure 14. (a) SEM photograph of flank scale of Teleosteomorpha n. sp. 1 (sample "T-5.5"). (b) SEM photograph of flank scale of Teleosteomorpha n. sp. 1 (MIC-V706).
Figure 13 in Scale morphology and specialized dorsal scales of a new teleosteomorph fish from the Aptian of West Gondwana
Figure 13. Scutes of Teleosteomorpha n. sp. 1. (a) Ventral scute preceding caudal fin in specimen MIC-V662. (b) Camera lucida drawing of scute in detail. (c) Pre-anal scutes in specimen MIC-V519a; ant, anteriad; pas, preanal scutes; ves, ventral caudal scute.
Figure 10 in Scale morphology and specialized dorsal scales of a new teleosteomorph fish from the Aptian of West Gondwana
Figure 10. Postcranial region of Teleosteomorpha n. sp. 1 (MIC-V701a). Detail in photograph and drawing of uppermost scale row in lateral view. It shows the second row also, as well as the first row scale from the left side of the specimen; ant, anteriad.
Figure 12 in Scale morphology and specialized dorsal scales of a new teleosteomorph fish from the Aptian of West Gondwana
Figure 12. Picture and camera lucida drawing of caudal fin squamation of Teleosteomorpha n. sp. 1 (MIC V523). Dorsal lobe shows the base of the first principal ray covered by scales. Note the dorsal scute preceding caudal fin; ant, anteriad; des, dorsal caudal scute; llls, last lateral line scale; 1st cr, first caudal ray; 1st r s, first ray scales; 1st pms, first posterior margin scale.
Figure 11 in Scale morphology and specialized dorsal scales of a new teleosteomorph fish from the Aptian of West Gondwana
Figure 11. Scales of Teleosteomorpha n. sp. 1. (a) Specimen MIC-V46a. (b) Detail of camera lucida drawing of scales carrying the main lateral line; ant, anteriad; asll, branch from lateral line with opening at surface; ll, main lateral line with opening at posterior margin; lls, lateral line scales.
Figure 9 in Scale morphology and specialized dorsal scales of a new teleosteomorph fish from the Aptian of West Gondwana
Figure 9. (a) Postcranial region of Teleosteomorpha n. sp. 1 (MIC-V660); white rectangle marks detail in (b). (b) Picture and camera lucida drawing showing the particular pattern of missing median dorsal row, but a series of scales on each side of dorsal margin; ant, anteriad; ls, left side scales; rs, right side scales.
Figure 15 in Scale morphology and specialized dorsal scales of a new teleosteomorph fish from the Aptian of West Gondwana
Figure 15. Histological slides of Teleosteomorpha n. sp. 1 (specimen MIC-V523) observed under petrographic microscope. (a) Lepisosteoidscale type. (b) Multiple ganoine layers under normal light. (c) Multiple ganoine layers under polarized light. (d) Ganoine and basal bony plate with all the lepisosteoid features; bbp, basal bony plate; c.W, canaliculi of Williamson; gl, ganoine layers; lb, laminar bone; llc, lateral line canal; o, lacunae (osteocyte spaces); sv.c, vascular canal; S.f, Sharpey's fiber.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.