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opencc-by-4.0Feb 2022View details →

Text-fig. 3. Cercidiphyllum cf. alalongum R.A.SCOTT et E.A.WHEELER, UF 279-24543. a, b: Diffuse-porous wood, exclusively solitary vessels, axial parenchyma rare, thick-walled fibers, TS. c: Scalariform perforation plate with more than 30 bars, RLS. d: Helical thickenings (HT) in vessel element tip, RLS. e: Opposite to scalariform intervessel pits, RLS. f, g: Heterocellular rays 1–2 cells wide, occasionally uniseriate and biseriate portions of similar width, TLS. h: Ray with alternating rows of procumbent and upright (-square) cells, RLS. Scale bars: 200 µm in a; 100 µm in b, f; 50 µm in c, g, h; 20 µm in d, e. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa

Text-fig. 3. Cercidiphyllum cf. alalongum R.A.SCOTT et E.A.WHEELER, UF 279-24543. a, b: Diffuse-porous wood, exclusively solitary vessels, axial parenchyma rare, thick-walled fibers, TS. c: Scalariform perforation plate with more than 30 bars, RLS. d: Helical thickenings (HT) in vessel element tip, RLS. e: Opposite to scalariform intervessel pits, RLS. f, g: Heterocellular rays 1–2 cells wide, occasionally uniseriate and biseriate portions of similar width, TLS. h: Ray with alternating rows of procumbent and upright (-square) cells, RLS. Scale bars: 200 µm in a; 100 µm in b, f; 50 µm in c, g, h; 20 µm in d, e.

opencc-by-4.0Dec 2021View details →

Text-fig. 5. Fossil remains of a leafy Lepidodendron ophiurus BRONGN. shoot bearing a Flemingites strobilus produced by a tree similar to that shown in Text-fig. 2a; Middle Coal Measures Formation (Duckmantian – upper Bashkirian), Brymbo, near Wrexham, UK (see Thomas et al. 2020: fig. 16b); National Museum Wales specimen 2013.43G.120. in Naming Of Parts: The Use Of Fossil-Taxa In Palaeobotany

Text-fig. 5. Fossil remains of a leafy Lepidodendron ophiurus BRONGN. shoot bearing a Flemingites strobilus produced by a tree similar to that shown in Text-fig. 2a; Middle Coal Measures Formation (Duckmantian – upper Bashkirian), Brymbo, near Wrexham, UK (see Thomas et al. 2020: fig. 16b); National Museum Wales specimen 2013.43G.120.

opencc-by-4.0Aug 2022View details →

Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)

Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).

FIGURE 9 in Distinguishing Scaphinotus mannii Wickham (Coleoptera: Carabidae: Cychrini), a Species of Conservation Concern, from Similar Congeneric Species

FIGURE 9. Median lobe of male genitalia. A,B. Scaphinotus mannii Wickham (Wawawai, Whitman County, Washington); C,D. S. regularis (LeConte) (Moscow Mountain, Latah County, Idaho; E,F. S. relictus (Horn) (Mount Spokane, Spokane County, Washington); A,C,E = dorsal view; B,C,F = left lateral view. Scale line = 1.0 mm. Images modified and reproduced with permission from Kavanaugh & Angel (2015).

FIGURE 10 in Distinguishing Scaphinotus mannii Wickham (Coleoptera: Carabidae: Cychrini), a Species of Conservation Concern, from Similar Congeneric Species

FIGURE 10. Apex of median lobe of male genitalia, dorsal view. A. Scaphinotus mannii Wickham (Tumalum Creek, Garfield County, Washington); B. S. regularis (LeConte) (9 km SSE of Lowell, Idaho County, Idaho); C. S. relictus (Horn) (Charley Creek, Asotin County, Washington). Scale line = 0.5 mm.

FIGURE 8 in Distinguishing Scaphinotus mannii Wickham (Coleoptera: Carabidae: Cychrini), a Species of Conservation Concern, from Similar Congeneric Species

FIGURE 8. Left apical stylomere of female ovipositor, dorsolateral view. A. Scaphinotus mannii Wickham (Cummings Creek, Garfield County, Washington); B. S. regularis (LeConte) (Cambridge, Idaho County, Idaho); C. S. relictus (Horn) (Cummings Creek, Garfield County, Washington). Arrows indicate where length and width measurements were taken. Scale line = 0.5 mm.

FIGURE 6. Pronotum, dorsal view. A in Distinguishing Scaphinotus mannii Wickham (Coleoptera: Carabidae: Cychrini), a Species of Conservation Concern, from Similar Congeneric Species

FIGURE 6. Pronotum, dorsal view. A. Scaphinotus mannii Wickham (Steptoe Canyon, Whitman County, Washington); B. S. regularis (LeConte) (9 km SSE of Lowell, Idaho County, Idaho); C. S. relictus (Horn) (8 km NNE of Moscow, Latah County, Idaho).

FIGURE 3. Dorsal habitus. A in Distinguishing Scaphinotus mannii Wickham (Coleoptera: Carabidae: Cychrini), a Species of Conservation Concern, from Similar Congeneric Species

FIGURE 3. Dorsal habitus. A. Scaphinotus mannii Wickham (Steptoe Canyon, Whitman County, Washington); B. S. regularis (LeConte) (9 km SSE of Lowell, Idaho County, Idaho); C. S. relictus (Horn) (8 km NNE of Moscow, Latah County, Idaho). Scale line = 1.0 mm.

FIGURE 1 in Distinguishing Scaphinotus mannii Wickham (Coleoptera: Carabidae: Cychrini), a Species of Conservation Concern, from Similar Congeneric Species

FIGURE 1. Base of pronotum showing presence or absence of posterolateral setae, dorsal view. A. Scaphinotus mannii Wickham with setae absent (Steptoe Canyon, Whitman County, Washington); B. S. mannii with setae present (Tumalum Creek, Garfield County, Washington); C. S. regularis (LeConte) (Slate Creek, Idaho County, Idaho); D. S. relictus (Horn) (8 km NNE of Moscow, Latah County, Idaho).

FIGURE 5. Labrum, dorsal view. A in Distinguishing Scaphinotus mannii Wickham (Coleoptera: Carabidae: Cychrini), a Species of Conservation Concern, from Similar Congeneric Species

FIGURE 5. Labrum, dorsal view. A. Scaphinotus mannii Wickham (Steptoe Canyon, Whitman County, Washington); B. S. regularis (LeConte) (9 km SSE of Lowell, Idaho County, Idaho); C. S. relictus (Horn) (8 km SE of Craigmont, Lewis County, Idaho).

FIGURE 7. Left elytron, dorsal view. A in Distinguishing Scaphinotus mannii Wickham (Coleoptera: Carabidae: Cychrini), a Species of Conservation Concern, from Similar Congeneric Species

FIGURE 7. Left elytron, dorsal view. A. Scaphinotus mannii Wickham (Steptoe Canyon, Whitman County, Washington); B. S. regularis (LeConte) (9 km SSE of Lowell, Idaho County, Idaho); C. S. relictus (Horn) (8 km NNE of Moscow, Latah County, Idaho).

FIGURE 4. Head, dorsal view. A in Distinguishing Scaphinotus mannii Wickham (Coleoptera: Carabidae: Cychrini), a Species of Conservation Concern, from Similar Congeneric Species

FIGURE 4. Head, dorsal view. A. Scaphinotus mannii Wickham (Steptoe Canyon, Whitman Co., Washington); B. S. regularis (LeConte) (9 km SSE of Lowell, Idaho County, Idaho); C. S. relictus (Horn) (Cold Creek Canyon, Columbia County, Washington).

FIGURE 11 in Distinguishing Scaphinotus mannii Wickham (Coleoptera: Carabidae: Cychrini), a Species of Conservation Concern, from Similar Congeneric Species

FIGURE 11. Male left protarsi, with tarsomeres (1 to 5) identified by number. A,B. Scaphinotus mannii Wickham (Steptoe Canyon, Whitman County, Washington); C,D. S. relictus (Horn) (Selway River at O'Hara Campground, Idaho County, Idaho); A,C = dorsal view; B,D = ventral view. Scale lines = 1.0 mm. Images modified and reproduced with permission from Kavanaugh & Angel (2015).

opencc-zeroMay 2024View details →

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Data from: Remarkable similarity of oxygen tolerance across marine taxa when standardized for temperature and body size

Species' ranges are shifting in response to increasing temperature and decreasing oxygen in coastal oceans. Forecasting these shifts is limited by information on physiological oxygen thresholds and how they depend on temperature. Here, we adopt an ecophysiological metric, the metabolic index, and estimate its parameters from data collected on marine taxa using phylogenetic trait imputation. The metabolic index is the ratio of temperature-dependent rates of oxygen supply to basal oxygen demands. By applying a hierarchical phylogenetic model to a data set of 74 marine taxa that accounts for both taxonomic distance (from Linnean classification) and biases related to lab methods, we find that the critical oxygen pressure at a reference body size and temperature is remarkably consistent across taxa, ranging 2.9 to 4.9 kPa. In comparison, the estimated effect of temperature on the critical oxygen pressure was more variable among taxa. These findings suggest that species-level differences in oxygen tolerance might be primarily related to differences in body size and preferred temperature. Further, this work provides data-informed distributions of parameters for species that lack experimental data to aid species distribution forecasting.

openother-ncMay 2024View details →

CompanyKG Dataset V2.0: A Large-Scale Heterogeneous Graph for Company Similarity Quantification

CompanyKG 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. Edges: 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. Nodes: The graph includes all companies connected by edges defined previously. Each node represents a company and is associated with a descriptive text, such as "Klarna is a fintech company that provides support for direct and post-purchase payments ...". 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>. Evaluation Tasks. 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: <ul> <li>Similarity Prediction (SP). 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>Competitor Retrieval (CR). Each sample contains one target company 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 N direct competitors in this CR evaluation set, we expect a competent method to retrieve all N competitors when searching for similar companies to A. </li> <li>Similarity Ranking (SR) is designed to assess the ability of any method to rank candidate companies (numbered 0 and 1) based on their similarity to a query company. 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>Edge Prediction (EP) 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> Background and Motivation 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 company similarity quantification, 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. 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.  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. 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 Knowledge Graph (KG). 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. Source Code and Tutorial: <a href="https://github.com/llcresearch/CompanyKG2">https://github.com/llcresearch/CompanyKG2</a> Paper: to be published

Figure 4 in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment

Figure 4: Relative abundance of eukaryotic communities of microinvertebrate external and internal microbiomes: (A) total non-host community (B) metazoan phyla, (C) fungal clades.

Figure 3 in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment

Figure 3: Relative abundance of bacterial communities of microinvertebrate external and internal microbiomes: (A) genera of Cyanobacteria, (B) genera of Bacteroidota, (C) families of Proteobacteria, (D) genera of Actinobacteriota.

Figure 2 in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment

Figure 2: Diversity of microinvertebrate bacterial external and bacterial internal microbiomes. (A) Shannon's diversity (box plot using Hill Numbers) with a significant difference between microbiomes (P=0.03, GLM) but not microinvertebrates (P=0.14), streams (P=0.22), or mat types (P=0.52). (B) Compositional difference based on Bray Curtis distance matrix visualized with a NMDS ordination, in which microinvertebrate host explained the most variation (R2 =0.14, PERMANOVA). Stars show centroid location of microbiome type, and solid circles show individual microbiomes.

Figure 1 in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment

Figure 1: Graphical abstract of methods used to construct external and internal microbiomes of nematodes and tardigrades. For internal microbiomes, microinvertebrates were washed, sequenced, and host sequences subtracted before being averaged for each of the 24 mat replicates and 3 host types. For external microbiomes, unwashed microinvertebrates were sequenced, host sequences subtracted, and averaged for each of the 24 mat replicates and host types. The internal ASV abundances were then subtracted from the corresponding ASVs of unwashed community of the same mat replicate and microinvertebrate type to create the final external microbiome.

SI Figure 4: SEM images of either unwashed (left) or washed (right) E. antarcticus nematodes. A. Unwashed head region with arrows pointing to attached material and possible fungal hyphae. B. Washed head region with arrows pointing to the remaining attached material. C. Unwashed annules with arrows pointing to commonly attached foreign material. D. Washed annules with arrows pointing to remaining attached material. E. Unwashed somatic pore with arrows pointing to the common organic material. F. Washed vulva with an arrow pointing to remaining attached organic material. G. Unwashed cuticle with arrows showing a possible biofilm. H. Washed cuticle showing single attached cells indicated with arrows. I. Unwashed cuticle showing an off-axis line of attached material. J. Washed cuticle showing a similar off-axis line of material (as indicated with arrow) but reduced in quantity compared to the unwashed. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment

SI Figure 4: SEM images of either unwashed (left) or washed (right) E. antarcticus nematodes. A. Unwashed head region with arrows pointing to attached material and possible fungal hyphae. B. Washed head region with arrows pointing to the remaining attached material. C. Unwashed annules with arrows pointing to commonly attached foreign material. D. Washed annules with arrows pointing to remaining attached material. E. Unwashed somatic pore with arrows pointing to the common organic material. F. Washed vulva with an arrow pointing to remaining attached organic material. G. Unwashed cuticle with arrows showing a possible biofilm. H. Washed cuticle showing single attached cells indicated with arrows. I. Unwashed cuticle showing an off-axis line of attached material. J. Washed cuticle showing a similar off-axis line of material (as indicated with arrow) but reduced in quantity compared to the unwashed.