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FIGURE 25. Asplenium uhligii. Rhizome scales.—A–C in Synoptic Revision of Aspleniaceae (Asplenium, Hymenasplenium) of Rwanda

FIGURE 25. Asplenium uhligii. Rhizome scales.—A–C, from type. Photographs: E.Fischer & W.Lobin.—Asplenium goetzei. Rhizome scales.—D–F, from type. Photographs: E.Fischer & W.Lobin. Scale bar: 1 mm.

FIGURE 24. Asplenium uhligii. Rhizome scales—A–F in Synoptic Revision of Aspleniaceae (Asplenium, Hymenasplenium) of Rwanda

FIGURE 24. Asplenium uhligii. Rhizome scales—A–F. Photographs: E.Fischer. A, B. Auquier 2289; C. de Witte 2235; D–F. Karisoke. Scale bar: 1 mm.

FIGURE 31. Asplenium volkensii.—A, B. Rhizome scales.—C–E in Synoptic Revision of Aspleniaceae (Asplenium, Hymenasplenium) of Rwanda

FIGURE 31. Asplenium volkensii.—A, B. Rhizome scales.—C–E. Spores. Photographs: E. Fischer, A, B, Gahinga; C–E. Fischer, W. Lobin & Y. Malkowsky. Scale bars: A–C. 1 mm; D–F. 10 µm; G. 8 µm.

FIGURE 8. Asplenium majus. A–E. Rhizome scales.—A. actiniopteroides. D, E in Synoptic Revision of Aspleniaceae (Asplenium, Hymenasplenium) of Rwanda

FIGURE 8. Asplenium majus. A–E. Rhizome scales.—A. actiniopteroides. D, E. Rhizome scales,—F, G. Type of A. actiniopteroides. Scale bars A–G. 1 mm.

opencc-zeroAug 2023View details →

Data from: Across space and time: a review of sampling and analytical biases in fossil data across macroecological scales

Quantitative studies of fossil data have proven critical to a number of major macroevolutionary and macroecological discoveries, such as the 'Big 5' mass extinctions of the Phanerozoic. The development and easy accessibility of major meta-data sources such as the Paleobiology Database and Geobiodiversity Database have also spurred the widespread application of these data to testing ecological hypotheses at finer spatiotemporal and phylogenetic scales. However, issues of preservational/taphonomic biases, sampling/collecting biases, taxonomic issues, and analytical choice can impact the degree of interpretative resolution possible, and even obscure biological 'signal' from error/bias-introduced 'noise'. The degree to which these factors can impact analytical interpretations is not well-documented in comparison to the scale of use of these data sources. Here, we review the many forms of systematic error that can creep into a paleoecological study, from the stage of data collection to the interpretation of analytical results, and provide two case studies based upon re-analysis of previously-published datasets to illustrate the varying impacts of such biases. The first case study focuses on the Cambrian Burgess Shale, and the second on the Belly River Group, with both representing highly-sampled, taphonomically characterized, and spatiotemporally-constrained datasets developed through multiple years of sustained field collecting. In the former, we illustrate the impacts of collecting bias through quantitative comparisons of collected vs. discarded specimens over multiple field seasons, illustrating the impact of this data loss on ecological reconstructions and analysis. In the latter case study, we review the impact of preservational biases, the approaches to their quantification and mitigation, where these approaches have led to misinterpretations in the past, and the differences in ecological resolution that result from occurrence vs abundance approaches in macroecological analysis. Lastly, we synthesize these case studies with our review of past approaches to propose a series of recommendations for future paleoecological and macroecological studies, emphasizing the continued importance of high-quality primary data and ongoing need for a first-principles approach to address existing issues of missing data.

opencc-zeroAug 2023View details →

Chromosome-scale thraustochytrid genome assembly

We used long-read sequencing to produce a telomere-to-telomere genome assembly for the osmoheterotrophic stramenopile protist Aurantiochytrium limacinum MYA-1381. Its ~62 Mb genome is mainly organized in 26 linear chromosomes with a novel configuration: subtelomeric rDNAs are interspersed with long repeated sequence elements denoted as LOng REpeated - TElomere And Rdna Spacers (LORE-TEARS). These repeats may play a role in chromosome end maintenance. A putative circular mirusvirus genome is present at a high copy number (called circular element 1; CE1). The presence of another mirusvirus genome at the end of chromosome 15 in superposition between two complete sets of rRNA and LORE-TEAR elements suggests a dynamic process at the chromosome ends.

opencc-by-4.0Jun 2023View details →

SPEI-GD: The first global multi-scale daily SPEI dataset for 1983-2020

The global daily SPEI dataset (SEPI-GD) at 0.25° spatial resolution form 1982 to 2021. This depository includes the five files of the daily SPEI data with five time scales (5, 30, 90, 180, and 360 days). The calculation based on ERA5's precipitation and Singer's potential evapotranspiration. All data are geographic latitude-longitude projection and NetCDF format. See paper for detailed explanation: Liu X, Yu S, Yang Z, et al. The first global multi-timescale daily SPEI dataset from 1982 to 2021[J]. Scientific Data, 2024, 11(1): 223.

opennotspecifiedAug 2020View details →

Fig. 3. Linear correlations between the scaled calculated and experimental 13C in Phenolics from Archidendron clypearia (Jack) I.C.Nielsen protect SH-SY5Y cells against H O -induced oxidative stress

Fig. 3. Linear correlations between the scaled calculated and experimental 13C NMR chemical shifts and statistical DP4+ parameters for compounds 1–3 (A–C).

opennotspecifiedSep 2023View details →

FIGURES 1–8. Antarctoperla aonikenk. Male holotype. 1. Antenna, scale bar 0.5 in A new species of Gripopterygidae (Insecta: Plecoptera) from Patagonia: Antarctoperla aonikenk sp. n.

FIGURES 1–8. Antarctoperla aonikenk. Male holotype. 1. Antenna, scale bar 0.5 mm; 2. Habitus, dorsal, without abdominal tip, scale bar 1 mm; 3. Head, dorsal, scale bar 200 µm; 4. Pronotum, dorsal, scale bar 200 µm; 5–6. Right wings, venations wings, scale bar 1 mm; 7. Genitalia, dorsal, without paraprocts, scale bar 200 µm; 8. Genitalia, lateral, without right paraproct, scale bar 200 µm. AA1 = first anterior Analis; AA2 = second anterior Analis; as = anterior sclerite; bsce = cercum base; ce = cercum; CuA = anterior Cubitus; CuP = posterior Cubitus; ep = epiproctum; M = Media; MA = anterior Media; MP = posterior Media; ms = medium sclerite; pa = paraproctum; ps = posterior sclerite; RA = anterior Radius; RP = posterior Radius; ScP = posterior Subcosta; vh = ventral hook.

opencc-by-4.0Feb 2023View details →

Preclinical to Clinical Translation for Intervertebral Disc Repair: Effects of Species-Specific Scale, Metabolism and Matrix Synthesis Rates on Cell-Based Regeneration

Supporting datasets for journal article funded by the ERC

opencc-by-4.0Aug 2023View details →

Data and R code for "Negative effects of wind on plant hydraulics at the global scale"

To minimize ontogenetic and methodological variation, we only included trait data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; and (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources. Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2) if the original data were not available. The following variables were extracted from WorldClim: mean annual wind speed, mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, precipitation of driest month, and minimum temperature of coldest month. The VPD data was extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data). The moisture index (MI) is the ratio of precipitation to PET. Simple linear regression was used to examine the relationships between two variables, utilizing the 'lm' function in R software. Partial regression analysis was conducted using the R package VISREG to investigate the relationships between wind speed and plant hydraulics while controlling for other variables. This analysis helped to illustrate the independent effect of wind on plant hydraulics. The Random Forest machine-learning algorithm (implemented using the R package randomForest) was utilized to assess the relative importance of environmental variables for each plant hydraulic trait. The Mean Decrease in Gini was calculated as the average of a variable's total decrease in node impurity, taking into account the proportion of samples that reach that node in each individual decision tree in the random forest. This provides a measure of a variable's importance in estimating the value of the target variable across all of the trees in the forest. A higher Mean Decrease in Gini value indicates greater importance of the variable. Multiple regression analyses were performed to develop predictive equations for plant hydraulic traits using environmental variables. To test for hydraulic traits-wind speed slope directions and differences among species groups in different climatic regions, we used standardized major axis (SMA) analyses. The R package SMATR was employed for these analyses. We considered p < 0.05 as the threshold for statistical significance in all models.

opencc-by-4.0Sep 2023View details →

Data for 'Nano-scale corrosion mechanism of T91 steel in static lead-bismuth eutectic: A combined APT, EBSD, and STEM investigation'

The original data for the manuscript 'Nano-scale corrosion mechanism of T91 steel in static lead-bismuth eutectic: A combined APT, EBSD, and STEM investigation'.

opencc-zeroSep 2023View details →

Data for: Multi-scale relationships in thermal limits within and between two cold-water frog species uncover different trends in physiological vulnerability

<ol> <li>Critical thermal limits represent an important component of an organism's capacity to cope with future temperature changes. Understanding the drivers of variation in these traits may uncover patterns in physiological vulnerability to climate change. Local temperature extremes have emerged as a major driver of thermal limits, although their effects can be mediated by the exploitation of fine-scale spatial variation in temperature through behavioral thermoregulation.</li> <li>Here, we investigated thermal limits along elevation gradients within and between two cold-water frog species (Ascaphus spp.), one with a coastal distribution (A. truei) and the other with a continental range (A. montanus). We quantified thermal limits for over 700 tadpoles, representing multiple populations from each species. We combined local temporal and fine-scale spatial temperature data to quantify local thermal landscapes (i.e., thermalscapes), including the opportunity for behavioral thermoregulation.</li> <li>Lower thermal limits for either species could not be reached experimentally reached without the water freezing, suggesting that cold tolerance is <0.3℃. In contrast, upper thermal limits varied among populations, but this variation only reflected local temperature extremes in A. montanus, perhaps due to greater variation in stream temperatures across its range. Lastly, we found minimal fine-scale spatial variability in temperature, suggesting limited opportunity for behavioral thermoregulation and thus increased vulnerability to warming for all populations.</li> <li>By quantifying local thermalscapes, we uncovered different trends in the relative vulnerability of populations across elevation for each species. In A. truei, physiological vulnerability decreased with elevation, whereas in A. montanus, all populations were equally physiologically vulnerable. These results highlight how similar environments can differentially shape physiological tolerance and patterns of vulnerability of species, and in turn, impact their vulnerability to future warming. </li> </ol>

opencc-zeroSep 2023View details →

The evolution of the Aristolochia pallida complex (Aristolochiaceae) contradicts traditional taxonomy and reflects large-scale glacial refugia in the Mediterranean

The taxonomy of the Mediterranean Aristolochia pallida complex has been under debate for several decades with the following species currently recognized: A. pallida, A. lutea, A. nardiana, A. microstoma, A. merxmuelleri, A. croatica and A. castellana. These taxa are distributed from Iberia to Turkey. To reconstruct phylogenetic and biogeographic patterns, we employed cpDNA sequence variation using both non-coding (intron and spacer) and protein-coding regions (i.e. trnK intron, matK gene and trnK-psbA spacer). Our results show that the morphology-based traditional taxonomy was not corroborated by our phylogenetic analyses. Aristolochia pallida, A. lutea, A. nardiana and A. microstoma were not monophyletic. Instead, strong geographic signals were detected. Two major clades, one exclusively occurring in Greece and a second one of pan-Mediterranean distribution, were found. Several subclades distributed in Greece, NW Turkey, Italy, as well as amphi-Adriatic subclades and a subgroup of southern France and Spain were revealed. The distribution areas of these groups are in close vicinity to hypothesised glacial refugia areas in the Mediterranean. According to molecular clock analyses, the diversification of this complex started around 3–3.3 my, before the onset of glaciation cycles, and the further evolution of and within major lineages falls into the Pleistocene. Based on these data, we conclude that the Aristolochia pallida alliance survived in different Mediterranean refugia rarely with low, but often with a high potential for range extension, and a high degree of morphological plasticity.

opencc-by-4.0Sep 2023View details →

SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection

Paper Accept by WACV 2024 [paper, supp] [<a href="https://arxiv.org/abs/2309.01907">arXiv</a>] Overview Synthetic datasets, recognized for their cost effectiveness, play a pivotal role in advancing computer vision tasks and techniques. However, when it comes to remote sensing image processing, the creation of synthetic datasets becomes challenging due to the demand for larger-scale and more diverse 3D models. This complexity is compounded by the difficulties associated with real remote sensing datasets, including limited data acquisition and high annotation costs, which amplifies the need for high-quality synthetic alternatives. To address this, we present SyntheWorld, a synthetic dataset unparalleled in quality, diversity, and scale. It includes 40,000 images with submeter-level pixels and fine-grained land cover annotations of eight categories, and it also provides 40,000 pairs of bitemporal image pairs with building change annotations for building change detection task. We conduct experiments on multiple benchmark remote sensing datasets to verify the effectiveness of SyntheWorld and to investigate the conditions under which our synthetic data yield advantages. <pre>Description ------------ This dataset has been designed for land cover mapping and building change detection tasks. File Structure and Content: --------------------------- 1. **1024.zip**: - Contains images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m. - `images` and `ss_mask` folders: Used for the land cover mapping task. - `images` folder: Post-event images for building change detection. - `small-pre-images`: Images with a minor off-nadir angle difference compared to post-event images. - `big-pre-images`: Images with a large off-nadir angle difference compared to post-event images. - `cd_mask`: Ground truth for the building change detection task. 2. **512-1.zip**, **512-2.zip**, **512-3.zip**: - Contains images of size 512x512 with a GSD of 0.3-0.6m. - `images` and `ss_mask` folders: Used for the land cover mapping task. - `images` folder: Post-event images for building change detection. - `pre-event` folder: Images for the pre-event phase. - `cd-mask`: Ground truth for building change detection. Land Cover Mapping Class Grep Map: ---------------------------------- class_grey = { "Bareland": 1, "Rangeland": 2, "Developed Space": 3, "Road": 4, "Tree": 5, "Water": 6, "Agriculture land": 7, "Building": 8, }</pre> Reference @misc{song2023syntheworld,       title={SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection},        author={Jian Song and Hongruixuan Chen and Naoto Yokoya},       year={2023},       eprint={2309.01907},       archivePrefix={arXiv},       primaryClass={cs.CV} }

Fig. 4 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness

Fig. 4. Spatial variation in the partial coefficients of determination (R2) for three environmental hypotheses and their shared effects. The coefficients are: energy (E), heterogeneity (H), seasonality (S), shared contribution between energy and heterogeneity (E: H), energy and seasonality (E: S), heterogeneity and seasonality (H: S), and the contribution shared among energy, heterogeneity and seasonality (E: H: S).

Fig. 2 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness

Fig. 2. Distribution of coefficients of determination (R2) of GWR for the analysis of bat species richness regressed on the predictors of the three environmental hypotheses combined.

Fig. 1 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness

Fig. 1. Global pattern of bat species richness. Legend corresponds to the number of bat species per 2◦ × 2◦ grid cell. Letters represent the zoogeographic realms (Holt et al. 2013): Na = Nearctic, P = Panamanian, Nt = Neotropical, Pa = Palearctic, Sa = Saharo-Arabian, At = Afrotropical, M = Madagascan, Or = Oriental, Au = Australian, Sj = Sino-Japanese, Oc = Oceanian.

Fig. 3 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness

Fig. 3. Spatial non-stationarity in the effect (R2) of the predictors of the three environmental hypotheses combined to explain the global pattern of bat species richness.