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Supplementary File 7 from: Rapier-Sharman N et. al., Secondary Transcriptomic Analysis of Triple-Negative Breast Cancer Reveals Reliable Universal and Subtype-Specific Mechanistic Markers, 2024
<p>Supplementary Materials File 7. Please note that though the order of the supplementary materials has changed since initial upload (File S7 was previously File S9 or S10), the contents of this zipped folder remain the same.</p>
R scripts, input and output data for: Season of death, pathogen persistence and wildlife behaviour alter number of anthrax secondary infections from environmental reservoirs
<p>An important part of infectious disease management is predicting factors that influence disease outbreaks, such as <em>R</em>, the number of secondary infections arising from an infected individual. Estimating <em>R</em> is particularly challenging for environmentally transmitted pathogens given time lags between cases and subsequent infections. Here, we calculated <em>R</em> for <em>Bacillus anthracis</em> infections arising from anthrax carcass sites in Etosha National Park, Namibia. Combining host behavioural data, pathogen concentrations, and simulation models, we show that <em>R</em> is spatially and temporally variable, driven by spore concentrations at death, host visitation rates and early preference for foraging at infectious sites. While spores were detected up to a decade after death, most secondary infections occurred within two years. Transmission simulations under scenarios combining site infectiousness and host exposure risk under different environmental conditions led to dramatically different outbreak dynamics, from pathogen extinction (<em>R</em><1) to explosive outbreaks (<em>R</em>>10). These transmission heterogeneities may explain variation in anthrax outbreak dynamics observed globally, and more generally, the critical importance of environmental variation underlying host-pathogens interactions. Notably, our approach allowed us to estimate the lethal dose of a highly virulent pathogen non-invasively from observational studies and epidemiological data, useful when experiments on wildlife are undesirable or impractical.</p>
Secondary Amazon rainforest partially recovers tree cavities suitable for nesting birds in 18–34 years
<p>Passive restoration of secondary forests can partially offset loss of biodiversity following tropical deforestation. Tree cavities, an essential resource for cavity-nesting birds, are usually associated with old forest. We investigated the restoration time for tree cavities suitable for cavity-nesting birds in secondary forest at the Biological Dynamics of Forest Fragments Project (BDFFP) in central Amazonian Brazil. We hypothesized that cavity abundance would increase with forest age, but more rapidly in areas exposed to cutting only, compared to areas where forest was cut and burned. We also hypothesized that cavities would be lower, smaller, and less variable in secondary forest than in old-growth forest, which at the BDFFP is part of a vast lowland forest with no recent history of human disturbance. We used pole-mounted cameras and tree-climbing to survey cavities in 39 plots (each 200 × 40 m) across old-growth forests and 11–34 year-old secondary forests. We used generalized linear models to examine how cavity supply was related to forest age and land-use history (cut only vs cut-and-burn), and principal components analysis to compare cavity characteristics between old-growth and secondary forest. Cavity availability increased with secondary forest age, regardless of land-use history, but the oldest secondary forest (31–34 years) still had fewer cavities (mean ± SE = 9.8 ± 2.2 cavities/ha) than old-growth forest (20.5 ± 4.2 cavities/ha). Moreover, secondary forests lacked cavities that were high and deep, with large entrances – characteristics likely to be important for many species of cavity-nesting birds. Several decades may be necessary to restore cavity supply in secondary Amazonian forests, especially for the largest birds (e.g, forest-falcons and parrots > 190 g). Retention of legacy trees as forest is cleared might help maintain a supply of cavities that could allow earlier recolonization by some species of cavity-nesting birds when cleared areas are abandoned.</p>
Text-fig. 9. Miscellaneous leaves. a: Leaf of Anacardiaceae, UAPC-ALTA S 59513. b: Sapindaceous leaf, BBM-PAL-P000046. c: Twig with compound leaves of Averrhoites affinis, UAPC-ALTA S 67694. d, e: cf. Morus. Finely serrate leaf with actinodromous venation, prominent agrophic veins and strongly percurrent tertiary veins, UAPC-ALTA S 67695. f: Leaf with strongly apically arched upper pairs of secondary veins and entire margins, UAPC-ALTA S 59504. g: Compound leaf, UAPC-ALTA S 59504A. h: Detail of (g) showing one leaflet. i: Incomplete basal part of a lamina with rounded base and entire margin, UAPC-ALTA S 6565. j: Compound leaf with leaflets sessile on a stout rachis, UAPC-ALTA sn. Scale bars: a, c, d, i = 2 cm, b, e–h, j = 1 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 9. Miscellaneous leaves. a: Leaf of Anacardiaceae, UAPC-ALTA S 59513. b: Sapindaceous leaf, BBM-PAL-P000046. c: Twig with compound leaves of Averrhoites affinis, UAPC-ALTA S 67694. d, e: cf. Morus. Finely serrate leaf with actinodromous venation, prominent agrophic veins and strongly percurrent tertiary veins, UAPC-ALTA S 67695. f: Leaf with strongly apically arched upper pairs of secondary veins and entire margins, UAPC-ALTA S 59504. g: Compound leaf, UAPC-ALTA S 59504A. h: Detail of (g) showing one leaflet. i: Incomplete basal part of a lamina with rounded base and entire margin, UAPC-ALTA S 6565. j: Compound leaf with leaflets sessile on a stout rachis, UAPC-ALTA sn. Scale bars: a, c, d, i = 2 cm, b, e–h, j = 1 cm.
Text-fig. 4. Monocots. a, b: Large monocot leaf part and counterpart, UAPC-ALTA S 17955A, B. a: Wide leaf showing entire margin at left. b: Counterpart showing dark wide midrib, and and secondaries parallel to one another, arising at low acute angle. c–e: Monocot leaf with parallel venation. c: Overview of elongate monocot leaf with parallel veins horizontal and linear to oval structures and smaller leaf fragment of same type lacking them (at lower right), UAPC-ALTA S 59491. d: Higher magnification of the smaller fragment with weak cross veins. e: Higher magnification of larger specimen with linear to oval structures between parallel veins. f, g: Monocot leaf with parallel venation. Fig. (f) shows higher magnification and (g) shows overview, BBM-PAL-P000009. Scale bars: a, b = 5 cm, c = 4 cm, d–f = 1 cm, g = 2 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 4. Monocots. a, b: Large monocot leaf part and counterpart, UAPC-ALTA S 17955A, B. a: Wide leaf showing entire margin at left. b: Counterpart showing dark wide midrib, and and secondaries parallel to one another, arising at low acute angle. c–e: Monocot leaf with parallel venation. c: Overview of elongate monocot leaf with parallel veins horizontal and linear to oval structures and smaller leaf fragment of same type lacking them (at lower right), UAPC-ALTA S 59491. d: Higher magnification of the smaller fragment with weak cross veins. e: Higher magnification of larger specimen with linear to oval structures between parallel veins. f, g: Monocot leaf with parallel venation. Fig. (f) shows higher magnification and (g) shows overview, BBM-PAL-P000009. Scale bars: a, b = 5 cm, c = 4 cm, d–f = 1 cm, g = 2 cm.
Supplementary data of article "Domestication has altered gene expression and secondary metabolites in pea seed coat".
<p><strong>Table S1.</strong> Excel- GO_terms_MF_selected_WGCNA_modules.</p> <p><strong>Table S2.</strong> Excel- GO_terms_MF_DEGs_UP_and_DOWN.</p> <p><strong>Table S3.</strong> Excel- GO_terms_MF_DEGs_summary.</p> <p><strong>Table S4.</strong> Excel- List of DEGs involved in flavonoid pathway found in WILD gene set.</p> <p><strong>Table S5.</strong> Protein recoveries calculated for individual pea protein samples. Numbers 1, 2, 3 denote treatment groups corresponding to seed developmental stages (D1, D2 and mature seeds, respectively). Letters a–d denote biological replicates within the treatment groups.</p> <p><strong>Table S6.</strong> Excel- Annotation of proteins differentially expressed in wild and domesticated pea seed coat samples.</p> <p><strong>Table S7.</strong> Primary metabolites identified by spectral similarity library search and/or co-elution with authentic standards in pea seed coats aqua methanolic extracts. Metabolite analysis relied on GC-EI-Q-MS analysis after derivatization of the lyophilized extracts with methoxamine hydrochloride (MOA) and <em>N</em>-methyl-<em>N</em>-(trimethylsilyl)trifluoroacetamide (MSTFA).</p> <p><strong>Table S8.</strong> Primary metabolites detected in the aq. methanolic extracts of mature Cameor seed coats demonstrating statistically significant up- and down-regulation in comparison to those of wild JI261.</p> <p><strong>Table S9.</strong> Primary metabolites of mature JI92 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of wild JI261.</p> <p><strong>Table S10.</strong> Primary metabolites of mature JI1794 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261.</p> <p><strong>Table S11.</strong> Primary metabolites of mature JI64 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261.</p> <p><strong>Table S12.</strong> Mass analyzer settings applied for QqTOF-MS experiments in analysis of seed coat (cell wall) hydrolyzates and reference authentic standards.</p> <p><strong>Table S13.</strong> Cell wall-bound metabolites extracted from the seed coats of wild (JI64, JI1794, JI261) and domesticated (Cameor, JI92) peas upon alkali hydrolysis of corresponding isolated and purified cell wall material.</p> <p><strong>Table S14.</strong> Excel- Coordinates of markers in S-plot obtained from OPLS-DA analysis (FIA-ESI-HRTMS, negative ionization, lock mass uncorrected).</p> <p><strong>Table S15.</strong> List of identified significantly differential metabolites rising during seed coat development.</p> <p><strong>Table S16.</strong> List of identified significantly differential metabolites decreasing during seed coat development (positive ionization mode).</p> <p><strong>Table S17.</strong> List of identified metabolites with significantly higher content in wild compared cultivated genotypes in older developmental stages (D5-6).</p> <p><strong>Table S18.</strong> Excel- Expression of genes encoding enzymes of monolignol pathway in seed coats (SC) and embryos (E) of domesticated (Cameor, JI92 and <em>Pisum abyssinicum</em> PI358617) and wild (JI64, JI1794, JI261) peas over five seed developmental stages (13, 17, 20, 23, 28 DAP, labelled as 1-5). PAL: phenylalanine ammonia-lyase, C4H: cinnamate-4-hydroxylase, 4CL: 4-coumaroyl: CoA ligase, HCT: hydroxycinnamoyl CoA:shikimate hydroxycinnamoyltransferase, COMT: caffeic acid O-methyltransferase, CSE: caffeoyl shikimate esterase, CAD: cinnamyl alcohol dehydrogenase, CCR: cinnamoyl CoA reductase, CCoAMT: caffeoyl CoA-3-methyltransferase, F5H: ferulate-5-hydroxylase</p> <p><strong>Table S19.</strong> Studied metabolites of phenylpropanoid pathway.</p> <p><strong>Table S20.</strong> Instrument settings used in the proteomics LIT-Orbitrap-MS and -MS/MS experiments.</p> <p><strong>Table S21.</strong> Procedures and specific settings for data processing and post-processing of the proteomics data.</p> <p><strong>Table S22.</strong> Gas chromatographic (GC) separation conditions and electron ionization-quadrupole-mass spectrometry (EI-Q-MS) settings for GC-EI-Q-MS analysis of the primary metabolites in pea seed coats.</p> <p><strong>Table S23.</strong> Chromatographic conditions used for UHPLC separation of seed coat (cell wall) hydrolyzates and reference authentic standards.</p> <p><strong>Table S24.</strong> Variable parameters of MS/cIMS/MS measurements.</p> <p><strong>Figure S1.</strong> The dynamics of gene expression between studied developmental stages within all genotypes (a) or among genotypes in particular developmental stages (b).</p> <p><strong>Figure S2.</strong> Twelve representative groups of transcription factors described within 20 gene modules of pea SC. Visualized by Cytoscape 3.9.0.</p> <p><strong>Figure S3.</strong> SDS-PAGE electropherograms of the total protein fractions isolated from the seed coats of JI92 (a, c, e) and JI64 (b, d, f) seeds before and after tryptic hydrolysis. Numbers 1, 2, 3 denote seed developmental stages: DS1, DS2 and mature seeds, respectively. Letters a-d denote biological replicates. The aliquots (10 μg) of samples before hydrolysis (a, b), the incompletely digested aliquots left on filter unit after peptide elution (c, d) and aliquots of tryptic hydrolysates (corresponding to 5 μg of protein), (e, f) were loaded on gels. Inter-gel normalization relied on the total density of the Protein Ladder (PageRuler™ Prestained Protein Ladder #26616, 10–180 kDa) lane (St); the ND (non-digested) sample represents a reference protein not subjected to hydrolysis.</p> <p><strong>Figure S4.</strong> The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups) (c) identified in domesticated JI92 seed coats at developmental stages D1, D2 and D6. The tryptic digests (<em>n</em> =&thinsp;3) obtained from seed coats were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode.</p> <p><strong>Figure S5.</strong> The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups, c) identified in wild pea JI64 seed coats at D1, D2 and D6 stages. The tryptic digests (<em>n</em> =&thinsp;3), obtained from pea seedlings, were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode.</p> <p><strong>Figure S6.</strong> Principal component analysis (PCA) with score plot representation (a) accomplished for seed coat proteins differentially expressed at developmental stages D1 and D2 and in the mature state (D6) and hierarchical clustering with a heatmap representation (b).</p> <p><strong>Figure S7.</strong> Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D1. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of domesticated JI92 and wild JI64, respectively.</p> <p><strong>Figure S8.</strong> Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D2. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of the domesticated JI92 and wild JI64, respectively.</p> <p><strong>Figure S9.</strong> Prediction of sub-cellular localization of the proteins more expressed in the developing seeds of JI92 and JI64 with the BUSCA prediction tool.</p> <p><strong>Figure S10.</strong> Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated Cameor by principal component analysis (PCA).</p> <p><strong>Figure S11.</strong> Representation of the differences in the metabolic profiles of the mature seed coats obtained from the wild JI261 and domesticated Cameor by the t-test with Volcano plot representation (a) and the top 30 differentially abundant metabolites demonstrating the most pronounced differences of corresponding GC-MS signals associated with seed dormancy (b).</p> <p><strong>Figure S12.</strong> Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated JI92 by principal component analysis (PCA) with score plot representation (a) and hierarchical clustering with heatmap representation (b).</p> <p><strong>Figure S13.</strong> Principal component analysis (PCA) illustrating distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261.</p> <p><strong>Figure S14.</strong> Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild pea genotypes, JI64 and JI261(control).</p> <p><strong>Figure S15.</strong> Evaluation of the differences in the patterns of the cell wall-bound metabolites obtained from mature seed coats of wild JI261 and domesticated Cameor: principal component analysis (PCA) with score plot representation (a), hierarchical clustering with heatmap representation (b) and <em>t</em>-test analysis with the Volcano-plot representation (c).</p> <p><strong>Figure S16.</strong> Statistical analysis (<em>t</em>-test with Volcano plot representation) characterizing the differences between the levels of mature seed coat cell wall-bound metabolites of Cameor compared with those of wild JI261.</p> <p><strong>Figure S17.</strong> Principal component analysis (PCA) illustrates the distribution of mature seed coat metabolic profiles of domesticated JI92 and wild JI261.</p> <p><strong>Figure S18.</strong> Principal component analysis (PCA) shows the distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261, control.</p> <p><strong>Figure S19.</strong> Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild genotypes, JI64 and control JI261.</p> <p><strong>Figure S20.</strong> Annotated cell wall-bound metabolites extracted from the seed coats of the dormant wild pea genotype JI261 and the seed coats from several pea genotypes varying in their dormancy (Cameor, JI92, JI64, and JI1794) upon alkali hydrolysis of corresponding isolated and purified cell wall material. </p> <p><strong>Figure S21.</strong> Ion mobility separation of <em>m/z</em> 299.0841.</p> <p><strong>Figure S22.</strong> Ion mobility separation of <em>m/z</em> 701.1907. </p> <p><strong>Figure S23.</strong> Ion mobility separation of <em>m/z</em> 619.1041.</p> <p><strong>Figure S24.</strong> Ion mobility separation of <em>m/z</em> 631.1017.</p> <p><strong>Figure S25.</strong> Ion mobility separation of <em>m/z</em> 641.1139.</p> <p><strong>Figure S26.</strong> Ion mobility separation of <em>m/z</em> 771.1346. </p> <p><strong>Figure S27.</strong> Reconstructed chromatograms of p-hydroxybenzoic and salicylic acids in DS5 of dormant JI64 and domesticated landraces JI92 (LC/HRTMS, negative ionization mode).</p> <p><strong>Figure S28.</strong> Module-trait relationship depiction showing the correlation between expression of the gene modules and the abundance of identified metabolites of the monolignol pathway.</p>
→ Fig. 2. Marellomorph arthropod Mimetaster florestaensis sp. nov. from Tremadocian of Mojotoro Mountains, Salta, Argentina. A–C. CNS-I 133/1-1, part. A. Cephalic shield and spines. Detail of the secondary spines on mediolateral spine (A2). B. View of the imprint of the ventral posterior margin of the cephalic shield. C. Explanatory drawing revealing the most important morphological characters. D. CNS-I 133/1-1´, counterpart showing detail of strong secondary spines on anterolateral spine. Arrows indicate the secondary spines. in A new marrellomorph euarthropod from the Early Ordovician of Argentina
→ Fig. 2. Marellomorph arthropod Mimetaster florestaensis sp. nov. from Tremadocian of Mojotoro Mountains, Salta, Argentina. A–C. CNS-I 133/1-1, part. A. Cephalic shield and spines. Detail of the secondary spines on mediolateral spine (A2). B. View of the imprint of the ventral posterior margin of the cephalic shield. C. Explanatory drawing revealing the most important morphological characters. D. CNS-I 133/1-1´, counterpart showing detail of strong secondary spines on anterolateral spine. Arrows indicate the secondary spines.
Dependence of MeV TOF SIMS secondary molecular ion yield from phthalocyanine blue on primary ion stopping power
<p>Time-of-flight Secondary Ion Mass Spectrometry (TOF SIMS) is a well-established mass spectrometry technique used for the chemical analysis of both organic and inorganic materials. In the last ten years, many advances have been made to improve the yield of secondary molecular ions, especially those desorbed from the surfaces of organic samples. For that, cluster ion beams with keV energies for the excitation were mostly used. Alternatively, single-ion beams with MeV energies can be applied, as done in the present work. It is well known that secondary molecular/ion yield depends strongly on the primary ion stopping power, but the nature of this dependence is not completely clear. Therefore, in the present work secondary ion yield from the phthalocyanine blue (C<sub>32</sub>H<sub>16</sub>CuN<sub>8</sub>, organic pigment) was measured for the various combinations of ion masses, energies and charge states. Measured values were compared with the existing models for ion sputtering. An increase of the secondary yield with the primary ion energy, electronic stopping, velocity and charge state was found for different types of primary ions. Although this general behavior is valid for all primary ions, there is no single parameter that can describe the measured results for all primary ions at once. </p> <p>- measured (calibrated) spectra are uploaded </p> <p> </p> <p> </p>
Imaging of Organic Samples with Megaelectron Volt Time-of-Flight Secondary Ion Mass Spectrometry Capillary Microprobe
<p>Time-of-flight Secondary Ion Mass Spectrometry (TOF SIMS) with MeV primary ions offers a fine balance between secondary ion yield for molecules in the mass range from 100 to 1000 Da and beam spot size, both of which are critical for imaging applications of organic samples. Using conically shaped glass capillaries with an exit diameter of a few micrometers, a high energy heavy primary beam can be collimated to less than 10 μm. In this work, imaging capabilities of such a setup are presented for some organic samples (leucine-evaporated mesh, fly wing section, ink deposited on paper). Lateral resolution measurement and molecular distributions of selected mass peaks are shown. The negative influence of the beam halo, an unavoidable characteristic of primary beam collimation with a conical capillary, is also discussed. A new start trigger for TOF measurements based on the detection of secondary electrons released by the primary ion is presented. This method is applicable for a continuous primary ion beam, and for thick targets that are not transparent to the primary ion beam. The solution preserves the good mass resolution of the thin target setup, where the detection of primary ions with a PIN diode is used for a start trigger, reduces the background, and enables a wide range of samples to be analyzed.</p>
X-ray imaging of 30 year old wine grape wood reveals cumulative impacts of rootstocks on scion secondary growth and harvest index
<p><span></span></p> <p><span>Annual rings from vines in a 30 year old, California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were measured at the beginning and end of the lifetime of the vineyard.</span></p> <p><span>X-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. </span></p> <p><span>Modeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius.</span></p> <p><span>Rootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species.</span></p>
Edge effects and vertical stratification of aerial insectivorous bats across the interface of primary-secondary Amazonian rainforest
<p><span>Edge effects - abiotic and biotic changes associated with habitat boundaries - are key drivers of community change in fragmented landscapes. Their influence is heavily modulated by matrix composition. With over half of the world's tropical forests predicted to become forest edge by the end of the </span><span>century, it is paramount that conservationists gain a better understanding of how tropical biota is impacted by edge gradients. Bats comprise a large fraction of tropical mammalian fauna and are demonstrably sensitive to habitat modification. Yet, </span><span>knowledge about how bat assemblages are affected by edge effects remains scarce</span><span>. Capitalizing on a whole-ecosystem manipulation in the Central Amazon, the aims of this study were to i) assess the consequences of edge effects for twelve aerial insectivorous bat species across the interface of primary and secondary forest and ii) investigate if the activity levels of these species differed between the understory and canopy and if they were modulated by distance from the edge</span><span>. Acoustic surveys were conducted along four 2-km transects each traversing equal parts of primary and ca. 30-year-old secondary forest. Five models were used to assess the changes in the relative activity of forest specialists (three species), flexible forest foragers (three species), and edge foragers (six species). Modelling results revealed no evidence of edge effects, except for forest specialists in the understory. No significant differences in activity were found between the secondary or primary forest but most species exhibited pronounced vertical stratification. Our study highlights that forest specialist bats are more edge-sensitive than both flexible forest and edge foraging bats and suggests that the influence of edge effects on aerial insectivorous bats may exceed 2 km. The absence of pronounced edge effects and the comparable activity levels between primary and old secondary forests indicates that old secondary forest can help ameliorate the consequences of fragmentation on tropical aerial insectivorous bats. </span></p>
Data from: Plant host traits mediated by foliar fungal symbionts and secondary metabolites
<p>Fungal symbionts living inside plant leaves ("endophytes") can vary from beneficial to parasitic, but the mechanisms by which the fungi affect the plant host phenotype remain poorly understood. Chemical interactions are likely the proximal mechanism of interaction between foliar endophytes and the plant, as individual fungal strains are often exploited for their diverse secondary metabolite production. Here, we go beyond single strains to examine commonalities in how 16 fungal endophytes shift plant phenotypic traits such as growth and physiology, and how those relate to plant metabolomics profiles. We inoculated individual fungi on switchgrass, <em>Panicum virgatum</em> L. This created a limited range of plant growth and physiology (2–370% of fungus-free controls on average), but effects of most fungi overlapped, indicating functional similarities in unstressed conditions. Overall plant metabolomics profiles included almost 2000 metabolites, which were broadly correlated with plant traits across all the fungal treatments. Terpenoid-rich samples were associated with larger, more physiologically active plants and phenolic-rich samples were associated with smaller, less active plants. Only 47 metabolites were enriched in plants inoculated with fungi relative to fungus-free controls, and of these, LASSO regression identified 12 metabolites that explained from 14–43% of plant trait variation. Fungal long-chain fatty acids and sterol precursors were positively associated with plant photosynthesis, conductance, and shoot biomass, but negatively associated with survival. The phytohormone gibberellin, in contrast, was negatively associated with plant physiology and biomass. These results can inform ongoing efforts to develop metabolites as crop management tools, either by direct application or via breeding, by identifying how associations with more beneficial components of the microbiome may be affected.</p>
Modelling seasonal dynamics of secondary growth in R
<p>The monitoring of seasonal radial growth of woody plants addresses the ultimate question of when, how, and why trees grow. Assessing the growth dynamics is important to quantify the effect of environmental drivers and understand how woody species will deal with the ongoing climatic changes. One of the crucial steps in the analyses of seasonal radial growth is to model the dynamics of xylem and phloem formation based on increment measurements on samples taken at relatively short intervals during the growing season. The most common approach is the use of the Gompertz equation, while other approaches, such as general additive models (GAMs) and generalised linear models (GLMs), have also been tested in recent years. For the first time, we explored artificial neural networks with Bayesian regularisation algorithm (BRNNs) and show that this method is easy to use, resistant to overfitting, tends to yield s-shaped curves and is therefore suitable for deriving temporal dynamics of secondary tree growth. We propose two data processing algorithms that allow more flexible fits. The main result of our work is the XPSgrowth() function implemented in the radial Tree Growth (rTG) R package, that can be used to evaluate and compare three modelling approaches: BRNN, GAM and the Gompertz function. The newly developed function, tested on intra-seasonal xylem and phloem formation data, has potential applications in many ecological and environmental disciplines where growth is expressed as a function of time. Different approaches were evaluated in terms of prediction error, while fitted curves were visually compared to derive their main characteristics. Our results suggest that there is no single best fitting method, therefore we recommend testing different fitting methods and selection of the optimal one.</p>
Derby database for mapping secondary to primary HMDB identifiers
<p>The data (hmdb_metabolites, released on 17/11/2021) used to create this ID mapping database was downloaded from HMDB (<em>Human Metabolome Database, </em>website URL: https://hmdb.ca/). </p> <p>This database was used for the <a href="https://github.com/tabbassidaloii/BridgeDbDemoBioSB2022">BridgeDb demo at BioSB 2022</a> conference.</p> <p>The scripts used to create this database based on HGNC: https://github.com/tabbassidaloii/create-bridgedb-secondary2primary</p> <p>This work was funded by the <a href="https://fairplus-project.eu/">FAIRplus project</a> (grant agreement no 802750) and <a href="https://www.nwo.nl/en/researchprogrammes/open-science/open-science-fund/open-science-fund-2021-awarded-grants">NWO Open Science Fund</a> (grant no <a href="https://www.nwo.nl/en/projects/203001121">203.001.121</a>).</p>
Derby database for mapping secondary to primary HGNC gene symbols
<p>The datasets (hgnc_complete_set and withdrawn) used to create this ID mapping database were downloaded from HGNC (<em>HUGO Gene Nomenclature Committee at the European Bioinformatics Institute, </em>website URL: https://www.genenames.org/) on 09/05/2022. </p> <p>This database was used for the <a href="https://github.com/tabbassidaloii/BridgeDbDemoBioSB2022">BridgeDb demo at BioSB 2022</a> conference.</p> <p>The scripts used to create this database based on HGNC: https://github.com/tabbassidaloii/create-bridgedb-secondary2primary</p> <p>This work was funded by the <a href="https://fairplus-project.eu/">FAIRplus project</a> (grant agreement no 802750) and <a href="https://www.nwo.nl/en/researchprogrammes/open-science/open-science-fund/open-science-fund-2021-awarded-grants">NWO Open Science Fund</a> (grant no <a href="https://www.nwo.nl/en/projects/203001121">203.001.121</a>).</p>
Riparian buffers provide refugia during secondary forest succession
<p>Aim Secondary forests regenerating from human disturbance are increasingly becoming a predominant forest type in many regions, and they play a significant role in forest community dynamics. Understanding the factors that underlie the variation in species responses during secondary succession is important for understanding community assembly and biodiversity monitoring and management. Because species vary in ecology and behavior, responses to ecosystem change should vary among species. Here, we show that habitat type (riparian, upland), phylogeny, and species traits mediate anuran and lizard probability of occurrence and species richness in pasture and secondary forest. Location Sarapiquí and Osa Peninsula, Costa Rica. Methods We used phylogenetic occupancy models to estimate assemblage-level and species-specific responses to forest succession in 30 chronosequence sites that include pasture, secondary forest regenerating from pasture, and mature forest sites. Results For the majority of species, we found increasing probability of occurrence in upland habitats as forest regenerated from pasture to secondary forest and similar probability of occurrence in riparian habitats across pasture, secondary forest, and mature forest sites. Species' responses to forest stage were phylogenetically correlated, and the trend was especially strong for anuran response to pasture sites. Anurans with lotic larval habitat had a positive occupancy response to pasture upland habitat and anurans with lentic larval habitat had a variable response to different forest stages compared to mature forest.</p>
Secondary Data: Measuring Person-centred Care in German Nursing Homes – Exploring Construct Validity of the Dementia Policy Questionnaire using Adjusted Multiple Correspondence Analysis
<p>This is the secondary data set and R-Code of R statistical software (version 4.0.4) to explore construct validity of the German Dementia Policy Questionnaire using Adjusted Multiple Correspondence Analysis.</p>
MyD88-TLR4-dependent choroid plexus activation precedes perilesional inflammation and secondary brain edema in a mouse model of intracerebral hemorrhage
<p>Supplementary data and code of the article "MyD88-TLR4-dependent choroid plexus activation precedes perilesional inflammation and secondary brain edema in a mouse model of intracerebral hemorrhage".</p>
Text-fig. 3. Juglandaceae. Carya (a–x). Scale bars = 1 cm. a–e: USNM PAL 772346. Micro-CT scan surface rendering. a, b: Lateral, c: apical, d: basal views. e: Virtual equatorial transverse section. f–n: USNM PAL 772347. f: Lateral view, reflected light, showing path of saw cut for transverse section of (i). g: Basal view, reflected light. h: Apical view, micro-CT surface rendering. i: Physical transverse section displaying locule and cellular preservation of parts of wall. j–n: Virtual sections from micro-CT scan data. j: Transverse section at apical 1/3 of nut. Note narrow lacunae (arrows). k: Longitudinal section parallel to primary septum, traversing one of the cotyledon lobes and showing secondary septum at base. l: Longitudinal section in plane at right angles to (k) in plane of primary septum, showing divergent placental bundles arising from base of nut (arrows). m: Equatorial transverse section showing two lobes of locule separated by primary septum. n: Transverse section near base of nut showing primary and secondary septa, creating four basal lobes of locule; note diverging placental bundles (arrows). o–x: USNM PAL 772351. o: Lateral view of broken nut with exposed locule cast, reflected light. p: Same orientation of nut, micro-CT surface rendering. q: Same specimen lateral view, rotated 90° from (p), micro-CT surface rendering. r: Apical view, reflected light. s–x: Virtual sections from micro-CT in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 3. Juglandaceae. Carya (a–x). Scale bars = 1 cm. a–e: USNM PAL 772346. Micro-CT scan surface rendering. a, b: Lateral, c: apical, d: basal views. e: Virtual equatorial transverse section. f–n: USNM PAL 772347. f: Lateral view, reflected light, showing path of saw cut for transverse section of (i). g: Basal view, reflected light. h: Apical view, micro-CT surface rendering. i: Physical transverse section displaying locule and cellular preservation of parts of wall. j–n: Virtual sections from micro-CT scan data. j: Transverse section at apical 1/3 of nut. Note narrow lacunae (arrows). k: Longitudinal section parallel to primary septum, traversing one of the cotyledon lobes and showing secondary septum at base. l: Longitudinal section in plane at right angles to (k) in plane of primary septum, showing divergent placental bundles arising from base of nut (arrows). m: Equatorial transverse section showing two lobes of locule separated by primary septum. n: Transverse section near base of nut showing primary and secondary septa, creating four basal lobes of locule; note diverging placental bundles (arrows). o–x: USNM PAL 772351. o: Lateral view of broken nut with exposed locule cast, reflected light. p: Same orientation of nut, micro-CT surface rendering. q: Same specimen lateral view, rotated 90° from (p), micro-CT surface rendering. r: Apical view, reflected light. s–x: Virtual sections from micro-CT
parallel-fibered bone; A5, osteocyte lacunae with well-preserved canaliculi; B3, osteocyte lacunae lacking canaliculi; B4, B5, growth pattern with preserved residuals of the thick annuli and zones (zo I–III) and thin annuli and zones (zo IV–VII); A6, growth pattern with preserved thin annuli and thick zones (zo I–IV), the dotted line marks the border between the perimedullary region and the cortex. Arrows in A5 and B3 indicate osteocyte lacunae; in B4, B5, and A6 indicate the annuli. Growth pattern in B4 figured on the lateral section side, in B5 and A5 on the ventral side; note the cortex thickness variation between B4 and B5. A1, A3, A4, A6, B1, B4, B5 in polarized light and A2, A5, B2, B3 in normal transmitted light. Abbreviations: an, annulus; ec, erosion cavity; pmr, perimedullary region; pos, primary osteon; sos, secondary osteon; zo, zone. in Palaeohistology helps reveal taxonomic variability in exceptionally large temnospondyl humeri from the Upper Triassic of Krasiejów, SW Poland
parallel-fibered bone; A5, osteocyte lacunae with well-preserved canaliculi; B3, osteocyte lacunae lacking canaliculi; B4, B5, growth pattern with preserved residuals of the thick annuli and zones (zo I–III) and thin annuli and zones (zo IV–VII); A6, growth pattern with preserved thin annuli and thick zones (zo I–IV), the dotted line marks the border between the perimedullary region and the cortex. Arrows in A5 and B3 indicate osteocyte lacunae; in B4, B5, and A6 indicate the annuli. Growth pattern in B4 figured on the lateral section side, in B5 and A5 on the ventral side; note the cortex thickness variation between B4 and B5. A1, A3, A4, A6, B1, B4, B5 in polarized light and A2, A5, B2, B3 in normal transmitted light. Abbreviations: an, annulus; ec, erosion cavity; pmr, perimedullary region; pos, primary osteon; sos, secondary osteon; zo, zone.
ScienceDex guides
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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.