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30,334 results for “Responsiveness”
Probing temperature-responsivity of microgels and its interplay with a solid surface by superresolution microscopy and numerical simulations
<p>This dataset supports the publication ' Probing temperature-responsivity of microgels and its interplay with a solid surface by super resolution microscopy and numerical simulations' published on ACS Nano. DOI: https://doi.org/10.1021/acsnano.2c07569</p>
Dataset for manuscript "Responses to single and multiple temperature-, medium-, and pH-stimuli triggering reversible shape shifts in hydrogel actuators"
<p>The dataset includes data for the article "Responses to single and multiple temperature-, medium-, and pH-stimuli triggering reversible shape shifts in hydrogel actuators", https://doi.org/10.1016/j.matdes.2022.111511</p>
Dataset related to: Sirtuin 3 Deficiency Aggravates Kidney Disease in Response to High-Fat Diet through Lipotoxicity-Induced Mitochondrial Damage
<p>The files contain all the dataset included in the manuscript divided by figures.</p> <p> </p> <p>Abstract: Sirtuin 3 (SIRT3) is the primary mitochondrial deacetylase that controls the antioxidant<br>pathway and energy metabolism. We previously found that renal Sirt3 expression and activity were<br>reduced in mice with type 2 diabetic nephropathy associated with oxidative stress and mitochondrial<br>abnormalities and that a specific SIRT3 activator improved renal damage. SIRT3 is modulated by<br>diet, and to assess whether Sirt3 deficiency aggravates mitochondrial damage and accelerates kidney<br>disease in response to nutrient overloads, wild-type (WT) and Sirt3-/- mice were fed a high-fat-diet<br>(HFD) or standard diet for 8 months. Sirt3-/- mice on HFD exhibited earlier and more severe<br>albuminuria compared to WT mice, accompanied by podocyte dysfunction and glomerular capillary<br>rarefaction. Mesangial matrix expansion, tubular vacuolization and inflammation, associated with<br>enhanced lipid accumulation, were more evident in Sirt3-/- mice. After HFD, kidneys from Sirt3-/-<br>mice showed more oxidative stress than WT mice, mitochondria ultrastructural damage in tubular<br>cells, and a reduction in mitochondrial mass and energy production. Our data demonstrate that Sirt3<br>deficiency renders mice more prone to developing oxidative stress and mitochondrial abnormalities<br>in response to HFD, resulting in more severe kidney diseases, and this suggests that mitochondria<br>protection may be a method to prevent HFD-induced renal injury.</p>
Datasets and Scripts associated with "Transposable elements are associated with the variable response to influenza infection"
<p>Scripts and datasets included here were used for the main analyses in the article "Transposable elements are associated with the variable response to influenza infection" (BioRxiv doi: <a href="https://doi.org/10.1101/2022.05.10.491101">https://doi.org/10.1101/2022.05.10.491101</a>).</p> <p>Study Summary:</p> <p>Influenza A virus (IAV) infections are frequent every year and result in a range of disease severity. Given that the regulation of transposable elements (TEs) contributes to the activation of innate immunity, we wanted to explore their potential role in this variability. Transcriptome profiling in monocyte-derived macrophages from 39 individuals following IAV infection revealed significant inter-individual variation in viral load post-infection. Using ATAC-seq we identified a set of TE families with either enhanced or reduced accessibility upon infection. Of the enhanced families, 15 showed high variability between individuals and had distinct epigenetic profiles. Motif analysis showed an association with known immune regulators (e.g., BATFs, FOSs/JUNs, IRFs, STATs, NFkBs, NFYs, and RELs) in stably enriched TE families and with other factors in variable families, including KRAB-ZNFs. We also observed a strong association between basal TE transcripts and viral load post infection and showed that TEs, and host factors regulating TEs, were predictive of the response. Our findings shed light on the variable transcriptional and epigenetic response to infection and the role TEs and KRAB-ZNFs may play in inter-individual variation in immunity.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Figure 13 in REVIEW Vertebrate origins are informed by larval lampreys (ammocoetes): a response to Miyashita et al., 2021
Figure 13. Yolk-sac hatchling of the Carboniferous lamprey Pipiscius, drawn from the fossil specimen in figure 2 of Miyashita et al. (2021). Its mouth opening, in the oral funnel, is the secondary mouth-opening of adult lampreys. To indicate this, it is coloured blue as in Figure 12C.
Figure 9 in REVIEW Vertebrate origins are informed by larval lampreys (ammocoetes): a response to Miyashita et al., 2021
Figure 9. The three clades of ostracoderm fishes that had bony head shields, with impressions from or on the inner surfaces of these head shields. This shows my interpretations of the specific branchial arches and gill pouches, with yellow marking the first ones. A, heterostracan Protopteraspis (Simopteraspis) primaeva Kiaer, 1928 (after fig. 197A in Stensio, 1958); B, osteostracan Scolenaspis signata Wangsjo, 1952 (after fig. 7 in Janvier, 1985); C, galeaspid Shuyu zhejiangensis, natural endocast (after fig. 1a in Gai et al., 2011).
Figure 8 in REVIEW Vertebrate origins are informed by larval lampreys (ammocoetes): a response to Miyashita et al., 2021
Figure 8. Pharynxes of lampreys and a jawed shark, compared. Frontal sections. A, ammocoete; B, adult lamprey; C, shark; D, an enlarged gill of each. The similarities are emphasized by colour-coding. The blue arrows in (A–C) show the direction of flow of the ventilatory water. In (A) the green skeletal arches are made of mucocartilage and the purple arches consist of typical, cellular cartilage (hyaline). The extrabranchial cartilages of sharks are comparable to the external branchial arches of lampreys. (A) is redrawn from Mallatt & Ridgway (1984), (B) from various sources including Randall (1972), (C) from Mallatt (1997/98) and (D) from Mallatt (1996).
Figure 11 in REVIEW Vertebrate origins are informed by larval lampreys (ammocoetes): a response to Miyashita et al., 2021
Figure 11. Lips and their skeletons seem to be homologous in ammocoete lampreys (A) and various other fishes (B–K). (A1) shows mucocartilage, which contributes to the skeleton of ammocoete lips. The adult lamprey (J), has the largest and most protrusive upper lip, as indicated by the '5 eye diameters' label in part J. For the skeletal elements in the lips, I use simple, generic names that tell the homologies (see the key). For the official names, see Mallatt (1996). These skeletal elements are usually cartilage or fibrous tissue and unlikely to fossilize, so they are best seen in the living animals (A, H–K). However, parts of the lip skeleton were bony – and thus preserved – in the fossil groups (D–G). Especially note the jawed ptyctodont placoderm (G) and the chimaeroid chondrichthyan (H). The ptyctodont elements are officially named prelabial (blue), premaxillary (pink), and premandibular (yellow) bones (Trinajstic et al., 2012). For the lips to have existed in the first jawed vertebrates, I assume that the ptyctodont state is primitive for the 'placoderm' clades, or else that lip cartilages existed in the other placoderm lineages but did not preserve. (A, H, I, and K1) are redrawn from Mallatt (1996); (B) from Mallatt & Chen (2003); (C) from Shu et al. (2003), figure 2.1b in Shu (2003) and Feinberg & Mallatt (2016); (D) from Moy-Thomas & Miles (1971); (E) from Janvier (1985; 1996); (F) from Janvier (2008); (G) from Trinajstic et al. (2012) based on specimens of Materpiscis Long et al., 2008 and Austroptyctodus Long, 1997; (J) from various photos, and (K2) from Oisi et al. (2013b).
Figure 12. The same mouth-opening characterizes all vertebrates except for adult lampreys. A, a in REVIEW Vertebrate origins are informed by larval lampreys (ammocoetes): a response to Miyashita et al., 2021
Figure 12. The same mouth-opening characterizes all vertebrates except for adult lampreys. A, a tunicate, is included as representing the closest relative of vertebrates, with the same mouth structures. Adult lampreys (C) do not have the typical, primary opening, but a secondary one in the oral funnel formed by their protrusive upper lips. The ammocoete lamprey (B) does not have a secondary mouth-opening, so the dashed blue line in (B) just shows the boundary corresponding to the adult lamprey's. Anaspids (E) are reconstructed with a primary mouth, despite the superficial similarity of their snout to that of adult lamprey; the anaspid snout does not project far enough forward (only three eye-diameters forward as opposed to five for the lamprey in Fig. 11J). Hagfish (D) also differ from adult lampreys in having the primary mouth-opening. In osteostracans (G) and galeaspids (H), the lips have not grown forward to form a secondary mouth, but have simply lain on the ground. I call their mouth openings 'pseudo-secondary.' Most of the pictures are retooled from Figures 10 and 11, but three are new: (A) is redrawn from Mallatt (2009), (E1) from Janvier (1996) and (E2) from Sansom et al. (2010).
Figure 10 in REVIEW Vertebrate origins are informed by larval lampreys (ammocoetes): a response to Miyashita et al., 2021
Figure 10. Internal anatomy of the mouth, lip, and pharynx regions of various vertebrates from lateral view, mostly as midsagittal sections. A–C, living jawless fishes; D, schematic drawing of a jawed fish; E–G, jawless fossil ostracoderms. F, the osteostracan, shows two alternate reconstructions of the unpreserved parts of the branchial arches (F2, F3) with (F3) being the one I favour. (A and D) are redrawn from Mallatt (1996); (C) from figure 98 in Marinelli & Strenger (1956); (E) is Poraspis Kiaer, 1930 and (G) is Duyunolepis Pan & Wang, 1982 (?), both from Janvier (2008); (F1) is Norselaspis Janvier, 1981 from figure 51A in Janvier (1985); and (F2) is Scolenaspis from figure 14B in Janvier (1985) and figure 3 in Long et al. (2010).
Figure 5 in REVIEW Vertebrate origins are informed by larval lampreys (ammocoetes): a response to Miyashita et al., 2021
Figure 5. Phylogenetic tree of early vertebrate relationships, condensed from that in the target article of Miyashita et al. (2021). Parsimony consensus tree based on 167 morphological characters. 'FYA,' 'FY,' etc. mark my interpretation of how the clades fed: see the Key. The relationships among the lampreys might alternatively be those in Figure 7B. Antiarcha and Arthrodira are two groups of jawed placoderms, which come out as paraphyletic in the tree.
Figure 6. Anaspids, a in REVIEW Vertebrate origins are informed by larval lampreys (ammocoetes): a response to Miyashita et al., 2021
Figure 6. Anaspids, a clade of fossil jawless fishes. A, labels some lamprey-like features. Redrawn from Janvier (1996) and Sansom et al. (2010).
Figure 4 in REVIEW Vertebrate origins are informed by larval lampreys (ammocoetes): a response to Miyashita et al., 2021
Figure 4. Fossil stem vertebrate from the Cambrian Period, the jawless fish Metaspriggina. Just of few of its many myomeres are shown. Inset is a dorsal view of the head. Redrawn from Rival et al. (2021) and Conway Morris & Caron (2014).
Figure 7 in REVIEW Vertebrate origins are informed by larval lampreys (ammocoetes): a response to Miyashita et al., 2021
Figure 7. Alternate trees of the relationships among lampreys. A, the relations calculated by Miyashita et al.; B, another possibility that was not tested nor refuted, but which is consistent with an ancestral filter-feeding larva. Abbreviation: DevCarb, Devonian and Carboniferous.
Figure 1 in REVIEW Vertebrate origins are informed by larval lampreys (ammocoetes): a response to Miyashita et al., 2021
Figure 1. Lampreys and the cephalochordate, amphioxus. A1–A4, the four genera of Paleozoic fossil lampreys considered by Miyashita et al. (2021); A5, Carboniferous fossil lamprey illustrated in Janvier (1996). B, modern adult lamprey (parasitic). C, ammocoete larval lamprey showing just a few of its many myomeres. D, amphioxus adult. E, amphioxus larva. (A1) was redrawn and modified from Gess et al. (2006), (A2) from news.uchicago.edu, (A3) from Bardack & Richardson (1977), (A4) from Bardack & Zangerl (1971), (A5) from Janvier (1996) and (C–E) from Feinberg & Mallatt (2016).
Figure 2 in REVIEW Vertebrate origins are informed by larval lampreys (ammocoetes): a response to Miyashita et al., 2021
Figure 2. Shared pharyngeal structures that are central to filter feeding in both ammocoetes and amphioxus, drawn in a composite pharynx. Lateral view with rostral to the right. These feeding structures are dominated by ciliary tracts and an endostyle gland. Tunicates largely show this same pattern. The ammocoete endostyle is more complex than the simple troughs present in amphioxus and tunicates, as drawn at bottom. The directions in which cilia move the food-laden mucus, as indicated by arrows, are the same in all three taxa. Note that the lamprey gill-seam tracts are located laterally, but they initially develop medially – in the same place as the branchial-bar tracts in amphioxus. After figure 1 in Mallatt (1984a), figure 1F in Mallatt (2009) and figure 1 in Barrington & Sage (1972).
Codes in R for spatial statistics analysis, ecological response models and spatial distribution models
<p>In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg <em>et al.</em>, 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022).</p> <p>It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter <em>et al.</em>, 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (<em>e.g.</em>, Naoki <em>et al.</em>, 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running:</p> <p>In the first instance, we generated the background points and extracted the values of the variables (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code2_Extract_values_DWp_SC.R?versionId=c1ea0c61-53fe-4f95-ab88-0c1cb28399cb">Code2_Extract_values_DWp_SC.R</a>). Barbet-Massin <em>et al. </em>(2012) recommend the use of 10,000 background points when using regression methods (<em>e.g.</em>, Generalized Linear Model) or distance-based models (<em>e.g.</em>, DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.). Then, we extracted the values of predictor variables (<em>e.g.</em>, bioclimatic, topographic, demographic, habitat) in function of presence and background points (<em>e.g.</em>, Hijmans and Elith, 2017).</p> <p>Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009).</p> <p>After that, we ran the code for the GBM method (Gradient Boost Machine; <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code3_GBM_Relative_contribution.R?versionId=1656bbae-66aa-409e-bb91-d8007dee8f95">Code3_GBM_Relative_contribution.R</a> and <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code4_Relative_contribution.R?versionId=0e1d9352-e6b2-43da-984b-d6853a914258">Code4_Relative_contribution.R</a>), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (<em>e.g.</em>, Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable.</p> <p>Subsequently, the correlation of the variables is run by Pearson's method (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code5_Pearson_Correlation.R?versionId=275f8dd4-b056-44d2-bfe5-f6264bc3298b">Code5_Pearson_Correlation.R</a>) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (<em>e.g.</em>, Awan <em>et al.</em>, 2021).</p> <p>Once the above codes were run, we uploaded the same subgroups (<em>i.e.</em>, presence and background groups with 75% training and 25% testing) (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code6_Presence&backgrounds.R?versionId=d797b528-782f-4a19-bd61-cfb197f38513">Code6_Presence&backgrounds.R</a>) for the GLM method code (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code7_GLM_model.R?versionId=e4aca276-d601-49ec-a62c-a9223b05a7ed">Code7_GLM_model.R</a>). Here, we first ran the GLM models per variable to obtain the <em>p</em>-significance value of each variable (alpha ≤ 0.05); we selected the value one (<em>i.e.</em>, presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (<em>e.g.</em>, Fielding and Bell, 1997; Allouche <em>et al.</em>, 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included.</p> <p>On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017).</p> <p>Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission).</p> <table align="center"> <tbody> <tr> <td> <p> </p> </td> <td> <p>Validation set</p> </td> </tr> <tr> <td> <p>Model</p> </td> <td> <p>True</p> </td> <td> <p>False</p> </td> </tr> <tr> <td> <p>Presence</p> </td> <td> <p>A</p> </td> <td> <p>B</p> </td> </tr> <tr> <td> <p>Background</p> </td> <td> <p>C</p> </td> <td> <p>D</p> </td> </tr> </tbody> </table> <p>We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche <em>et al.</em>, 2006).</p> <p>The last code (<em>i.e.</em>, <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code8_DOMAIN_SuitHab_model.R?versionId=d951a8f2-d3a4-4804-b862-1b2762061876">Code8_DOMAIN_SuitHab_model.R</a>) is for species distribution modelling using the DOMAIN algorithm (Carpenter <em>et al.</em>, 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model.</p> <p>Regarding the model evaluation and estimation, we selected the following estimators:</p> <p>1) partial ROC, which evaluates the approach between the curves of positive (<em>i.e.</em>, correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020).</p> <p>2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long <em>et al.</em>, 1988).</p>
Postdiction Competition - Sera.ta - Seismic Response of Masonry Cross Vaults: Shaking table tests and numerical validations
<p>Once the blind prediction has been completed (DOI 10.5281/zenodo.7624666), new data are given to the research groups to perform the post diction simulations. The main objectives are:</p> <ul> <li>to calibrate the numerical model based on the dynamic properties;</li> <li>to emprove the capability of different modelling/analysis approaches and better justify the numerical assumptions.</li> </ul> <p>Below you can find the data set for the post diction analysis namely:</p> <ul> <li>Unstrengthened specimen results: </li> <li>Strengthened specimen results: </li> <li>Results of mix mortars used for the TRM technique:</li> </ul> <p> </p>
AHEAD2020 WP9.8 Soft Proton Response Matrix
<p>Low energy (< 300 keV) protons entering the field of view of the XMM-Newton telescope and scattering with the mirror surface are observed in the form of a sudden increase in the background level. Such flaring events, affecting about 30-40% of the XMM-Newton observing time, can hardly be disentangled from true X-ray events and cannot be rejected on board. All future high throughput gracing incidence X-ray telescopes operating outside the radiation belts (e.g. ATHENA) are potentially affected by soft proton induced contamination that must be foreseen and limited since the design phase. On the other side, a clear description of the interaction model would link the observed soft proton spectra by XMM-Newton to the ones hitting the telescope pupil, mapping the low energy particle environment along its orbit.</p> <p>Thanks to the latest validation studies on the physics models describing the reflection process of protons at grazing angles, we build a proton response matrix for the XMM-Newton and ATHENA missions, describing the effective area and energy redistribution of protons entering the mirror aperture. The simulation pipeline comprises two independent simulation frameworks for the X-ray optics reflectivity, based on ray-tracing and Geant4, and a Geant4 simulation for the proton transmission efficiency caused by the combination of optical filters, on-chip electrodes and the detection depletion regions, requiring a detailed mass model of the focal plane assemblies.</p> <p>The response matrix for protons will allow a better understanding of the proton radiation environment, with the aim of modeling the in-flight non X-ray background of current and future X-ray focusing telescopes. The XMM-Newton matrix will be used to analyze the mean energy spectra of the background flares, converting the mission into a “proton telescope”, while characterizing its particle background. The matrices for the ATHENA telescope will allow for a fast evaluation of the soft proton induced background for any input population, driving the design of shielding solutions.</p> <p>The response matrix is formatted according to the NASA OGIP (Office of Guest Investigators Program) calibration database (caldb) format, and it consists of an RMF and ARF file in FITS (Flexible Image Transport System) format. X-ray data analysis tools available to the X-ray astronomy community such as Xspec and SPEX can be used to simulate or analyse the soft proton-induced background spectra.</p> <p>The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the AHEAD2020 project (grant agreement n. 871158).</p>
Greater flowering and response to flooding in Lythrum virgatum than L. salicaria (purple loosestrife)
<p>Newly introduced trait diversity can spur rapid evolution and facilitate local adaptation in the introduced plant <em>Lythrum</em> <em>salicaria</em>. The horticultural plant <em>L</em>. <em>virgatum</em> might further introduce meaningful trait variation by escaping into established L. salicaria populations or by hybridizing with <em>L</em>. <em>salicaria</em>. Although many experiments have focused on <em>L. salicaria</em> genotypes, relatively little is known about <em>L. virgatum</em> ecology. We used a greenhouse common garden to compare traits and flood response of <em>L. salicaria</em> and <em>L</em>. <em>virgatum</em> collected from two sources each in their native range. We tested the hypotheses that these two wetland taxa have comparable responses to flooding (inundation), and that flood tolerance correlated to higher fitness. Flooding produced stronger stress responses in <em>L. virgatum</em>. Compared to <em>L. salicaria</em>, <em>L. virgatum</em> shifted more aboveground allocation away from reproduction, decreased inflorescence biomass by 40% more, and produced 7% more stem aerenchymatous phellum, a specialized tissue that maintains aeration. Despite these more pronounced responses to flooding stress, <em>L. virgatum</em> had higher fitness (inflorescence biomass and reproductive allocation) than <em>L. salicaria</em>. Overall, <em>L. virgatum</em> differed from <em>L</em>. <em>salicaria</em> in functionally important ways. <em>Lythrum virgatum</em> persisted under flooding and produced more reproductive biomass than <em>L. salicaria</em> under both flooded and non-flooded conditions. However, inundation stressed <em>L. virgatum</em> more than <em>L. salicaria</em>. <em>Lythrum virgatum</em> is likely able to establish in the wetland habitats in which <em>L. salicaria</em> prevails but may possess broader habitat tolerances.</p>
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.