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440 results for “Combined analysis”
The WWU DUNEuro reference data set for combined EEG/MEG source analysis
<p>The provided dataset consists of two high-quality realistic head models and combined EEG/MEG data which can be used for state-of-the-art methods in brain research, such as modern finite element methods (FEM) to compute the EEG/MEG forward problems using the software toolbox DUNEuro (http://duneuro.org).</p> <p>A combined EEG/MEG dataset from a somatosensory experiment is provided (<strong>sep_sef.zip</strong>): Somatosensory evoked potentials (SEP) and fields (SEF) were elicited by stimulating the median nerve at the wrist of the right arm with monophasic square-wave electrical pulses with 0.5 ms duration. A random stimulus onset asynchrony between 350 and 450 ms was used and the strength was adjusted to invoke a clear movement of the thumb. The duration of the experiment was 10 minutes for a measurement of 1200 trials and data was acquired with a sampling rate of 1200 Hz and online low pass filtered at 300 Hz. An artifact reduction was achieved by reversing the polarity of the stimulation during the second half of the measurement. A 74-channel EEG (EASYCAP GmbH, Herrsching, Germany), for which the electrode positions were digitized using a Polhemus device (FASTRAK, Polhemus Incorporated, Colchester, Vermont, U.S.A.), and a whole-head MEG with 275 axial gradiometers and 29 reference coils (OMEGA2005, VSM MedTech Ltd., Canada) were used in the measurement.</p> <p>Ethics Statement: One healthy subject (49 years, male) participated in this study. The subject had no history of psychiatric or neurological disorders and had given written informed consent before the experiment. All procedures had been approved by the ethics committee of the University of Erlangen, Faculty of Medicine on 10.05.2011 (Ref. No. 4453).</p> <p>Additionally, two different advanced realistic head models are supplied, which both use a six-compartment segmentation from T1/T2-MRI of the test subject. They differentiate between scalp, skull compacta, skull spongiosa, cerebrospinal fluid (CSF) and gray and white matter tissue. One head model is a tetrahedral volumetric mesh (<strong>realistic_tet_mesh_6c.msh</strong>), while the other provides the geometric information by level-sets for each tissue boundary (<strong>realistic_levelsets_6c.zip</strong>). </p> <p>A detailed description of the construction of the tetrahedral mesh can be found <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25272">here</a> (subsection 2.3), the main steps are presented in the following. First, the MR images were co-registered and resampled so that the voxels of the anatomical data are cubic. Furthermore, the images were cut sufficiently below the skull of the participant. Subsequently, the segmentation of the T1w and T2w was performed in order to create six volumetric masks representing the six tissue compartments. The brain compartment was segmented via the <a href="http://surfer.nmr.mgh.harvard.edu">FreeSurfer</a> software. The remaining preprocessing and creation of the volumetric masks was entirely performed via routines available in <a href="https://www.fieldtriptoolbox.org/">FieldTrip</a>. In particular, the scalp and skull segmentations were done via the <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/">spm12</a> software, embedded in FieldTrip. Once the masks were assembled, a volumetric tetrahedral mesh was created using the <a href="https://doc.cgal.org/Manual/3.5/doc_html/cgal_manual/Mesh_3/Chapter_main.html">CGAL</a> software embedded in <a href="http://iso2mesh.sourceforge.net/cgi-bin/index.cgi">iso2mesh</a>, resulting in 885,214 nodes and 5,335,615 tetrahedrons. The mesh is provided in <a href="https://gmsh.info">gmsh</a> format, including information about the node positions, elements defined by their node indices, and labels for each element indicating the tissue compartment.</p> <p>For the construction of the unfitted head model, a six-compartment voxel segmentation was constructed based on the T1- and T2-weighted MR images, distinguishing between skin, skull compacta and spongiosa, CSF, gray and white matter using <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/">SPM12</a> via <a href="https://www.fieldtriptoolbox.org/">Fieldtrip</a>, <a href="https://fsl.fmrib.ox.ac.uk/fsl">FSL</a> and internal MATLAB routines. Surfaces were extracted from this voxel segmentation to distinguish between the different tissue compartments. In order to smooth the surfaces while sustaining the available information from the segmentation, we applied an anti-aliasing algorithm created for binary voxel images presented in (<a href="https://doi.org/10.1145/353888.353893">Whitaker, 2000</a>). The resulting smoothed surfaces are represented as discrete level-set functions, i.e., by <span class="math-tex">\(N^3\)</span>-dimensional arrays (<span class="math-tex">\(N\)</span>=257), the value on each node indicates the signed distance to the respective surface.</p>
Cost-effectiveness analysis of cetuximab combined with chemotherapy as a first-line treatment for RAS wild-type metastatic colorectal cancer patients based on the TAILOR trial
<p><b>Objectives</b> Cetuximab plus leucovorin, fluorouracil, and oxaliplatin (FOLFOX-4) is superior to FOLFOX-4 alone as a first-line treatment for patients with RAS wild-type metastatic colorectal cancer (wt mCRC), with significantly improved survival benefit by TAILOR, an open-label, randomized, multicentre, phase III trial. Nevertheless, the cost-effectiveness of these two regimens remains uncertain. The following study aims to determine whether cetuximab combined with FOLFOX-4 is a cost-effective strategy for specific RAS wt mCRC patients in China.</p> <p><b>Design</b> A combined decision tree and Markov model with three health states (stable, progressive and dead) was constructed to simulate a hypothetical cohort of patients with RAS wt mCRC. The health outcomes and utility scores were derived from the TAILOR trial and previously published sources, respectively. Costs were calculated with reference to the Chinese societal perspective. A lifetime horizon was used. Univariate and probabilistic sensitivity analyses were carried out to test the robustness of the model results.</p> <p><b>Participants</b> The included patients were newly diagnosed Chinese patients with fully RAS wt mCRC. <b>Interventions</b> Either cetuximab plus FOLFOX-4 or FOLFOX-4 alone as a first-line treatment.</p> <p><b>Main outcome measures</b> The primary outcomes are costs, quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs).</p> <p><b>Results</b> Baseline analysis showed that the addition of cetuximab increased the QALYs by 0.383, while an increase of $62,947 was observed in relation to FOLFOX-4 chemotherapy. This led to an incremental cost-effectiveness ratio (ICER) of $164,044/QALY. Sensitivity analysis showed that across the wide variation in parameters, the ICER exceeded the willingness-to-pay threshold of $28,106/QALY, which was three times the per capita GDP in China.</p> <p><b>Conclusions</b> Despite the survival benefit, cetuximab combined with FOLFOX-4 is not a cost-effective treatment for the first line treatment of patients with RAS wt mCRC in China.</p>
Data from: A combined mesowear analysis of Mexican Bison antiquus shows a generalist diet with geographical variation
Bison antiquus was one of the largest and most widely distributed megafaunal species during the Late Pleistocene in North America, giving rise to the modern plains bison in the middle Holocene. Despite the importance of the ancient bison, little is known about its feeding ecology. We employed a combination of extended mesowear, and mesowear III to infer the diet preference and habitat use of three Mexican samples of B. antiquus. Two northern samples from the Transmexican Volcanic Belt morphotectonic Province: La Piedad-Santa Ana and La Cinta-Portalitos, as well as one southern sample from the Sierra Madre del Sur morphotectonic province: Viko Vijin. We found that the northern Mexican samples were primarily non-strict grazers, while the southern sample displays a pattern consistent with mixer feeder habits. This suggests variability among the diets of these bison samples, caused by different paleoenvironments. This evidence complements the paleoenvironmental reconstructions in the studied localities; for the northern samples, open prairies composed of patches of woodland or shrubland and for the southern locality a fluvial floodplain with short-lived vegetation. In both scenarios, grasses (Poaceae) were non-dominant. The dietary habits of our samples of ancient bison in Mexico are the southernmost dietary inference for the species in North America and expand our knowledge of the dietary habits of Bison antiquus during the late Pleistocene.
Data from: Combined effects of natural enemies and competition for resources on a forest defoliator: a theoretical and empirical analysis
Explanations for the dynamics of insect outbreaks often focus on natural enemies, on the grounds that parasitoid and pathogen attack rates are high during outbreaks. While natural enemy models can successfully reproduce outbreak cycles, experiments have repeatedly demonstrated the importance of resource quality and abundance. Experiments, however, are rarely invoked in modeling studies. Here we combine mechanistic models, observational data, and field experiments to quantify the roles of parasitoid attacks and resource competition on the jack pine budworm, Choristoneura pinus. By fitting models to a combination of observational and experimental data, we show that parasitoid attacks are the main source of larval budworm mortality at low and intermediate budworm densities, but that resource competition is the main source of mortality at high densities. Our results further show that the effects of resource competition become more severe with increasing host tree age, and that the effects of parasitoids are moderated by strong competition between parasitoids for hosts. Allowing for these effects in a model of insect outbreaks leads to realistic outbreak cycles, while a host-parasitoid model without resource competition produces an unrealistic stable equilibrium. The effects of resource competition are modulated by tree age, which in turn depends on fire regimes. Our model therefore suggests that increases in fire frequency due to climate change may interact in complex ways with budworm outbreaks. Our work shows that resource competition can be as important as natural enemies in modulating insect outbreaks, while demonstrating the usefulness of high-performance computing in experimental field ecology.
Data from: Combining micro-volume isotope analysis and numerical simulation to reproduce fish migration history
1. Tracking the movement of migratory fish is of great importance for efficient conservation, although this has been technically difficult to achieve in small fish to which artificial tags cannot be attached. 2. We show that migration history can be reproduced by combining high-resolution otolith stable oxygen isotope ratio (δ18O) analysis and numerical simulation. 3. High-precision micro-milling and micro-volume carbonate analysing systems had the remarkable capability of extracting the otolith δ18O profiles with 10–30 days resolution. Furthermore, reasonable movements were reproduced by searching the routes consistent with the otolith δ18O profile, using an individual-based model with random swimming behaviour. 4. This method will be a valuable alternative to tagging and electronic loggers for revealing migration routes in early life stages, thereby providing crucial information to understand population structures and the environmental cause of recruitment variabilities, and to validate and improve fish movement models.
Data and code for Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques
<p>Hydrological data (including daily water levels, flow velocities, and streamflow discharges) from two hydrological stations, the Hankou Station in the Yangtze River (YR) and the Hanchuan Station in the Han River (HR), were obtained from Hubei Province Hydrology and Water Resources Center.</p> <p>Water quality data (i.e., total nitrogen (TOTN), total phosphorus (TOTP), and water temperature in the Han River) and algae densities at three sections (Baihezui, Qinduankou and Zongguan) were acquired from the Yangtze River Basin Ecological and Environmental Supervision Authority. </p> <p><span>The R script(s) for machine learning models can also be found at <a href="../api/records/10901736/draft/files/Code%20for%20machine%20learning%20classification%20model.R/content" target="_blank" rel="noopener noreferrer">Code for machine learning classification model.R</a>.</span></p> <p> </p>
Integrative taxonomic analysis to reveal the species status of Bombus flavidus, combining COI and nuclear sequencing, wing morphometrics and secretions used for mate attraction as well as patterns of color polymorphism
<p>Bumble bees, due to their morphological monotony and color diversity, have presented difficulties with species delimitation. Recent bumble bee declines have made it ever more imperative to resolve the status of species to address conservation concerns. Some of the taxa found to be most threatened are the often-rare socially parasitic bumble bees, which have additional trophic requirements. Among the socially parasitic bumble bees,<i> Bombus flavidus</i> Eversmann has contentious species status. While multiple separate species allied with <i>Bombus flavidus</i> have been suggested, until recently, recognition of two species, a Nearctic <i>Bombus fernaldae</i> (Franklin) and Palearctic <i>B. flavidus,</i> was favoured. Limited genetic data, however, suggested that even these could be a single widespread species, <i>B. flavidus</i>. We addressed the species status of this lineage using an integrative taxonomic approach, combining <i>COI</i> and nuclear sequencing, wing morphometrics and secretions used for mate attraction. We also explore patterns of color polymorphism that have previously confounded taxonomy in this lineage. Our results support the conspecific status of <i>Bombus fernaldae</i> and <i>Bombus flavidus,</i> however, sampling specimens from across the range of these two taxa revealed a distinct population within this broader species confined to eastern North America. This makes the distribution of the social parasite <i>B. flavidus</i> the broadest of any bumble bee, broader than the known distribution of any non-parasitic bumble bee species. Analysis of color phenotypes revealed that color polymorphisms are retained across the range of the species, but may be influenced by local mimicry complexes. Following these results, <i>Bombus flavidus</i> Eversmann, 1852<i> </i>is synonymized with <i>Bombus fernaldae </i>(Franklin, 1911) <b>syn. nov.</b> and a subspecific status, <i>Bombus flavidus </i><i>appalachiensis</i> <b>ssp. nov.</b>, is assigned to the distinct lineage ranging from the Appalachians to the eastern boreal regions of the United States and far southeastern Canada.</p>
Supplementary material 1 from: Kotov S, Gontova T, Kononenko N, Chernyavski E, Chikitkina V (2022) Phytochemical analysis and anti-allergic activity of a combined herbal medicine based on bur-marigold, calendula and hawthorn. Pharmacia 69(1): 237-247. https://doi.org/10.3897/pharmacia.69.e77624
HPLC results for dry extracts of the bur-marigold herb, calendula flowers, hawthorn leafand flowers and combined extract
Replication Package - How Do Requirements Evolve During Elicitation? An Empirical Study Combining Interviews and App Store Analysis
<p>This is the replication package for the paper titled "How Do Requirements Evolve During</p> <p>Elicitation? An Empirical Study Combining Interviews and App Store Analysis", by Alessio Ferrari, Paola Spoletini and Sourav Debnath.</p> <p> </p> <p>The package contains the following folders and files. </p> <p> </p> <p>**<strong>/Experiment Material</strong>**</p> <p>This folder contains the material used for the experiment, and provided to the participants.</p> <p>In particular, it includes the following files:</p> <p> </p> <p>- Happy CampingTM_briefdescription.pdf/docx: brief description of the product for which requirements need to be elicited</p> <p>- Hw_description.pdf/docx: desciption of the tasks to be performed by the participants</p> <p>- Modeling_Intro_Slides.pdf: introductory slides to modelling for requirements engineering</p> <p>- Self-assessment Questionnaire.pdf: first questionnaire to self-assess the mistakes, from the SaPeer method (https://doi.org/10.1007/s00766-020-00334-0) </p> <p>- Self-assessment Questionnaire (Second Interview).pdf: second questionnare to self-assess the mistakes, from the Sapeer method</p> <p> </p> <p>**<strong>/R-analysis</strong>**</p> <p> </p> <p>This is a folder containing all the R implementations of the the statistical tests included in the paper, together with the source .csv file used to produce the results. Each R file has the same title as the associated .csv file. The titles of the files reflect the RQs as they appear in the paper. The association between R files and Tables in the paper is as follows:</p> <p> </p> <p>- RQ1-1-analyse-story-rates.R: Tabe 1, user story rates </p> <p>- RQ1-1-analyse-role-rates.R: Table 1, role rates</p> <p>- RQ1-2-analyse-story-category-phase-1.R: Table 3, user story category rates in phase 1 compared to original rates</p> <p>- RQ1-2-analyse-role-category-phase-1.R: Table 5, role category rates in phase 1 compared to original rates</p> <p>- RQ2.1-analysis-app-store-rates-phase-2.R: Table 8, user story and role rates in phase 2</p> <p>- RQ2.2-analysis-percent-three-CAT-groups-ph1-ph2.R: Table 9, comparison of the categories of user stories in phase 1 and 2</p> <p>- RQ2.2-analysis-percent-two-CAT-roles-ph1-ph2.R: Table 10, comparison of the categories of roles in phase 1 and 2. </p> <p> </p> <p>The .csv files used for statistical tests are also used to produce boxplots. The association betwee boxplot figures and files is as follows. </p> <p> </p> <p>- RQ1-1-story-rates.csv: Figure 4 </p> <p>- RQ1-1-role-rates.csv: Figure 5</p> <p>- RQ1-2-categories-phase-1.csv: Figure 8</p> <p>- RQ1-2-role-category-phase-1.csv: Figure 9</p> <p>- RQ2-1-user-story-and-roles-phase-2.csv: Figure 13</p> <p>- RQ2.2-percent-three-CAT-groups-ph1-ph2.csv: Figure 14</p> <p>- RQ2.2-percent-two-CAT-roles-ph1-ph2.csv: Figure 17</p> <p>- IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv: Figure 15</p> <p>- IMG-only-RQ2.2-frequent-roles.csv: Figure 18</p> <p> </p> <p>NOTE: The last two .csv files do not have an associated statistical tests, but are used solely to produce boxplots.</p> <p> </p> <p>**<strong>/Data-Analysis</strong>**</p> <p> </p> <p>This folder contains all the data used to answer the research questions. </p> <p> </p> <p>**<strong>RQ1.xlsx</strong>**: includes all the data associated to RQ1 subquestions, two tabs for each subquestion (one for user stories and one for roles). The names of the tabs are self-explanatory of their content.</p> <p> </p> <p>**<strong>RQ2.1.xlsx</strong>**: includes all the data for the RQ1.1 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: for each category of user story, and for each analyst, there are two lines. </p> <p>The first one reports the number of user stories in that category for phase 1, and the second one reports the</p> <p>number of user stories in that category for phase 2, considering the specific analyst. </p> <p> </p> <p>* Data Source-role: for each category of role, and for each analyst, there are two lines. </p> <p>The first one reports the number of user stories in that role for phase 1, and the second one reports the</p> <p>number of user stories in that role for phase 2, considering the specific analyst. </p> <p> </p> <p>* RQ2.1 rates: reports the final rates for RQ2.1. </p> <p>NOTE: The other tabs are used to support the computation of the final rates.</p> <p> </p> <p>**<strong>RQ2.2.xlsx</strong>**: includes all the data for the RQ2.2 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Data Source-role: same as RQ2.1.xlsx</p> <p> </p> <p>* RQ2.2-category-group: comparison between groups of categories in the different phases, used to produce Figure 14</p> <p> </p> <p>* RQ2.2-role-group: comparison between role groups in the different phases, used to produce Figure 17</p> <p> </p> <p>* RQ2.2-specific-roles-diff: difference between specific roles, used to produce Figure 18</p> <p> </p> <p>**<strong>NOTE:</strong>** the other tabs are used to support the computation of the values reported in the tabs above. </p> <p> </p> <p>**<strong>RQ2.2-single-US-category.xlsx</strong>**: includes the data for the RQ2.2 subquestion associated to single categories of user stories.</p> <p>A separate tab is used given the complexity of the computations. </p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Totals: total number of user stories for each analyst in phase 1 and phase 2</p> <p> </p> <p>* Results-Rate-Comparison: difference between rates of user stories in phase 1 and phase 2, used to produce the file</p> <p>"img/IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv", which is in turn used to produce Figure 15</p> <p> </p> <p>* Results-Analysts: number of analysts using each novel category produced in phase 2, used to produce Figure 16.</p> <p>NOTE: the other tabs are used to support the computation of the values reported in the tabs above. </p> <p> </p> <p>**<strong>RQ2.3.xlsx</strong>**: includes the data for the RQ2.3 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Data Source-role: same as RQ2.1.xlsx</p> <p> </p> <p>* RQ2.3-categories: novel categories produced in phase 2, used to produce Figure 19</p> <p> </p> <p>* RQ2-3-most-frequent-categories: most frequent novel categories</p> <p> </p> <p>**<strong>/Raw-Data-Phase-I</strong>**</p> <p>The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx, plus the file of the original user stories with annotations (original-us.xlsx). Each file contains two tabs:</p> <p> </p> <p>- Evaluation: includes the annotation of the user stories as existing user story in the original categories (annotated with "E"), novel user story in a certain category (refinement, annotated with "N"), and novel user story in novel category (Name of the category in column "New Feature"). **<strong>NOTE 1:</strong>** It should be noticed that in the paper the case "refinement" is said to be annotated with "R" (instead of "N", as in the files) to make the paper clearer and easy to read. </p> <p> </p> <p>- Roles: roles used in the user stories, and count of the user stories belonging to a certain role.</p> <p> </p> <p>**<strong>/Raw-Data-Phaes-II</strong>**</p> <p>The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx. Each file contains two tabs:</p> <p> </p> <p>- Analysis: includes the annotation of the user stories as belonging to existing original </p> <p>category (X), or to categories introduced after interviews, or to categories introduced </p> <p>after app store inspired elicitation (name of category in "Cat. Created in PH1"), or to </p> <p>entirely novel categories (name of category in "New Category").</p> <p> </p> <p>- Roles: roles used in the user stories, and count of the user stories belonging to a certain role.</p> <p> </p> <p>**<strong>/Figures</strong>**</p> <p> </p> <p>This folder includes the figures reported in the paper. The boxplots are generated from the </p> <p>data using the tool http://shiny.chemgrid.org/boxplotr/. The histograms and other plots are </p> <p>produced with Excel, and are also reported in the excel files listed above. </p>
Comparison of Clinical Outcomes Between Liposuction and Liposuction in Combination with Microsurgery for Lymphedema Treatment: A Proportions Meta-Analysis
<p>Funnel plots for heterogenicity of each subgroup analysis. a) Excess Volume Reduction Lipo subgroup. b) Excess Volume Reduction Lipo+Micro subgroup. c) Decrease/reduction in post-operative use of compression garment subgroup Lipo. d) Decrease/reduction in post-operative use of compression garment subgroup Lipo+Micro. e) Post-operative reduction of erysipelas/cellulitis subgroup Lipo. f) Post-operative reduction of erysipelas/cellulitis subgroup Lipo+Micro.</p>
FIGURE. Multilocus phylogenetic tree inferred from Bayesian analysis based on the combined TEF1-α and ACT sequences. Bayesian posterior probabilities are indicated next to the nodes. The tree was rooted with Cladosporium herbarum CBS 121621. The species in this study are indicated in bold. Types of species are indicated after the culture collection number (T = ex-type, ex-epitype, ex-neotype, or reference strain). in Six new species of Cladosporium associated with decayed leaves of native bamboo (Bambusoideae) in a fragment of Brazilian Atlantic Forest
FIGURE. Multilocus phylogenetic tree inferred from Bayesian analysis based on the combined TEF1-α and ACT sequences. Bayesian posterior probabilities are indicated next to the nodes. The tree was rooted with Cladosporium herbarum CBS 121621. The species in this study are indicated in bold. Types of species are indicated after the culture collection number (T = ex-type, ex-epitype, ex-neotype, or reference strain).
FIGURE. (Continued) Multilocus phylogenetic tree inferred from Bayesian analysis based on the combined TEF1-α and ACT sequences. Bayesian posterior probabilities are indicated next to the nodes. The tree was rooted with Cladosporium herbarum CBS 121621. The species in this study are indicated in bold. Types of species are indicated after the culture collection number (T = ex-type, ex-epitype, exneotype, or reference strain). in Six new species of Cladosporium associated with decayed leaves of native bamboo (Bambusoideae) in a fragment of Brazilian Atlantic Forest
FIGURE. (Continued) Multilocus phylogenetic tree inferred from Bayesian analysis based on the combined TEF1-α and ACT sequences. Bayesian posterior probabilities are indicated next to the nodes. The tree was rooted with Cladosporium herbarum CBS 121621. The species in this study are indicated in bold. Types of species are indicated after the culture collection number (T = ex-type, ex-epitype, exneotype, or reference strain).
Supplementary material for the publication: "Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer"
<p><span><span>This dataset contains postprocessed data for the publication „<span>Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer</span>“.</span></span></p>
GC-MS Combined with Fast GC e-nose for the Analysis of Volatile Components of Chamomile (Matricaria chamomilla L.) - Supplementary Materials
Open the record for dataset details and reuse information.
PRISMA Flowchart for the systematic review: Impact on postoperative complications of combined prehabilitation targeting co-exisiting smoking, malnutrition, obesity, alcohol drinking, and physical inactivity: a systematic review and meta-analysis of randomised trials
<p><strong><span>PRISMA 2020 Flow Chart.</span></strong><span> “No predefined risk factors” covers studies with relevant multimodal interventions that were excluded due to lack of predefined risky lifestyles in the population. “Only trial registration or conference abstract” covers reports of these but with no subsequent publication. SNAP: Smoking, Nutrition (overweight/obesity, malnutrition), Alcohol or Physical inactivity.</span></p>
Meta-analysis summary-level results of histology GWAS -- combined, visceral
<ol> <li>MarkerName -- name of the SNP</li> <li>Allele1 -- first allele</li> <li>Allele2 -- second allele</li> <li>Freq1 -- frequency of Allele1</li> <li>FreqSE -- standard error of the freq estimate</li> <li>MinFreq -- lower confidence bound of the freq estimate</li> <li>MaxFreq -- upper confidence bound of the freq estimate</li> <li>Weight -- sample size weight</li> <li>Zscore -- z-score of the SNP</li> <li>P-value -- p-value of the SNP</li> <li>Direction -- directions of the betas in the cohorts, in the order the cohorts are listed in the corresponding *.metalParams.txt file</li> <li>HetISq -- I-squared estimate for heterogeneity test</li> <li>HetChiSq -- Chi-Square estimate for heterogeneity test</li> <li>HetDf -- degrees of freedom for heterogeneity test</li> <li>HetPVal -- p-value for heterogeneity test</li> </ol>
Meta-analysis summary-level results of histology GWAS -- combined, subcutaneous
<ol> <li>MarkerName -- name of the SNP</li> <li>Allele1 -- first allele</li> <li>Allele2 -- second allele</li> <li>Freq1 -- frequency of Allele1</li> <li>FreqSE -- standard error of the freq estimate</li> <li>MinFreq -- lower confidence bound of the freq estimate</li> <li>MaxFreq -- upper confidence bound of the freq estimate</li> <li>Weight -- sample size weight</li> <li>Zscore -- z-score of the SNP</li> <li>P-value -- p-value of the SNP</li> <li>Direction -- directions of the betas in the cohorts, in the order the cohorts are listed in the corresponding *.metalParams.txt file</li> <li>HetISq -- I-squared estimate for heterogeneity test</li> <li>HetChiSq -- Chi-Square estimate for heterogeneity test</li> <li>HetDf -- degrees of freedom for heterogeneity test</li> <li>HetPVal -- p-value for heterogeneity test</li> </ol>
Analysis of opposing histone modifications H3K4me3 and H3K27me3 reveals candidate diagnostic biomarkers for triple negative breast cancer and gene set prediction combinations
<p>Supplementary data of manuscript <strong>'Analysis of opposing histone modifications H3K4me3 and H3K27me3 reveals candidate diagnostic biomarkers for triple negative breast cancer and gene set prediction combinations </strong><strong>Analysis of opposing histone modifications H3K4me3 and H3K27me3 reveals candidate diagnostic biomarkers for triple negative breast cancer and gene set prediction combinations'</strong></p>
APPENDIX. GenBank accession numbers of all DNA sequences of Cophyla used in this study. NA, not applicable. Asterisks mark cases where sequences from different samples were combined to chimeric terminals for analysis. in Description of the lucky Cophyla (Microhylidae, Cophylinae), a new arboreal frog from Marojejy National Park in north-eastern Madagascar
APPENDIX. GenBank accession numbers of all DNA sequences of Cophyla used in this study. NA, not applicable. Asterisks mark cases where sequences from different samples were combined to chimeric terminals for analysis.
Fig. 1 in Ultrastructural and immunocytochemical investigation of paramylon combined with new 18S rDNA-based secondary structure analysis clarifies phylogenetic affiliation of Entosiphon sulcatum (Euglenida: Euglenozoa)
Fig. 1 Phylograms obtained from maximum likelihood (ML) analyses of 182 euglenozoan taxa with new 18S rDNA sequences boxed and most ingroup taxa pruned to major groupings, sequences of Heterolobosea and Jakobida were used as outgroup. Congruent Bayesian inference (BI) posterior probability values>0.50 were mapped onto both ML trees and are
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.