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407 results for “biological activity”
Figure 12 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 12. SEM visualization of α- and γ-Al2O3 NPs aggregation on ventral side of C. mycophagus mite. A = treated female by α-Al2O3 NPs, B = treated female by γ-Al2O3 NPs, C = untreated female.
Figure 7 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 7. Females, nymphal and larval mortality (means ± SE) of C. mycophagus mites, subjected to synthesized α- and γ- Al2O3 NPs at different concentrations and exposure time – A. α-Al2O3 NPs; B. γ-Al2O3 NPs. Different letters within the same exposure time are significantly different (Duncan test, P ≤ 0.05).
Figure 5 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 5. Females, nymphal and larval mortality (means ± SE) of M. fungivorus mites, subjected to synthesized α- and γ-Al2O3 NPs at different concentrations and exposure time – A. α-Al2O3 NPs; B. γ-Al2O3 NPs. Different letters within the same exposure time are significantly different (Duncan test, P ≤ 0.05).
Figure 14 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 14. Mean growth Inhibition (A) and corresponding percentage (B), of F. oxysporum in response to different concentrations of α and γ-Al2O3 NPs after 5 days of growth at 30 °C and 180 rpm in PDB growth medium (Where R2: the relation coefficient and y: the predicted fungal inhibition value at "X" nanoparticles concentration).
Figure 6 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 6. Mortality (means ± SE) of M. fungivorus females, nymphs and larvae, concerning α- and γ-Al2O3 NPs at tested concentrations and exposure time.
Figure 10 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 10. Females' mortality (means ± SE) of (A) M. fungivorus and (B) C. mycophagus mites subjected to synthesized α and γ-Al2O3 NPs at different concentrations and exposure time. different letters within the same concentrations are significantly different, Duncan test (P ≤ 0.05).
Figure 13 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 13. Mean growth Inhibition (A) and corresponding percentage (B) of Aspergillus flavus in response to different concentrations of α- and γ-AL2O3 NPs after five days of growth at 30 ℃ and 180 rpm in PDB growth medium. (Where R2: the relation coefficient and y: the predicted fungal inhibition value at "X" nanoparticles concentration).
Text-fig. 2 Associate Professor RNDr. Václav Ziegler CSc. in Faculty of Education of Charles University at Prague during Trends in didactics of biology, conference on the 20th anniversary of the restoration activities of the Department of Biology and Environmental Studies at the Faculty of Education of Charles University in Prague 2. October 2014. (photo: author 2014) in Václav Ziegler Septagenarian
Text-fig. 2 Associate Professor RNDr. Václav Ziegler CSc. in Faculty of Education of Charles University at Prague during Trends in didactics of biology, conference on the 20th anniversary of the restoration activities of the Department of Biology and Environmental Studies at the Faculty of Education of Charles University in Prague 2. October 2014. (photo: author 2014)
Fig. 5 in Influence of seasonality and biological activity on infection by helminths in Cantabrian bear
Fig. 5. Seasonal kinetics of helminths egg-output in Cantabrian brown bears (n = 248) according to their activity periods. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Influence of seasonality and biological activity on infection by helminths in Cantabrian bear
Fig. 2. Seasonal variations in the prevalence of helminth infection in feces of brown bears (n = 248) from the western part of the Cantabrian Mountains (Asturias and Le´on provinces, Spain). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Influence of seasonality and biological activity on infection by helminths in Cantabrian bear
Fig. 4. Seasonal variations in the prevalence of helminth infection in feces of brown bears (n = 248) from Cantabrian Mountains (Spain) according to their activity periods. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Influence of seasonality and biological activity on infection by helminths in Cantabrian bear
Fig. 1. Distribution of the sampling of brown bear feces (n = 248) in the western part of the Cantabrian Mountains (Asturias and Le´on provinces, Spain). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Data described in the article "Nitrogen-Containing Flavonoids─Preparation and Biological Activity"
<p>The dataset includes supplementary data, i.e. the results of optimization of Ullmann reaction, cellular antioxidant and anti-inflammatory activity, cytotoxicity, antibacterial activity, and molecular docking, <sup>1</sup>H, <sup>13</sup>C{<sup>1</sup>H} NMR data, HPLC, and HRMS analyses of the study titled "Nitrogen-Containing Flavonoids─Preparation and Biological Activity" available here: <a href="https://doi.org/10.1021/acsomega.4c04627">https://doi.org/10.1021/acsomega.4c04627</a></p>
Physical stabilization of water-soluble PVA nanofibrous materials functionalized with biologically active substances
<p>Tissue engineering aims to develop materials that enhance biological activity and promote tissue healing and regeneration. One promising approach is to functionalize nanofibrous materials with antimicrobial substances, such as lipophosphonoxin (LPPO), and use water-soluble polymers like polyvinyl alcohol (PVA) to incorporate bioactive molecules into fibers. However, water-soluble materials often face the issue of "burst release," releasing over 90% of the active substances within the initial 24 hours. This research focuses on preparing functionalized nanofibrous materials based on PVA containing the experimental antimicrobial compound LPPO and subsequent physical stabilization of the materials using the "Heat treatment" method. The applied stabilization successfully reduced the incorporated substance's release rate by up to 50%. The resulting materials have the potential to provide functional cross-linked PVA nanofiber scaffolds for regenerative medicine applications in large and chronic skin injuries.</p>
The rapid and highly parallel identification of antibodies with defined biological activities by SLISY
<p>This is the sequencing data that accompanies the manuscript published in Nature Communications titled "The rapid and highly parallel identification of antibodies with defined biological activities by SLISY".</p>
Multi-Omics Visible Drug Activity Prediction with a Biologically Informed Neural Network Model
<p>Drug discovery is a challenging task, it takes several years for a drug to be introduced on the market, with most of<br> the studied drugs not even passing the first phase. The understanding of the mechanisms influencing response to drugs<br> can reduce failures and accelerate drug development. Virtual drug screening, based on Machine Learning models, is a<br> promising field for the prediction of the outcome of a treatment. However, the complex relationships between the features<br> learned by these models are still poorly understood and not easy to interpret.<br> We have designed a Neural Network model for drug sensitivity prediction that leverages a Visible Neural Network, an<br> easily interpretable model, due to its biologically informed nature. The trained model can be inspected to study which<br> biological processes were fundamental for the prediction and to identify the drug properties that affect sensitivity. It<br> combines multi-omics data from various types of tumor tissues and drug representations based on molecular descriptors.<br> The mechanisms learned from the network can also be exploited to find candidate drugs for synergy to predict the effect<br> of combined therapies. We consider the unbalanced nature of public drug screening datasets and show that our model<br> outperforms state-of-the-art visible machine learning models.</p>
A Study of the Efficacy and Safety of Upadacitinib (ABT-494) in Participants With Moderately to Severely Active Crohn's Disease Who Have Inadequately Responded to or Are Intolerant to Biologic Therapy
ClinicalTrials.gov study NCT03345836. IPD Sharing: YES. Countries: 48. Publications: 7.
A Study Comparing Risankizumab to Placebo in Participants With Active Psoriatic Arthritis Including Those Who Have a History of Inadequate Response or Intolerance to Biologic Therapy(Ies)
ClinicalTrials.gov study NCT03671148. IPD Sharing: YES. Countries: 25. Publications: 9.
A Study to Assess the Efficacy and Safety of Risankizumab in Participants With Moderately to Severely Active Crohn's Disease Who Failed Prior Biologic Treatment
ClinicalTrials.gov study NCT03104413. IPD Sharing: YES. Countries: 43. Publications: 5.
Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023
<p>Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023</p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
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.