Results 91 to 100 of about 694,176 (262)
Optimizing photoactivation of PA‐mCherry for optical pooled CRISPR screens
FEBS Open Bio, EarlyView.Photoactivatable PA‐mCherry finds widespread use to optically tag individual cells. However, confocal 405 nm UV laser‐scanning (normal scan) is much less efficient than widefield UV illumination, limiting the use of PA‐mCherry on confocal instruments. We remedy this limitation by reporting that rapid and repeated confocal scanning with a low‐intensity,
Sravasti Mukherjee +3 more
wiley +1 more source
The number of true positives and false positives identified in different methods.
, 2012 The number of true positives and false positives identified in different methods.
Robin R. Gutell (156452) +3 more
core +1 more source
FEBS Open Bio, EarlyView.Time‐restricted feeding (TRF) in mice increased liver fatty acid oxidation and decreased fatty acid biosynthesis. These alterations persisted when TRF was discontinued and the host was infected with Mycobacterium tuberculosis. Pre‐exposure to TRF did not alter tissue (lung and spleen) mycobacterial burden but significantly reduced CD3+ T cells in lungs
Ashish Gupta +7 more
wiley +1 more source
Detection of False Positives in Data Loss Prevention (DLP) Applications Using Machine Learning.
, 2022 non
LEONARDS, DORENE AGATHA
core
Observations from the Proteomics Bench
ProteomesMany challenges in proteomics result from the high-throughput nature of the experiments. This paper first presents pre-analytical problems, which still occur, although the call for standardization in omics has been ongoing for many years.
Simone König +2 more
doaj +1 more source
Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
True vs. false positives with synthetic data.
, 2015 Peptide flip true positives = percent of peptide flips in the actual synthetic model that are present in the qFit 2.0 model. Peptide flip false positives = percent of residues with a peptide flip in the qFit 2.0 model that are not in the actual synthetic
James S. Fraser (818667) +2 more
core +1 more source
Identifying Exoplanets and Unmasking False Positives with NGTS [PDF]
, 2018 In my PhD, I advanced the scientific exploration of the Next Generation Transit Survey (NGTS), a ground-based wide-field survey operating at ESO’s Paranal Observatory in Chile since 2016.
core +2 more sources
Exploring the use of grammarly in assessing English academic writing
HeliyonThis study explores the extent to which Grammarly can be a reliable assessment tool for academic English writing. Ten articles published in high-status scholarly Q.1 journals and written by specialist English native speakers were used to evaluate the ...
Abdallah Abu Qub'a +2 more
doaj +1 more source
MagmaFlow: A desktop platform for artificial intelligence‐driven expression analysis
FEBS Open Bio, EarlyView.MagmaFlow is a free, no‐code platform for gene expression analysis. It generates interactive volcano plots, links genes to literature, pathways, and diseases, prioritizes candidates using millions of publications, identifies affected biological processes, builds network diagrams, and exports publication‐ready figures and reports for macOS and Windows ...
Carlos E. Buss +7 more
wiley +1 more source