Results 231 to 240 of about 87,132 (306)

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source

Updated safety evaluation of the food enzyme glucan 1,4-α-maltohydrolase from the genetically modified <i>Bacillus subtilis</i> strain BABSC. [PDF]

EFSA J
EFSA Panel on Food Enzymes (FEZ), Zorn H, Barat Baviera JM, Bolognesi C, Catania F, Gadermaier G, Greiner R, Mayo B, Mortensen A, Roos YH, Solano MLM, Van Loveren H, Vernis L, Aguilera J, Peluso S, Liu Y.   +15 more
europepmc   +1 more source

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source

The European Parliament and the objectives of the Common Agricultural Policy. Research and Documentation Papers: Agriculture Series No. 9 [PDF]

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Assessment of fall-back MRLs for revoked CXLs previously implemented in the EU legislation. [PDF]

EFSA J
European Food Safety Authority (EFSA).
europepmc   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Assessment of a feed additive consisting of zinc bislysinate (3b613) for all animal species for the renewal of its authorisation (Senzyme GmbH). [PDF]

EFSA J
EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP), Villa RE, Azimonti G, Bonos E, Christensen H, Durjava M, Dusemund B, Gehring R, Glandorf B, Kouba M, López-Alonso M, Marcon F, Martelli G, Nebbia C, Pechová A, Prieto-Maradona M, Theodoridou K, Woutersen R, Galobart J, Vettori MV, Manini P, Villa AN, Tarrés-Call J, Valeri P.   +23 more
europepmc   +1 more source

Clinically Informed Intelligent Classification of Ovarian Cancer Cells by Label‐Free Holographic Imaging Flow Cytometry

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone, Beatrice Cavina, Daniele Gaetano Sirico, Martina Mugnano, Vittorio Bianco, Lisa Miccio, Anna Myriam Perrone, Anna Maria Porcelli, Giuseppe Gasparre, Ivana Kurelac, Pasquale Memmolo, Pietro Ferraro   +11 more
wiley   +1 more source

Safety evaluation of the food enzyme arabinan endo-1,5-α-l-arabinanase from the non-genetically modified <i>Aspergillus tubingensis</i> strain ARS R7-60. [PDF]

EFSA J
EFSA Panel on Food Enzymes (FEZ), Zorn H, Baviera JMB, Bolognesi C, Catania F, Gadermaier G, Greiner R, Mayo B, Mortensen A, Roos YH, Solano MLM, Van Loveren H, Vernis L, Peluso S, Cavanna D, Andryszkiewicz M, Criado A, Liu Y.   +17 more
europepmc   +1 more source

Machine Learning‐Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of image quality metrics in combination with machine learning enables automatic image quality assessment for fluorescence microscopy images. The method can be integrated into the experimental pipeline for optical microscopy and utilized to classify artifacts in experimental images and to build quality rankings with a reference‐free approach ...
Elena Corbetta, Thomas Bocklitz
wiley   +1 more source