Results 121 to 130 of about 18,955 (239)

Sustainability Strategies in the Cocoa‐Chocolate Value Chain: An Analysis Using Stakeholder Theory, Global Value Chain Theory, and Resource Dependence Theory

Agribusiness, EarlyView.
ABSTRACT The cocoa‐chocolate value chain faces significant environmental and social challenges, driving firms to adopt sustainability strategies ranging from individual practices to third‐party certifications. This study investigates the factors associated with these strategies by analyzing 304 cocoa‐chocolate companies using firm‐level data from the ...
Stella Marschner, Luigi Orsi, Alessandro Olper, Stefanella Stranieri   +3 more
wiley   +1 more source

Epsilon/zeta toxin-antioxin systems in gram-negative bacteria

, 2015
Their genetic repertoire allows bacteria to adapt to constantly changing environments and to survive inhospitable conditions. Such genetic modules are toxin-antitoxin (TA) systems, which increase the survival of a population by inducing dormancy or cell death of a subpopulation of cells.
openaire   +2 more sources

Flexible Memory: Progress, Challenges, and Opportunities

Advanced Intelligent Discovery, EarlyView.
Flexible memory technology is crucial for flexible electronics integration. This review covers its historical evolution, evaluates rigid systems, proposes a flexible memory framework based on multiple mechanisms, stresses material design's role, presents a coupling model for performance optimization, and points out future directions.
Ruizhi Yuan, Sen Chen, Minghui Guo, Ziliang Cui, Yujie Du, Jing Liu   +5 more
wiley   +1 more source

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence‐Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study

Advanced Intelligent Systems, EarlyView.
The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez, Estefania Ascencio, Jose M. Ageitos, Lucia Feijoo, Shan He, Amirreza Daghighi, Gerardo M. Casanola‐Martin, Cristian R. Munteanu, Santiago Rodriguez Yañez, Alejandro Pazos, Harbil Bediaga, Brenda Durand Ordiales, Raquel Esteban, Ana‐Maria Heres, Jorge Curiel Yuste, Lur Epelde, Bakhtiyor Rasulev, Tomas Gonzalez, Sonia Arrasate, Humberto González‐Diaz   +19 more
wiley   +1 more source

Does Clostridium Perfringens Epsilon Toxin Mimic an Auto-Antigen Involved in Multiple Sclerosis? [PDF]

Toxins (Basel)
Gougeon ML, Seffer V, Hoxha C, Maillart E, Popoff MR.   +4 more
europepmc   +1 more source

Clostridium perfringens epsilon toxin binds to erythrocyte MAL receptors and triggers phosphatidylserine exposure. [PDF]

J Cell Mol Med, 2020
Geng Z, Huang J, Kang L, Gao S, Yuan Y, Li Y, Wang J, Xin W, Wang J.   +8 more
europepmc   +1 more source

A Fully Soft Sensing Suit With Optimal Sensor Placement for Real‐Time Motion Tracking

Advanced Intelligent Systems, EarlyView.
A fully soft, skin‐conformable sensing suit integrating stretchable sensors, liquid metal wiring, and soft electrodes was developed using direct ink writing, with sensor placement optimized through an automated algorithmic pipeline. This system enables accurate and unobtrusive real‐time motion tracking, providing a scalable, material‐based solution to ...
Jinhyeok Oh, Joonbum Bae
wiley   +1 more source

Multiple Sclerosis and Clostridium perfringens Epsilon Toxin: Is There a Relationship? [PDF]

Biomedicines
Huss A, Bachhuber F, Feraudet-Tarisse C, Hiergeist A, Tumani H.   +4 more
europepmc   +1 more source

In vivo Blood-brain Barrier Permeability Assays Using Clostridium perfringens Epsilon Toxin. [PDF]

Bio Protoc, 2020
Mazzucco MR, Vartanain T, Linden JR.
europepmc   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source