Abundance of Pollinator Insect (Forcipomyia Spp .) of Cocoa Under Some Shade Trees [PDF]
, 2016 Cocoa production is affected by cocoa flowering and fruiting. The factor affects fruit seeting is pollinator agent such as Forcipomyia spp. Until now, information concerning population dynamics of Forcipomyia in some models of cocoa shading trees remains
Rahayu, D. S. (Dwi) +2 more
core +3 more sources
Analysis of the interactions of shade trees on coffee leaf diseases and coffee yield in complex agroforestry systems [PDF]
, 2019 In complex coffee-based agroforestry, quantifying interactions within the agrosystem that impact on coffee disease regulation and on coffee yield is a major stake to design sustainable cropping systems.
Allinne, Clémentine +6 more
core
Shade trees: a determinant to the relative success of organic versus conventional coffee production
Agroforestry Systems, 2017 Greater understanding of the influences on long-term coffee productivity are needed to develop systems that are profitable, while maximizing ecosystem services and lowering negative environmental impacts.
F. Schnabel +5 more
semanticscholar +1 more source
Disordered but rhythmic—the role of intrinsic protein disorder in eukaryotic circadian timing
FEBS Letters, EarlyView.Unstructured domains known as intrinsically disordered regions (IDRs) are present in nearly every part of the eukaryotic core circadian oscillator. IDRs enable many diverse inter‐ and intramolecular interactions that support clock function. IDR conformations are highly tunable by post‐translational modifications and environmental conditions, which ...
Emery T. Usher, Jacqueline F. Pelham
wiley +1 more source
Nitrogen fixing shade trees in coffee agroforestry: Quantification of nitrogen transfer to the coffee plant [PDF]
, 2019 Nitrogen-fixing shade trees in coffee agroforestry systems are assumed to provide an alternative nitrogen source for the coffee crop when fertilizer applications are low, but the transfer of nitrogen from shade trees to the coffee crop has not yet been ...
Blanchart, Eric +3 more
core
Soil Bacterial Diversity and Productivity of Coffee - Shade Tree Agro-ecosystems [PDF]
, 2012 Coffee productions should have environmental values such as providing high soil microbial diversity while producinghigh yield. To examine that purposes, two experimental plots were constucted at benchmark site of Conservationand Sustainable Management of
Evizal, R. (Rusdi) +4 more
core +3 more sources
Meta‐analysis fails to show any correlation between protein abundance and ubiquitination changes
FEBS Open Bio, EarlyView.We analyzed over 50 published proteomics datasets to explore the relationship between protein levels and ubiquitination changes across multiple experimental conditions and biological systems. Although ubiquitination is often associated with protein degradation, our analysis shows that changes in ubiquitination do not globally correlate with changes in ...
Nerea Osinalde +3 more
wiley +1 more source
Advanced Functional Materials, EarlyView.The integration of photovoltaic (PV) systems into building structures introduces distinct fire risks with critical implications for occupant safety. This review examines the key fire hazards associated with PV implementation and explores mitigation strategies, including flame‐retardant additives.
Florian Ollagnon +7 more
wiley +1 more source
Shade and leaf retention: an aspect of effective Coffee Leaf Rust management [PDF]
, 2019 Coffee Leaf Rust (CLR) has been reported in over 50 coffee growing countries causing significant economic losses in Arabica coffee. The aim of this study was to determine the effect of shade on leaf life span in relation to severity of coffee leaf rust ...
Alwora, G. +4 more
core
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source