Lignin-enriched tricalcium phosphate/sodium alginate 3D scaffolds for application in bone tissue regeneration

The bone is a connective, vascularized, and mineralized tissue that confers protection to organs, and participates in the support and locomotion of the human body, maintenance of homeostasis, as well as in hematopoiesis. However, throughout the lifetime, bone defects may arise due to traumas (mechanical fractures), diseases, and/or aging, which when too extensive compromise the ability of the bone to self-regenerate. To surpass such clinical situation, different therapeutic approaches have been pursued. Rapid prototyping techniques using composite materials (consisting of ceramics and polymers) have been used to produce customized 3D structures with osteoinductive and osteoconductive properties. In order to reinforce the mechanical and osteogenic properties of these 3D structures, herein, a new 3D scaffold was produced through the layer-by-layer deposition of a tricalcium phosphate (TCP), sodium alginate (SA), and lignin (LG) mixture using the Fab@Home 3D-Plotter. Three different TCP/LG/SA formulations, LG/SA ratio 1:3, 1:2, or 1:1, were produced and subsequently evaluated to determine their suitability for bone regeneration. The physicochemical assays demonstrated that the LG inclusion improved the mechanical resistance of the scaffolds, particularly in the 1:2 ratio, since a 15 % increase in the mechanical strength was observed. Moreover, all TCP/LG/SA formulations showed an enhanced wettability and maintained their capacity to promote the osteoblasts' adhesion and proliferation as well as their bioactivity (formation of hydroxyapatite crystals). Such results support the LG inclusion and application in the development of 3D scaffolds aimed for bone regeneration.info:eu-repo/semantics/publishedVersio

Phenobot - Intelligent photonics for molecular phenotyping in Precision Viticulture

The Phenobot platform is comprised by an autonomous robot, instrumentation, artificial intelligence, and digital twin diagnosis at the molecular level, marking the transition from pure data-driven to knowledge-driven agriculture 4.0, towards a physiology-based approach to precision viticulture. Such is achieved by measuring the plant metabolome ‘in vivo' and 'in situ', using spectroscopy and artificial intelligence for quantifying metabolites, e.g.: i. grapes: chlorophylls a and b, pheophytins a and b, anthocyanins, carotenoids, malic and tartaric acids, glucose and fructose; ii. foliage: chlorophylls a and b, pheophytins a and b, anthocyanins, carotenoids, nitrogen, phosphorous, potassium, sugars, and leaf water potential; and iii. soil nutrients (NPK). The geo-referenced metabolic information of each plant (organs and tissues) is the basis of multi-scaled analysis: i. geo-referenced metabolic maps of vineyards at the macroscopic field level, and ii. genome-scale 'in-silico' digital twin model for inferential physiology (phenotype state) and omics diagnosis at the molecular and cellular levels (transcription, enzyme efficiency, and metabolic fluxes). Genome-scale 'in-silico' Vitis vinifera numerical network relationships and fluxes comprise the scientific knowledge about the plant's physiological response to external stimuli, being the comparable mechanisms between laboratory and field experimentation - providing a causal and interpretable relationship to a complex system subjected to external spurious interactions (e.g., soil, climate, and ecosystem) scrambling pure data-driven approaches. This new approach identifies the molecular and cellular targets for managing plant physiology under different stress conditions, enabling new sustainable agricultural practices and bridging agriculture with plant biotechnology, towards faster innovations (e.g. biostimulants, anti-microbial compounds/mechanisms, nutrition, and water management). Phenobot is a project under the Portuguese emblematic initiative in Agriculture 4.0, part of the Recovery and Resilience Plan (Ref. PRR: 190 Ref. 09/C05-i03/2021 – PRR-C05-i03-I-000134)

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio