Artificial Intelligence Tools to Better Understand Seed Dormancy and Germination

Despite a large number of publications available, the control mechanisms of seed dormancy and germination are far to be fully understood. Seed dormancy and germination are very complex biological processes and because they involve multiple factors (physiological, mechanical, and environmental) and their nonlinear interactions. This explains why extremely little variations on some of those factors and in the way they interact caused enormous variation in the obtained results. Multifactorial process like these can be modeled using computer-based tools to predict better results. In this chapter, some basic concepts relative to seed dormancy and germination and the main factors (physiological, involved in seed dormancy, particularly dormancy-inducers and dormancy-breakers, and seed germination) are reviewed. In the next two, we describe the use of artificial intelligence computer-based models to better understand the physiological mechanisms of seed dormancy (how dormancy is controlled and how can be released) and seed germination. Finally, some applications of artificial neural networks, fuzzy logic, and genetic algorithms to elucidate critical factors and predict optimal condition for seed dormancy-breaking and germination are given as examples of the utility of this powerful computer-based tools

Green Processing of Nanoporous Biodegradable Carriers of Bioactive Agents for Pharmaceutical and Biomedical Applications

Pharmaceutical and biomedical industries demand simple, safe and reproducible processing methods thus urging the development of novel straightforward manufacturing approaches. The product manufacturing by the green processing of admixtures and end-product would avoid long and costly purification (downstream) steps. In this work, the green supercritical fluid technology is used for the processing of nanoporous carriers (aerogels) for bioactive agents [1,2]. Aerogels in the form of one micron-sized particles were processed and loaded with a model bioactive compound (ketoprofen). Results show that the carrier has excellent textural properties (specific surface area of 200 m2/g) and a high loading capacity (7 wt.%) of the bioactive compound in the amorphous form. Release profile tests show the capacity of the carrier to modulate the drug release to the medium (PBS pH 7.4). The resulting material can be incorporated in the formulation of several pharmaceutical and biomedical products

Modeling the effects of light and sucrose on in vitro propagated plants: A multiscale system analysis using artificial intelligence technology

Background: Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. Methodology and Principal Findings: In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 μmol m-2 s -1. Conclusions: Our results demonstrate that artificial intelligence models are not only successful in identifying complex nonlinear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work. © 2014 Gago et al.This work was supported by The Regional Government Xunta de Galicia (PGIDIT02BTF30102PR) and Spanish MEC (AGL 2003-05877) to PPG. ML (PR2010-0460) and PPG (PR2010-0357) thank the Spanish Ministry of Education for their financial support during their sabbatical year at Faculty of Science, University of Utrecht, NetherlandsPeer Reviewe

Machine Learning Technology Reveals the Concealed Interactions of Phytohormones on Medicinal Plant In Vitro Organogenesis

Organogenesis constitutes the biological feature driving plant in vitro regeneration, in which the role of plant hormones is crucial. The use of machine learning (ML) technology stands out as a novel approach to characterize the combined role of two phytohormones, the auxin indoleacetic acid (IAA) and the cytokinin 6-benzylaminopurine (BAP), on the in vitro organogenesis of unexploited medicinal plants from the Bryophyllum subgenus. The predictive model generated by neurofuzzy logic, a combination of artificial neural networks (ANNs) and fuzzy logic algorithms, was able to reveal the critical factors affecting such multifactorial process over the experimental dataset collected. The rules obtained along with the model allowed to decipher that BAP had a pleiotropic effect on the Bryophyllum spp., as it caused different organogenetic responses depending on its concentration and the genotype, including direct and indirect shoot organogenesis and callus formation. On the contrary, IAA showed an inhibiting role, restricted to indirect shoot regeneration. In this work, neurofuzzy logic emerged as a cutting-edge method to characterize the mechanism of action of two phytohormones, leading to the optimization of plant tissue culture protocols with high large-scale biotechnological applicabilityThe authors acknowledge the FPU grant awarded to Pascual García-Pérez from the Spanish Ministry of Education (grant number FPU15/04849)S

From Ethnomedicine to Plant Biotechnology and Machine Learning: The Valorization of the Medicinal Plant Bryophyllum sp.

The subgenus Bryophyllum includes about 25 plant species native to Madagascar, and is widely used in traditional medicine worldwide. Different formulations from Bryophyllum have been employed for the treatment of several ailments, including infections, gynecological disorders, and chronic diseases, such as diabetes, neurological and neoplastic diseases. Two major families of secondary metabolites have been reported as responsible for these bioactivities: phenolic compounds and bufadienolides. These compounds are found in limited amounts in plants because they are biosynthesized in response to different biotic and abiotic stresses. Therefore, novel approaches should be undertaken with the aim of achieving the phytochemical valorization of Bryophyllum sp., allowing a sustainable production that prevents from a massive exploitation of wild plant resources. This review focuses on the study of phytoconstituents reported on Bryophyllum sp.; the application of plant tissue culture methodology as a reliable tool for the valorization of bioactive compounds; and the application of machine learning technology to model and optimize the full phytochemical potential of Bryophyllum sp. As a result, Bryophyllum species can be considered as a promising source of plant bioactive compounds, with enormous antioxidant and anticancer potential, which could be used for their large-scale biotechnological exploitation in cosmetic, food, and pharmaceutical industriesThis research was funded by Xunta de Galicia through “Red de Uso Sostenible de los Recursos Naturales y Agroalimentarios” (REDUSO, grant number ED431D 2017/18) and “Cluster of Agricultural Research and Development” (CITACA Strategic Partnership, gran numbered ED431E 2018/07). The authors acknowledge the FPU grant awarded to Pascual García-Pérez from the Spanish Ministry of Education (grant number FPU15/04849)S

Computer-based tools provide new insight into the key factors that cause physiological disorders of pistachio rootstocks cultured in vitro

During the in vitro culture of plants some physiological disorders caused major problems that have been associated with culture media composition. The objective of this study was to better understand the abnormal physiological response of two pistachio rootstocks to changes in culture media ingredients. On this purpose, two computer-based tools were employed: design of experiment (DOE) and neurofuzzy logic. DOE was employed to generate a five-dimensional IV-design spaces allowing to reduce the number of treatments from 6,250 to 61. The second one, an artificial intelligence (AI) tool, neurofuzzy logic, was used to understand the cause-effect relationships between the factors studied (25) and seven physiological disorders including shoot-tip necrosis (STN), leaf necrosis (LN), leaf color (LC), basal callus (BC) formation, shoot fasciation (SF), hyperhydricity and epinasty, typically described during pistachio in vitro culture. Four out of the seven disorders were successfully modeled, being significantly affected by a limited number of factors. STN and BC were significantly affected by the concentration of EDTA−. However, while a low concentration of EDTA− reduces the STN, promotes BC. LN and LC were strongly alleviated by high amounts of thiamine-HCl. Undoubtedly, the results demonstrate the importance of recording and using data related to physiological disorders along with growth parameters when developing suitable culture media for plant tissues. The computer-based tools have been useful to: i) well sample experimental design; ii) reduce the final number of treatments and the experimental work; iii) identify the key factors affecting each disorder; iv) get insight about the causes that promote the appearance of physiological disorders. Our findings demonstrate that the recently AI designed POM media, although not optimal, is the most suitable (favouring growth and limiting physiological abnormalities) media for in vitro culture of pistachio compared to those media, currently used.Xunta de Galicia | Ref. ED431C2017/09Xunta de Galicia | Ref. ED431D-2017/1

Current Stage of Marine Ceramic Grafts for 3D Bone Tissue Regeneration

Bioceramic scaffolds are crucial in tissue engineering for bone regeneration. They usually provide hierarchical porosity, bioactivity, and mechanical support supplying osteoconductive properties and allowing for 3D cell culture. In the case of age-related diseases such as osteoarthritis and osteoporosis, or other bone alterations as alveolar bone resorption or spinal fractures, functional tissue recovery usually requires the use of grafts. These bone grafts or bone void fillers are usually based on porous calcium phosphate grains which, once disposed into the bone defect, act as scaffolds by incorporating, to their own porosity, the intergranular one. Despite their routine use in traumatology and dental applications, specific graft requirements such as osteoinductivity or balanced dissolution rate are still not completely fulfilled. Marine origin bioceramics research opens the possibility to find new sources of bone grafts given the wide diversity of marine materials still largely unexplored. The interest in this field has also been urged by the limitations of synthetic or mammalian-derived grafts already in use and broadly investigated. The present review covers the current stage of major marine origin bioceramic grafts for bone tissue regeneration and their promising properties. Both products already available on the market and those in preclinical phases are included. To understand their clear contribution to the field, the main clinical requirements and the current available biological-derived ceramic grafts with their advantages and limitations have been collected.This research was partially supported by the European Union projects 0245_IBEROS_1_E and 0302_CVMAR_I_1_P, both from Interreg V-A Spain-Portugal (POCTEP 2015) and BLUEHUMAN EAPA_151/2016 from INTERREG Atlantic Area, European Regional Development Fund. Moreover, regional funds from Competitive Reference Groups (GRC) ED431C 2016/008 and ED431C 2017_51 and Research networks ED431D 2017/13 both from Xunta de Galicia (Spain) are also acknowledged. P. Diaz-Rodriguez and M. López-Alvarez are thankful for the funding support provided by 0245_IBEROS_1_E from EU Interreg V-A Spain-Portugal (POCTEP) project 2017/13 both from Xunta de Galicia (Spain) are also acknowledged. P. Diaz-Rodriguez and M. López-Alvarez are thankful for the funding support provided by 0245_IBEROS_1_E from EU Interreg V-A Spain-Portugal (POCTEP) project.S

Artificial intelligence tools to better understand seed dormancy and germination

Despite a large number of publications available, the control mechanisms of seed dormancy and germination are far to be fully understood. Seed dormancy and germination are very complex biological processes and because they involve multiple factors (physiological, mechanical, and environmental) and their nonlinear interactions. This explains why extremely little variations on some of those factors and in the way they interact caused enormous variation in the obtained results. Multifactorial process like these can be modeled using computer-based tools to predict better results. In this chapter, some basic concepts relative to seed dormancy and germination and the main factors (physiological, involved in seed dormancy, particularly dormancy-inducers and dormancy-breakers, and seed germination) are reviewed. In the next two, we describe the use of artificial intelligence computer-based models to better understand the physiological mechanisms of seed dormancy (how dormancy is controlled and how can be released) and seed germination. Finally, some applications of artificial neural networks, fuzzy logic, and genetic algorithms to elucidate critical factors and predict optimal condition for seed dormancy-breaking and germination are given as examples of the utility of this powerful computer-based tools

Contribución de la Facultad de Farmacia de Santiago de Compostela al desarrollo de la investigación científica española en el primer tercio del siglo XX (1900-1936)

The Faculty of Pharmacy of Santiago de Compostela was founded in 1857, but research activities did not start until the beginning of the 20th century. The new Spanish regulations promoting the experimentation at universities and the stages of professors and recently graduated students outside Spain contributed to generate a remarkable research group in a provincial university. The excessive university centralism at that time in Spain and the characteristics of the headquarters of the faculty —a 16th century Renaissance palace— hindered, in general, the research development. However, the scientific spirit of professors and students, and their work were imposed and they achieved an estimable status in pharmacy research in Galicia. The facilities were the staff's highest priority and it raised more than a protest. A remarkable investment in apparatuses and laboratory materials was carried out and researchers started to publish original research papers in local journals, such as the «Revista de farmacia», published by their own University. In the previous years to the Spanish Civil War (1936) the research status in drug development and the background of the staff and students were important, allowing them to join the military laboratory that Franco’s army created at the faculty of pharmacy. The research work continued in a small scale and was specially focused on the production of «copy drugs» copying European specific drugs which were scarce at that time. The first third of the 20th century was the germ of the great research prestige that the Faculty of Pharmacy of Santiago de Compostela enjoys at present.La Facultad de Farmacia de Santiago de Compostela se fundó en 1857, pero no fue hasta comienzos del siglo XX cuando comenzó a desarrollar una labor de investigación en medicamentos. Las nuevas reglamentaciones que fomentaban la experimentación en los laboratorios universitarios y la formación de profesores y recién licenciados en el extranjero, contribuyeron a generar un grupo investigador notable dentro de una Facultad de provincias. La excesiva centralización en materia universitaria de la época y las características de la sede de la Facultad —un palacio renacentista del siglo XVI— dificultaron, en general, el desarrollo de la investigación. Sin embargo, el espíritu científico de profesores y alumnos, y su trabajo se impusieron y lograron un estatus digno en investigación de medicamentos en Galicia. En estos años comenzaron a publicarse trabajos originales principalmente en la «Revista de Farmacia», publicada por la propia Universidad compostelana. En los años precedentes a la guerra civil española (1936) el nivel alcanzado en investigación y desarrollo de medicamentos era importante, favoreciendo el hecho de que, una vez comenzada la contienda, el personal de la Facultad trabajó en el laboratorio de farmacia militar en el que fue transformada por el ejército franquista. Durante este período la labor investigadora continuó, aunque a menor escala, y orientada a la obtención de «medicamentos copia» de otros específicos que escaseaban en tiempos de guerra. Durante el primer tercio del siglo XX se desarrolló el germen del prestigio investigador del que goza en la actualidad la Facultad de Farmacia compostelana