scholarly journals Applying Molecular Phenotyping Tools to Explore Sugarcane Carbon Potential

2021 ◽  
Vol 12 ◽  
Author(s):  
Maria Juliana Calderan-Rodrigues ◽  
Luíza Lane de Barros Dantas ◽  
Adriana Cheavegatti Gianotto ◽  
Camila Caldana
Keyword(s):  
Data Science ◽  
Sustainable Use ◽  
Biomass Accumulation ◽  
Carbon Potential ◽  
Breeding Programs ◽  
Sugarcane Yield ◽  
Speed Up ◽  
C Partitioning ◽  
Competing Pathways ◽  
Molecular Phenotyping

Sugarcane (Saccharum spp.), a C4 grass, has a peculiar feature: it accumulates, gradient-wise, large amounts of carbon (C) as sucrose in its culms through a complex pathway. Apart from being a sustainable crop concerning C efficiency and bioenergetic yield per hectare, sugarcane is used as feedstock for producing ethanol, sugar, high-value compounds, and products (e.g., polymers and succinate), and bioelectricity, earning the title of the world’s leading biomass crop. Commercial cultivars, hybrids bearing high levels of polyploidy, and aneuploidy, are selected from a large number of crosses among suitable parental genotypes followed by the cloning of superior individuals among the progeny. Traditionally, these classical breeding strategies have been favoring the selection of cultivars with high sucrose content and resistance to environmental stresses. A current paradigm change in sugarcane breeding programs aims to alter the balance of C partitioning as a means to provide more plasticity in the sustainable use of this biomass for metabolic engineering and green chemistry. The recently available sugarcane genetic assemblies powered by data science provide exciting perspectives to increase biomass, as the current sugarcane yield is roughly 20% of its predicted potential. Nowadays, several molecular phenotyping tools can be applied to meet the predicted sugarcane C potential, mainly targeting two competing pathways: sucrose production/storage and biomass accumulation. Here we discuss how molecular phenotyping can be a powerful tool to assist breeding programs and which strategies could be adopted depending on the desired final products. We also tackle the advances in genetic markers and mapping as well as how functional genomics and genetic transformation might be able to improve yield and saccharification rates. Finally, we review how “omics” advances are promising to speed up plant breeding and reach the unexplored potential of sugarcane in terms of sucrose and biomass production.

scholarly journals Strategy for Sustainable Development and Utilization of Sheep and Goat Resources in Serbia

2017 ◽  
Vol 2 (6) ◽  
pp. 11 ◽  
Author(s):  
Milan P. Petrovic ◽  
Violeta Caro Petrovic ◽  
Dragana Ruzic Muslic ◽  
Nevena Maksimovic ◽  
B. Cekic ◽  
...  
Keyword(s):  
Small Ruminant ◽  
Sustainable Use ◽  
Domestic Sheep ◽  
Breeding Programs ◽  
Sustainable Resources ◽  
The Past ◽  
Different Types ◽  
The Status ◽  
Development And Utilization ◽  
Market Factors

The aims of this study were to determine the status of small ruminant production in Serbia and to provide projections for their sustainable use with optimal strategy of genetic improvement of sheep and goats in the future. For sustainable sheep and goat production, it is necessary to know a number of biological, technological, organizational and market factors. Number of sheep in Serbia during the past two decades fell by about 20%.  This  country grows more than 1.7 million sheep. In terms of breed structures, most of the populations are indigenous Pramenka sheep (80%), while the remaining 20% are Tsigai, Merinolandschaf, Ile de France, Pirot improved, Mis sheep, and other less important populations, as well as the crossbreed with foreign and domestic sheep. Interest of goat rearing is constantly increasing in last years for 20-30%.  In regard to the breed structure, the least represented are goats of Alpine breed – approx. 2- 3%, White Serbian goat - 15%, different types of crosses – approx. 35% same as goats of low land Balkan type, and approx. 12% of high land Balkan type. Strategy of sheep and goat breeding programs in Serbia is focused on the improvement of indigenous breeds, because they are less demanding, and most importantly, the input is lower and their products have higher quality. Keywords: sheep; goat; sustainable; resources; meat; milk

scholarly journals An Alternative Bioassay for Synchytrium endobioticum Demonstrates the Expression of Potato Wart Resistance in Aboveground Plant Parts

2019 ◽  
Vol 109 (6) ◽  
pp. 1043-1052 ◽  
Author(s):  
B. T. L. H. van de Vossenberg ◽  
M. P. E. van Gent-Pelzer ◽  
M. Boerma ◽  
L. P. van der Gouw ◽  
T. A. J. van der Lee ◽  
...  
Keyword(s):  
Molecular Techniques ◽  
Breeding Programs ◽  
Potato Wart ◽  
Potato Varieties ◽  
Synchytrium Endobioticum ◽  
Plant Parts ◽  
Resting Spores ◽  
Plant Pathogen Interaction ◽  
Speed Up ◽  
Polymerase Chain

The obligate biotrophic chytrid species Synchytrium endobioticum is the causal agent of potato wart disease. Currently, 39 pathotypes have been described based on their interaction with a differential set of potato varieties. Wart resistance and pathotyping is performed using bioassays in which etiolated tuber sprouts are inoculated. Here, we describe an alternative method in which aboveground plant parts are inoculated. Susceptible plants produced typical wart symptoms in developing but not in fully expanded aboveground organs. Colonization of the host by S. endobioticum was verified by screening for resting spores by microscopy and by molecular techniques using TaqMan polymerase chain reaction and RNAseq analysis. When applied to resistant plants, none of these symptoms were detectable. Recognition of S. endobioticum pathotypes by differentially resistant potato varieties was identical in axillary buds and the tuber-based bioassays. This suggests that S. endobioticum resistance genes are expressed in both etiolated “belowground” sprouts and green aboveground organs. RNAseq analysis demonstrated that the symptomatic aboveground materials contain less contaminants compared with resting spores extracted from tuber-based assays. This reduced microbial contamination in the aboveground bioassay could be an important advantage to study this obligate biotrophic plant–pathogen interaction. Because wart resistance is active in both below- and aboveground organs, the aboveground bioassay can potentially speed up screening for S. endobioticum resistance in potato breeding programs because it omits the requirement for tuber formation. In addition, possibilities arise to express S. endobioticum effectors in potato leaves through agroinfiltration, thereby providing additional phenotyping tools for research and breeding. [Formula: see text] Copyright © 2019 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

scholarly journals AUTOMAT[R]IX: learning simple matrix pipelines

2021 ◽  
Author(s):  
Lidia Contreras-Ochando ◽  
Cèsar Ferri ◽  
José Hernández-Orallo
Keyword(s):  
Data Science ◽  
Learning Algorithm ◽  
Search Space ◽  
Matrix Transformation ◽  
R Program ◽  
Input And Output ◽  
Output Matrix ◽  
Frequency Of Use ◽  
Speed Up ◽  
Simple Matrix

AbstractMatrices are a very common way of representing and working with data in data science and artificial intelligence. Writing a small snippet of code to make a simple matrix transformation is frequently frustrating, especially for those people without an extensive programming expertise. We present AUTOMATIX, a system that is able to induce R program snippets from a single (and possibly partial) matrix transformation example provided by the user. Our learning algorithm is able to induce the correct matrix pipeline snippet by composing primitives from a library. Because of the intractable search space—exponential on the size of the library and the number of primitives to be combined in the snippet, we speed up the process with (1) a typed system that excludes all combinations of primitives with inconsistent mapping between input and output matrix dimensions, and (2) a probabilistic model to estimate the probability of each sequence of primitives from their frequency of use and a text hint provided by the user. We validate AUTOMATIX with a set of real programming queries involving matrices from Stack Overflow, showing that we can learn the transformations efficiently, from just one partial example.

scholarly journals On to the Next Chapter for Crop Breeding: Convergence with Data Science

2019 ◽  
Author(s):  
Elhan S. Ersoz ◽  
Nicolas F. Martin ◽  
Ann E. Stapleton
Keyword(s):  
Data Science ◽  
New Technologies ◽  
Predictive Analytics ◽  
Future Research ◽  
Agricultural Product ◽  
Crop Breeding ◽  
Breeding Programs ◽  
Public And Private ◽  
Agricultural Industry ◽  
Wide Range

Crop breeding is as ancient as the invention of cultivation.  In essence, the objective of crop breeding is to improve plant fitness under human cultivation conditions, making crops more productive while maintaining consistency in life cycle and quality. The applications of predictive breeding has been gaining momentum in agricultural industry and public breeding programs for the last decade, in the aftermath of genomic selection being recognized and widely applied for accelerating genetic gain in breeding programs. The massive amounts of data that has been generated by industry and farmers year after year through several decades has finally been recognized as an asset. A wide range of analytical methods such as machine learning, deep learning and artificial intelligence that were initially developed for diverse quantitative disciplines are now being adopted to crop breeding decision making processes. New technologies are currently being developed that would enable integration of data from various domains such as geospatial variables and a multitude of phenotypic responses as well as genetic information, in order to identify, develop and improve crop faster via partial or full automation of the decisions that pertain to variety development. Here we will discuss and summarize efforts from public and private domains for predictive analytics, and its applications to crop breeding and agricultural product development, and provide suggestions for future research.

scholarly journals Rapid development of homozygous lines through culture of isolated microspores in leafy crops of Brassicaceae Burnett

2019 ◽  
pp. 8-12
Author(s):  
Elena A. Domblides ◽  
Olga A. Chichvarina ◽  
Anna I. Minejkina ◽  
Evgeniу L. Evgeniу ◽  
Viktor A. Kharchenko ◽  
...  
Keyword(s):  
Rapid Development ◽  
Vegetable Crops ◽  
Breeding Programs ◽  
Whole Process ◽  
Embryo Yield ◽  
Isolated Microspores ◽  
Homozygous Line ◽  
Basic Protocol ◽  
Speed Up

Relevance Biotechnological methods are generally used to speed up breeding programs and to enhance genetic diversity, so the culture of isolated microspore in vitro can be regarded as one of very suitable methods. Nontraditional and uncommon vegetable crops belonging to Brassicaceae Burnett. are becoming more popular. Methods Accessions of sarepta mustard (Brassica juncea L. Czern.) and rocket salad (Eruca sativa Mill.) were taken for the study with the aim to optimize the basic protocol for these species. Results As a result of the study the optimum cultivation conditions have been determined for the species. Sizes of buds 2.5-3.5 mm long for sarepta mustard and 7.0-7.5 long for rocket salad which were used for cultivation had been experimentally defined. It was also shown that the cold pretreatment had improved the embryo yield. The nutritional NLN-13 medium with pH 6.1 and pretreatment at 32°C during a cultivation day had been shown to be more favourable for all accessions. All conditions that had been used were suitable for embryo formation. First divisions had been seen after 4 days of cultivation, while the embryos at primary cotyledonary stage only appeared after 2 weeks of cultivation. The embryo yield per 5 buds reached 25-30 and 5-7 in the sarepta mustard and the rocket salad, respectively. It is worth noticing that the root formation and plant adaptation had passed better and faster in sarepta mustard than in rocket salad. Thus, whole process of homozygous line developing can be completed for 4-5 months, making the breeding program 3 times shorter.

scholarly journals New proposals to strengthen the boom of agriculture in Peru: massive use of genetic resources and development of modern breeding programs

2021 ◽  
Vol 3 (1) ◽  
pp. 35-39
Author(s):  
C. I. Arbizu ◽  
R. H. Blas
Keyword(s):  
Climate Change ◽  
Human Resources ◽  
Private Sector ◽  
Genetic Resources ◽  
Plant Genetic Resources ◽  
Sustainable Use ◽  
Agricultural Activity ◽  
Breeding Programs ◽  
Modern Breeding ◽  
Germplasm Banks

Peru is a place with abundant biological resources that should be employed for the benefit of society in general. However, to date, the use of Peruvian plant genetic resources was not fully exploited for the development of improved crops. This work was mostly conducted by the international private sector. The Climate Change Laboratory at Instituto Nacional de Innovación Agraria, and other laboratories at Universidad Nacional José Faustino Sánchez Carrión and Universidad Nacional Agraria La Molina together with other research programs of other institutions seek to promote the massive and sustainable use of plant genetic resources maintained in germplasm banks. It is planned to make use of modern molecular and morphological techniques. Moreover, infrastructure and human resources are being improved. As a result, we will be able to maintain the growth of the agricultural activity in Peru in terms of space and time.

scholarly journals Assessing the Genetic Diversity of Chrysanthemum Cultivars with Microsatellites

2013 ◽  
Vol 138 (6) ◽  
pp. 479-486 ◽  
Author(s):  
Yuan Zhang ◽  
Chen Wang ◽  
HongZheng Ma ◽  
SiLan Dai
Keyword(s):  
Genetic Diversity ◽  
Magnetic Beads ◽  
Genomic Library ◽  
Gene Diversity ◽  
Sustainable Use ◽  
Morphological Characteristics ◽  
Breeding Programs ◽  
Genetic Studies ◽  
Mean Values ◽  
Cultivar Classification

The morphological characteristics of chrysanthemum (Chrysanthemum ×morifolium) are rich in variation. However, as a result of the aneuploid polyploidy of the chrysanthemum genome and the lack of proper tools, the genomic information of this crop is limited. Development of microsatellite markers has been an increasing trend in crop genetic studies because of the applicability of these markers in breeding programs. In this study, we reported the development of a simple sequence repeat in chrysanthemums using a magnetic beads enrichment method. An enriched genomic library with AC and GT microsatellite motifs was constructed, and 53 positive clones were detected by a colony polymerase chain reaction (PCR) technique. Of these clones, 35 showed high-quality sequences, and 35 primer pairs were designed accordingly. Twenty-six (74.29%) of the 35 primer pairs revealed polymorphisms on a set of 40 chrysanthemum cultivars. There were 172 alleles amplified over 26 loci with an average of 6.615 alleles per locus. The mean values of gene diversity corrected for the sample size and the inbreeding coefficient were 0.609 and 0.119 over 26 loci, respectively, which indicated that the majority of the microsatellite loci is highly informative. Cluster analysis based on 26 polymorphic loci demonstrated that the selected cultivars were clustered according to geographical origin. This study shows the isolation efficiency of the magnetic beads technique; the abundance of microsatellites in chrysanthemum; and the potential application for the cultivar classification, the studies on genetic diversity, and molecular breeding of chrysanthemums, which is beneficial to promoting the conservation and sustainable use of this crop.

scholarly journals KnowMore: an automated knowledge discovery tool for the FAIR SPARC datasets

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1132
Author(s):  
Ryan Quey ◽  
Matthew A. Schiefer ◽  
Anmol Kiran ◽  
Bhavesh Patel
Keyword(s):  
Knowledge Discovery ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Discovery Process ◽  
Data Practices ◽  
Speed Up ◽  
Automated Knowledge ◽  
Data Ecosystem ◽  
Automated Discovery ◽  
Grand Prize

Background: This manuscript provides the methods and outcomes of KnowMore, the Grand Prize winning automated knowledge discovery tool developed by our team during the 2021 NIH SPARC FAIR Data Codeathon. The National Institutes of Health Stimulating Peripheral Activity to Relieve Conditions (NIH SPARC) program generates rich datasets from neuromodulation researches, curated according to the Findable, Accessible, Interoperable, and Reusable (FAIR) SPARC data standards. Currently, the process of simultaneously comparing and analyzing multiple SPARC datasets is tedious because it requires investigating each dataset of interest individually and downloading all of them to conduct cross-analyses. It is crucial to enhance this process to enable rapid discoveries across SPARC datasets. Methods: To fill this need, we created KnowMore, a tool integrated into the SPARC Portal that only requires the user to select their datasets of interest to launch an automated discovery process. KnowMore uses several SPARC resources (Pennsieve, o²S²PARC, SciCrunch, protocols.io, Biolucida), data science methods, and machine learning algorithms in the back end to generate various visualizations in the front end intended to help the user identify potential similarities, differences, and relations across the datasets. These visualizations can lead to a new discovery, new hypothesis, or simply guide the user to the next logical step in their discovery process. Results: The outcome of this project is a SPARC portal-ready code architecture that helps researchers to use SPARC datasets more efficiently and fully leverages their FAIR characteristics. The tool has been built and documented such that more data analysis methods and visualization items could be easily added. Conclusions: The potential for automated discoveries from SPARC datasets is huge given the unique SPARC data ecosystem promoting FAIR data practices, and KnowMore has only demonstrated a small highlight of what could be achieved to speed up discoveries from SPARC datasets.

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