scholarly journals Genome sequence and genetic diversity analysis of an under-domesticated orphan crop, white fonio (Digitaria exilis)

GigaScience ◽  
2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Xuewen Wang ◽  
Shiyu Chen ◽  
Xiao Ma ◽  
Anna E J Yssel ◽  
Srinivasa R Chaluvadi ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Long Terminal Repeat Retrotransposons ◽  
Low Fertility ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Future Research ◽  
Diversity Analysis ◽  
Repeat Content ◽  
High Level ◽  
Digitaria Exilis

Abstract Background Digitaria exilis, white fonio, is a minor but vital crop of West Africa that is valued for its resilience in hot, dry, and low-fertility environments and for the exceptional quality of its grain for human nutrition. Its success is hindered, however, by a low degree of plant breeding and improvement. Findings We sequenced the fonio genome with long-read SMRT-cell technology, yielding a ∼761 Mb assembly in 3,329 contigs (N50, 1.73 Mb; L50, 126). The assembly approaches a high level of completion, with a BUSCO score of >99%. The fonio genome was found to be a tetraploid, with most of the genome retained as homoeologous duplications that differ overall by ∼4.3%, neglecting indels. The 2 genomes within fonio were found to have begun their independent divergence ∼3.1 million years ago. The repeat content (>49%) is fairly standard for a grass genome of this size, but the ratio of Gypsy to Copia long terminal repeat retrotransposons (∼6.7) was found to be exceptionally high. Several genes related to future improvement of the crop were identified including shattering, plant height, and grain size. Analysis of fonio population genetics, primarily in Mali, indicated that the crop has extensive genetic diversity that is largely partitioned across a north-south gradient coinciding with the Sahel and Sudan grassland domains. Conclusions We provide a high-quality assembly, annotation, and diversity analysis for a vital African crop. The availability of this information should empower future research into further domestication and improvement of fonio.

Genetic Diversity Analysis among 27 Indian Goat Populations using Microsatellite Markers

2020 ◽  
Author(s):  
Rajkumar Sah ◽  
Santpal Dixit
Keyword(s):  
Genetic Diversity ◽  
Association Studies ◽  
Research Work ◽  
Diversity Analysis ◽  
National Bureau ◽  
Genetic Diversity Analysis ◽  
Well Differentiated ◽  
Inbreeding Level ◽  
High Level ◽  
Indian Goat

Background: Livestock genetic diversity studies focus on their within and diversity, breed history, adaptive variations, ancestral information, site of domestication and parentage testing and assess the genetic uniformity, admixture or subdivision, inbreeding, or introgression in the population which is helpful in breed formation and their sustainable utilization.Methods: The present research work was conducted during the year 2016-17 at National Bureau of Animal Genetics Resources, Karnal-132001. STR data of 25 markers on 1237 random samples of 27 goat populations was used for analysis. The genetic diversity analysis of new population viz: Narayanpatna, Raighar, Kalahandi, Malkangiri of Odisha state and Rohilkhandi (UK) and their association studies with other Indian goat breeds was performed.Result: It was found that used markers are highly polymorphic- and the studied breeds/population showed great diversity and distributed mostly on the basis of physio-geographical condition and type of production but among new populations diversity was least which might be due to exchange of animal for breeding purposes. The studied new goat populations were well differentiated from other goat breeds which might be due to physio-geographical condition and breeding practices, so these may be considered as separate breeds/populations. In conclusion, the results showed high level of conserved genetic diversity in the Indian goat breeds. The smaller and isolated new population showed less diversity and a higher inbreeding level as compared to registered breeds.

scholarly journals Magnitude and frequency of debris and slush flows in the Khibiny mountain valleys, Kola Peninsula, NW Russia

2019 ◽  
Vol 381 ◽  
pp. 37-47
Author(s):  
Ekaterina V. Garankina ◽  
Vladimir R. Belyaev ◽  
Fedor A. Romanenko ◽  
Maxim M. Ivanov ◽  
Natalia V. Kuzmenkova ◽  
...  
Keyword(s):  
Kola Peninsula ◽  
Extreme Event ◽  
Remote Sensing Data ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Future Research ◽  
Last Deglaciation ◽  
Stream Channels ◽  
14C Dating ◽  
Sediment Delivery

Abstract. Available results for five studied valleys of the Khibiny Mountains, Kola Peninsula, suggest that slush flows and, possibly for some valleys, typical debris flows with lower frequency, are a leading mechanism for downstream sediment delivery and valley floor topographical formation. Typical fluvial topography in slush flow-affected basins is extremely suppressed or nonexistent, since under such conditions, stream channels are unable to rework slush flow deposits. The recovery phase of fluvial topography can serve as an indicator of the magnitude and time passed since the last extreme event. A combination of grain size analysis, radionuclide fingerprinting with the 232Th content in the finer-grained sediment matrix (size <10 mm) and 14C dating, were applied to reveal the age and common structure of debris and slush flow environments and to investigate the main factors in their lithodynamics. Those helped to estimate transportation distances and capacities of the flows and the amount of fluvial reworking of its deposits with time. Application of radiocarbon dating to determine absolute ages (about 30 dates) of stabilization periods for the colluvial cones, mountain fans and valley bottoms and integration with other available chronological data provided a basis for distinguishing several stages of decreased activity of debris and slush flows and extreme slope failures through the second half of the Holocene. Field mapping and remote sensing data interpretation revealed spatial distribution patterns of debris and slush flows. Geomorphic analysis of large relic landforms in valley bottoms confirms, in general, the case for a significant reduction of debris flow magnitude since the last deglaciation and distinct shift to slush flow processes with much lower clastic content. A reliable chronology of the early events is yet to be obtained representing a challenging problem for future research.

scholarly journals Molecular genetic diversity of some rabbit breeds based on mitochondrial 16S rRNA sequences

2021 ◽  
Vol 29 (3) ◽  
pp. 193
Author(s):  
Mohammad Allam ◽  
N. S. Mahrous
Keyword(s):  
Genetic Diversity ◽  
16S Rrna ◽  
Genetic Relationships ◽  
Molecular Genetic ◽  
Future Research ◽  
Diversity Analysis ◽  
Molecular Genetic Diversity ◽  
White Breed ◽  
Rrna Sequences ◽  
16S Rrna Sequences

<p>The present study was performed to assess the genetic variations among six rabbit breeds in Egypt based on mitochondrial 16S rRNA sequences. The length of partial mitochondrial 16S rRNA in the six rabbit breeds ranged from 546 bp to 558 bp. The sequenced regions were submitted to GenBank/NCBI under accession numbers (MW052052 - MW052057). The average content of A+T was 57% in all breeds. Among breeds, the percentages of genetic distance values were ranged from 0.000 to 0.004. The highest P-distance (0.004) was found between the New Zealand White breed and all other breeds. The results support the suitability of mitochondrial 16S rRNA for genetic diversity analysis of rabbit breeds and the applicability for future research on genetic relationships and the phylogeny of rabbit breeds.</p>

scholarly journals Molecular caracterization of a corn core collection–landraces subgroup

2017 ◽  
Vol 12 (1) ◽  
pp. 11
Author(s):  
Ronaldo Rodrigues Coimbra ◽  
Glauco Vieira Miranda ◽  
Newton Portilho Carneiro ◽  
Cláudia Teixeira Guimarães ◽  
Derly José Henriques da Silva ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Core Collection ◽  
Jaccard Index ◽  
Aflp Analysis ◽  
Diversity Analysis ◽  
The Core ◽  
Germplasm Collections ◽  
Molecular Information ◽  
High Level ◽  
Landrace Accessions

The efficient use of genetic resources- stored in germplasm collections can be maximized if morphoagronomic and molecular information on the accessions is made available. To achieve this, a collection that is well-structured, well-curated and easily accessible (the core collection) is required. Consequently, the objective of the current study was to characterize 80 landrace accessions from the maize core collection of the Federal University of Viçosa (UFV), and assay thenngenetic diversity of the various landraces, considering grain type and ecogeographic origin. For this, AFLP analysis was performed using 12 primer combinations. Genetic diversity of the collection was quantified with the UPGMA method, using the Jaccard Index to quantify dissimilarity. The core collection was divided into four sub-populations by grain type, and into six sub-populations based on ecogeographic origin. Genetic diversity analysis was performed both within and between sub-populations. A high level of genetic variability was found among the landrace accessions of UFV Core Collection, principally among those accessions with dentate type grains.Classification by grain type and ecogeographic origin allowed genetically divergent groups to be distinguished.

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

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