Notes

The result involving Problem-Solving-Approach-Based Coaching upon Maternal Function Edition in Women together with Delayed Preterm Child: A new Randomized Managed Demo.
lar performance to the likelihood ratio method.
A growing proportion of research has proved that microRNAs (miRNAs) can regulate the function of target genes and have close relations with various diseases. Developing computational methods to exploit more potential miRNA-disease associations can provide clues for further functional research.

Inspired by the work of predecessors, we discover that the noise hiding in the data can affect the prediction performance and then propose an anti-noise algorithm (ANMDA) to predict potential miRNA-disease associations. Firstly, we calculate the similarity in miRNAs and diseases to construct features and obtain positive samples according to the Human MicroRNA Disease Database version 2.0 (HMDD v2.0). Then, we apply k-means on the undetected miRNA-disease associations and sample the negative examples equally from the k-cluster. Further, we construct several data subsets through sampling with replacement to feed on the light gradient boosting machine (LightGBM) method. Finally, the voting method is applied to predict potential miRNA-disease relationships. As a result, ANMDA can achieve an area under the receiver operating characteristic curve (AUROC) of 0.9373 ± 0.0005 in five-fold cross-validation, which is superior to several published methods. In addition, we analyze the predicted miRNA-disease associations with high probability and compare them with the data in HMDD v3.0 in the case study. The results show ANMDA is a novel and practical algorithm that can be used to infer potential miRNA-disease associations.

The results indicate the noise hiding in the data has an obvious impact on predicting potential miRNA-disease associations. We believe ANMDA can achieve better results from this task with more methods used in dealing with the data noise.
The results indicate the noise hiding in the data has an obvious impact on predicting potential miRNA-disease associations. We believe ANMDA can achieve better results from this task with more methods used in dealing with the data noise.
Peanut smut is a disease caused by the fungus Thecaphora frezii Carranza & Lindquist to which most commercial cultivars in South America are highly susceptible. It is responsible for severely decreased yield and no effective chemical treatment is available to date. However, smut resistance has been identified in wild Arachis species and further transferred to peanut elite cultivars. To identify the genome regions conferring smut resistance within a tetraploid genetic background, this study evaluated a RIL population susceptible Arachis hypogaea subsp. hypogaea (JS17304-7-B) × resistant synthetic amphidiploid (JS1806) [A. correntina (K 11905) × A. cardenasii (KSSc 36015)] × A. batizocoi (K 9484)
segregating for the trait.

A SNP based genetic map arranged into 21 linkage groups belonging to the 20 peanut chromosomes was constructed with 1819 markers, spanning a genetic distance of 2531.81 cM. read more Two consistent quantitative trait loci (QTLs) were identified qSmIA08 and qSmIA02/B02, located on chromosome A08 and A02/B02, respectively. The QTL qSmIA08 at 15.20 cM/5.03 Mbp explained 17.53% of the phenotypic variance, while qSmIA02/B02 at 4.0 cM/3.56 Mbp explained 9.06% of the phenotypic variance. The combined genotypic effects of both QTLs reduced smut incidence by 57% and were stable over the 3years of evaluation. The genome regions containing the QTLs are rich in genes encoding proteins involved in plant defense, providing new insights into the genetic architecture of peanut smut resistance.

A major QTL and a minor QTL identified in this study provide new insights into the genetic architecture of peanut smut resistance that may aid in breeding new varieties resistant to peanut smut.
A major QTL and a minor QTL identified in this study provide new insights into the genetic architecture of peanut smut resistance that may aid in breeding new varieties resistant to peanut smut.
Gmelina arborea Roxb is a fast-growing tree species of commercial importance for tropical countries due to multiple industrial uses of its wood. Wood is primarily composed of thick secondary cell walls of xylem cells which imparts the strength to the wood. Identification of the genes involved in the secondary cell wall biosynthesis as well as their cognate regulators is crucial to understand how the production of wood occurs and serves as a starting point for developing breeding strategies to produce varieties with improved wood quality, better paper pulping or new potential uses such as biofuel production. In order to gain knowledge on the molecular mechanisms and gene regulation related with wood development in white teak, a de novo sequencing and transcriptome assembly approach was used employing secondary cell wall synthesizing cells from young white teak trees.

For generation of transcriptome, RNA-seq reads were assembled into 110,992 transcripts and 49,364 genes were functionally annotated using plaranscriptomic resource aimed at fostering both basic and breeding studies.
Segmental duplications (SDs) are long DNA sequences that are repeated in a genome and have high sequence identity. In contrast to repetitive elements they are often unique and only sometimes have multiple copies in a genome. There are several well-studied mechanisms responsible for segmental duplications non-allelic homologous recombination, non-homologous end joining and replication slippage. Such duplications play an important role in evolution, however, we do not have a full understanding of the dynamic properties of the duplication process.

We study segmental duplications through a graph representation where nodes represent genomic regions and edges represent duplications between them. The resulting network (the SD network) is quite complex and has distinct features which allow us to make inference on the evolution of segmantal duplications. We come up with the network growth model that explains features of the SD network thus giving us insights on dynamics of segmental duplications in the human genome. Based on our analysis of genomes of other species the network growth model seems to be applicable for multiple mammalian genomes.

Our analysis suggests that duplication rates of genomic loci grow linearly with the number of copies of a duplicated region. Several scenarios explaining such a preferential duplication rates were suggested.
Our analysis suggests that duplication rates of genomic loci grow linearly with the number of copies of a duplicated region. Several scenarios explaining such a preferential duplication rates were suggested.
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