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Accompanying the Sustainable development Framework is the Incheon Declaration adopted by around 1600 participants at the World Education Forum held in Incheon, Republic of Korea in May 2015. The Declaration represents the firm commitment of countries and the global education community to a single, renewed education agenda. A Education 2030 Framework for Action was adopted in November 2015 by UNESCO together with Member States, which outlines how to translate global commitments into practice at a country, regional and global level.
KiCad is an open source software used to capture and design the printed electronic circuit boards. This inbuilt tool allows the user to create a bill of materials, artwork, Gerber files, and 3D views of the PCB and its respective components. It is really so simple to install KiCad 4.0.7 on Linux Mint 18.3 and this tutorial covers the ground on the same process.
The only form you need to sign is the contributor agreement, which is fully automated via the web.As the image below says \"This establishes the terms and conditions for your contributions and ensures that source code can be licensed appropriately\"
With your code rebased from original master and pushed to your personal GitHub area, you can now submit your work as a pull request.If you look at the top of the page in GitHub for your work area their will be a \"Pull Request\" button.Selecting this will then provide a gui to automate the submission of your pull request.
You can add as many Drools runtimes as you need.For example, the screenshot below shows a configuration where three runtimes have been defined: a Drools 4.0.7 runtime, a Drools 5.0.0 runtime and a Drools 5.0.0.SNAPSHOT runtime.The Drools 5.0.0 runtime is selected as the default one.
Methods of the CommandFactory create the supported commands, all of which can be marshalled using XStream and the BatchExecutionHelper. BatchExecutionHelper provides details on the XML format as well as how to use Drools Pipeline to automate the marshalling of BatchExecution and ExecutionResults.
Note that, due to the lazy nature of the phreak algorithm used by Drools, the activations are by default materialized only at firing time, but it is possible to anticipate the evaluation and then the activation of a given rule at the moment when a fact is inserted into the session by annotating it with @Propagation(IMMEDIATE) as explained in the Propagation modes section.
The behavior of the Conditional Element or is different from the connective for constraints and restrictions in field constraints.The Drools engine actually has no understanding of the Conditional Element or.Instead, via a number of different logic transformations, a rule with or is rewritten as a number of subrules.This process ultimately results in a rule that has a single or as the root node and one subrule for each of its CEs.Each subrule can activate and fire like any normal rule; there is no special behavior or interaction between these subrules.- This can be most confusing to new rule authors.
Domain Specific Languages (or DSLs) are a way of creating a rule language that is dedicated to your problem domain.A set of DSL definitions consists of transformations from DSL \"sentences\" to DRL constructs, which lets you use of all the underlying rule language and engine features.Given a DSL, you write rules in DSL rule (or DSLR) files, which will be translated into DRL files.
We predicted 19,192 genes that produced 19,192 proteins in CamFer2. Of these genes, 3.69% (708) did not match proteins from UniProt/Swiss-Prot. There were many structural variations (inversions and repeats) when comparing the assembled chromosomes of CamFer2 and the C. ferus genome assembly from Ming et al., [12] (Supplemental Fig. 2). Ultimately, these latter genomes have similar scaffold N/L50 values, but CamFer2 has much smaller contig N/L50 values because of more abundant and larger gaps in assembled chromosomes (Supplemental Table 1). The CamFer2 assembly and these annotations have been submitted to Dryad - see Data Accessibility Statement.
Old World camels are known to be resistant to serious infectious diseases that threaten other livestock species inhabiting the same geographical regions, although they may contract other poorly-studied diseases [35]. On the other hand, diseases of Camelidae are often difficult to deal with, having non-specific signs with a considerable economic impact [36]. Hence, as diversity in immune response gene regions may influence infectious disease susceptibility in populations, a better understanding of IR gene diversity will support camel breeding and sustainable management in countries of the Global South with large camel populations. As our data were not normally distributed and could not be transformed to approximate a normal distribution, we assessed differences in nucleotide diversity within species in different immune complexes of the genome by using a non-parametric bootstrapping method to estimate 95% confidence intervals of mean nucleotide diversity (Fig. 3 and Supplemental Fig. 3).
MHC class I and class II genes are amongst the most polymorphic genes studied in vertebrates [37]. Pathogen-mediated selection is widely held to be the major driving force in maintaining the high diversity at MHC loci [38]. In particular, the MHC diversity in populations is maintained by balancing selection [39]. According to the 95% confidence intervals derived from non-parametric bootstrap tests of mean nucleotide diversities, we observed that MHC (class I and II) genes had higher mean nucleotide diversity compared to all other gene groups, for two-humped camels, in both SNPs-indels and just non-synonymous SNPs analyses, and for dromedaries in SNP-indels analysis but not for only non-synonymous SNP analysis (Fig. 3). Previous research by Plasil et al., [4] showed that MHC nucleotide diversity within the three Old World species was generally low. In this case, the authors looked specifically into the antigen-binding sites and not to the complete genes where, according to our results, additional diversity appears to be present. The functional importance of this variation is currently unknown. However, it is important to acknowledge how particular pathogens affect immune genetic diversity and, vice versa, how genetic variation influences adaptation to emerging zoonosis, habitat fragmentation, and climate change [40]. MHC genes play an important role in the adaptive branch of the immune system and have been used extensively to estimate levels of adaptive genetic variation [41]. While innate immunity is an efficient first protection against many pathogens but rather less specific, adaptive (or acquired) immunity is a highly specific immune response, and its variability is subject to different selective pressures [30, 31]. Overall, mean nucleotide diversity was never different when comparing innate and adaptive IR gene groups in all three species, in both SNPs-indels and non-synonymous SNPs analyses.
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In all tissues of both tetraploids, the homoeolog expression levels were notably lower than the P. guichenoti orthologs (Supplementary Fig. 60 and Supplementary Table 30). In each tissue, the expression levels of 2,096 pseudo-ancestral genes in the common carp and goldfish increased, close to or higher than the diploid orthologs, hinting at the dosage compensation (Fig. 4a and Supplementary Fig. 60). In total, 1,451 (69.2%) pairs and 1,916 (91.4%) of the 2,096 common carp homoeologs were cotranscribed in 9 tissues and in at least 3 tissues, respectively (Supplementary Table 31). The proportions of cotranscribed goldfish homoeologous pairs approximated to those in common carp. These data suggest that the homoeologs were subject to cotranscription to maintain the dosage compensation.
For common carp, de novo contig assembly was developed using raw PacBio reads and Nanopore reads using wtdbg2 (ref. 43). The contigs were error corrected with long reads using racon v.1.3.1 (ref. 44) and polished with cleaned Illumina reads using pilon v.1.22 (ref. 45). The contigs of wtdbg2 assembly and previously published assembly14 were assembled into longer contigs using quickmerge46. The contigs were scaffolded using the mate-pair libraries with SSPACE v.3.0 (ref. 47) and Platanus v.1.2.4 (ref. 48). The gaps in the scaffolds were closed with reads from the paired-end libraries using Platanus v.1.2.4 and further filled with long reads using LR_Gapcloser v.1.0 (ref. 49). The assembly pipelines for P. guichenoti and P. tetrazona are described in Supplementary Methods 2.
The genome coverage of each assembly was assessed by aligning the cleaned Illumina genome-seq reads with BWA v.0.7.17 (ref. 57) and aligning the cleaned Illumina RNA-seq reads to the genome using HISAT 2 (v.2.1.0)58. The genome contiguity was measured based on insert size distribution and Hi-C contact signals. We compared the actual insert size distribution with the estimated insert size of each paired-end/mate-pair library, determined by aligning reads to the genome using BWA v.0.7.17. The genome contiguity was confirmed by mapping Hi-C data using HiCPlotter59. The common carp genetic markers were aligned to the assembly using BLAT (v.35X1), and the correlation between sequence distance and genetic distance was used to estimate the assembly contiguity. The assessment of common carp assembly improvement was described in Supplementary Methods 3. 153554b96e
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