Incubación de empresas, actividad emprendedora y generación de conocimiento en el marco de la relación empresa-universidad-gobierno

En la actualidad, el conocimiento y la innovación son factores clave en los procesos de mejora económica y progresión social. Por tal motivo, las lógicas productivas y de articulación social requieren estar orientadas a generar y difundir información y conocimiento. Desde esta perspectiva, los aspectos intangibles de producción basados en el “saber hacer” son la piedra angular de la dinámica económica y social. Esta era, definida como la economía o la sociedad del conocimiento, se identifica como la estructura o sistema que orienta sus actividades productivas, organizacionales, sociales e institucionales para producir, acumular y transmitir conocimiento, basado en el impulso a la innovación, el espíritu emprendedor y el dinamismo económico. Uno de los ámbitos sustanciales al construir comunidades del aber se localiza en la configuración de vínculos entre universidades, industrias y gobiernos, acto trascendental para potenciar el emprendimiento y generar un ambiente innovador. Una manera de materializar este nuevo contexto productivo es mediante las incubadoras de empresas: estructuras capaces de fortalecer las habilidades del emprendedor, al tiempo que crean y transmiten conocimiento para generar nuevas tecnologías, la creación de empleos y el crecimiento económico, tanto en el ámbito local como nacional. La presente obra contiene diversos resultados de investigación que, desde distintos ángulos teóricos y empíricos, permiten analizar cómo emprender e incubar empresas, la gestión del conocimiento en organizaciones y las condiciones regionales de innovación, ofreciendo contribuciones interesantes en temas medulares que ayudan a entender mejor lo que se define como una economía o sociedad del conocimiento.Universidad Autónoma del Estado de Méxic

Data from: High-resolution methylome analysis uncovers stress-responsive genomic hotspots and drought-sensitive TE superfamilies in the clonal Lombardy poplar

The following dataset contains the processed data presented in the article "High-resolution methylome analysis uncovers stress-responsive genomic hotspots and drought-sensitive TE superfamilies in the clonal Lombardy poplar" Supplementary_methods.docx: contain detailed information for the experimental stress treatments, sequencing library preparation, sequencing and DMR calling. BedGraph files (CpG.bed, CHG.bed, CHH.bed): contain methylation levels (%) for each cytosine in the Lombardy poplar genome, in the respective sequence context. The first three columns represent the genomic coordinates of the cytosine, the 56 following columns indicate the methylation levels for each of the samples. Missing values are represented with NA (when particular cytosines were not captured by the sequencing method). DMR_annotation_Populus_nigra_Italica_after_biotic_and_abiotic_treatments.txt: contains all the identified regions that showed significant stress-induced differential methylation (DMR). The file include all annotation for each single DMR: genomic location, genomic feature, gene, TE, sequence context and stress treatment, besides other specific relevant information. sample_IDs_basic_metadata.txt: contains the sample ID and the associated metadata (stress treatment and ortet location and ID) for all samples used in the analysis. Supplementary_file_1_metadata_samples.xlsx: contains the metadata associated to each sample including: sequencing statistics before and after quality and adapter trimming, read mapping and coverage statistics, and number of interrogated cytosines on each sequence context (CpG, CHG, CHH). Supplementary_file_2_GO_enrichments.xlsx: contains the complete results for the GO enrichment analysis for different gene datasets associated to: drought-CHH-DMRs, SINEs, MITEs, SINEs + MITEs. italica_denovo_TE_280920.gff: contains the predicted TEs using the following methodology. First, TEs were de-novo annotated using the Extensive de-novo TE Annotator (EDTA) (version 1.8.3) (https://github.com/oushujun/EDTA) with default parameters, except for option --sensitive: 1, which uses RepeatModeler (version 2.0.1) to identify remaining TEs. All the steps in EDTA pipeline were selected (filter, final and anno) in order to perform whole-genome annotation/analysis after the TE library was constructed. Then, in the annotated library from EDTA, we merged overlapping fragments and fragments located at a close distance (<10bp) in a strand wise manner. The merged fragment was annotated as the family of longer merged fragment. Structural variants derived from nanopore data were used to redefine the boundaries of overlapping TE fragments to be more precise with actual predictions. LINE elements were identified independently by RepeatModeler in order to construct a more comprehensive de-novo TE library. SaliS.fasta: contains the consensus sequences of Salicaceae SINE families (SaliS), the file was built by extracting information from the supplementary table 2 of the publication: "Divergence of 3′ ends as a driver of short interspersed nuclear element (SINE) evolution in the Salicaceae" (https://doi.org/10.1111/tpj.14721) Pnigra_Italica_SaliS.bed: the file contains the annotated SaliS found by blastn over the P. nigra Italica reference genome (-qcov_hsp_perc 90 -perc_identity 70 -word_size 7). Column headers: chr, start, end, length, strand, perc_identity, SaliS family. Pnigra_Italica_all_TEs_for_anno.bed: contains the merged information from italica_denovo_TE_280920.gff and Pnigra_Italica_SaliS.bed. Column headers: chr, start, end, length, strand, perc_identity (only for SaliS), TE superfamily. CXX_ortet_DMRs_merged.bed: contains DMRs merged from all pairwise DMR callings between two ortets. One file per context. Column headers: chr, start, end, number of comparisons where the DMR occur, avg number of cytosines (when called in multiple DMR callings), avg differential methylation vs. control (when called in multiple DMR callings), avg adjusted p value (when called in multiple DMR callings), avg DMR length (when called in multiple DMR callings). SCRIPTS cov_filtering.sh: to filter individual positions according to a custom threshold. unionbedg_with_NAs.sh: to merge information from different samples in a single file taking into account the percentage of missing values per position across the given samples. anovas_and_contrasts_boxplots_barplots_cld.r: to perform statistical tests for the effect of treatments and ortets on the average global methylation. Each sequence context was analyzed separately. CHH_noise_filter.sh: to remove cytosines with invariable methylation values across 90% of the samples. GlobalMethAvg_calculation.r: to calculate global average methylation given a methylation file (CpG.bed, CHG/bed or CHH.bed) and sample file. Hclustering_and_PCAs_analysis.r: to perform hierarchical clustering, principal component analysis and plot the respective figures. ICC_matrices_analysis.r: to calculate intraclass correlation coefficients among all pairwise combinations and plot colored grids Annotations are based on the de novo reference genome of the Populus nigra cv. Italica clone uploaded in the ENA project: PRJEB44889 (www.ebi.ac.uk/ena/browser/view/GCA_950102115). Bisulfite sequencing data can be found under the ENA project: PRJEB5183

Data from: High-resolution methylome analysis in the clonal Populus nigra cv. 'Italica' reveals environmentally sensitive hotspots and drought-responsive TE superfamilies

&lt;p&gt;The following dataset contains the processed data presented in the article&nbsp;&lt;strong&gt;&quot;High-resolution methylome analysis in the clonal Populus nigra cv. &lsquo;Italica&rsquo; reveals environmentally sensitive hotspots and drought-responsive TE superfamilies&lt;/strong&gt;&quot;&lt;/p&gt; &lt;ul&gt; &lt;li&gt;&lt;strong&gt;BedGraph files&nbsp;(CpG.bed, CHG.bed, CHH.bed):&lt;/strong&gt;&nbsp;contain methylation levels (%) for each cytosine in the Lombardy poplar genome, in the respective sequence context. The first three columns represent the genomic coordinates of the cytosine, the 56 following columns indicate&nbsp;the methylation levels for each of the samples. Missing values are represented with NA (when particular&nbsp;cytosines were not captured by the sequencing method).&nbsp;&lt;/li&gt; &lt;li&gt;&lt;strong&gt;DMR_annotation_Populus_nigra_Italica_after_biotic_and_abiotic_treatments.txt: &lt;/strong&gt;contains all the identified regions that showed significant stress-induced differential methylation (DMR). The file include all annotation for each single DMR: genomic location, genomic feature, gene, TE, sequence context&nbsp;and stress treatment, besides other specific relevant information.&lt;/li&gt; &lt;li&gt;&lt;strong&gt;sample_IDs_basic_metadata.txt&lt;/strong&gt;: contains the sample ID and the associated metadata (stress treatment and ortet location and ID) for all samples used in the analysis.&lt;/li&gt; &lt;li&gt;&lt;strong&gt;italica_denovo_TE_280920.gff&lt;/strong&gt;: contains the predicted TEs using the following methodology.&nbsp;First, TEs were de-novo annotated using the Extensive de-novo TE Annotator (EDTA) (version 1.8.3) (https://github.com/oushujun/EDTA) with default parameters, except for option --sensitive:&nbsp;1, which uses RepeatModeler (version 2.0.1) to identify remaining TEs. All the steps in EDTA pipeline were selected (filter, final and anno) in order to perform whole-genome annotation/analysis after the TE library was constructed. Then, in&nbsp;the&nbsp;annotated library from EDTA, we merged overlapping fragments and fragments&nbsp;located at a close distance (&lt;10bp) in a strand wise manner. The&nbsp;merged fragment was annotated&nbsp;as the family of longer merged fragment. Structural variants derived from nanopore data were used to redefine the boundaries of overlapping TE fragments to be more precise with actual predictions.&nbsp;LINE elements were identified independently by RepeatModeler&nbsp;in order to construct a more comprehensive de-novo TE library.&lt;/li&gt; &lt;li&gt;&lt;strong&gt;SaliS.fasta:&lt;/strong&gt;&nbsp;contains the consensus sequences of &lt;strong&gt;Sali&lt;/strong&gt;caceae &lt;strong&gt;S&lt;/strong&gt;INE families (SaliS), the file was built by extracting information&nbsp;from the supplementary table 2 of the publication: &quot;Divergence of 3&prime; ends as a driver of short interspersed nuclear element (SINE) evolution in the Salicaceae&quot; (https://doi.org/10.1111/tpj.14721)&lt;/li&gt; &lt;li&gt;&lt;strong&gt;Pnigra_Italica_SaliS.bed: &lt;/strong&gt;the file contains the annotated SaliS found by blastn over the P. nigra Italica reference genome&nbsp;(-qcov_hsp_perc 90 -perc_identity 70 -word_size 7).&nbsp;Column headers: chr, start, end, length, strand, perc_identity, SaliS family.&lt;/li&gt; &lt;li&gt;&lt;strong&gt;Pnigra_Italica_all_TEs_for_anno.bed:&nbsp;&lt;/strong&gt;contains the merged information from&nbsp;&lt;strong&gt;italica_denovo_TE_280920.gff &lt;/strong&gt;and&lt;strong&gt;&nbsp;Pnigra_Italica_SaliS.bed.&lt;/strong&gt;&nbsp;Column headers: chr, start, end, length, strand, perc_identity (only for SaliS), TE superfamily.&lt;/li&gt; &lt;li&gt;&lt;strong&gt;CXX_ortet_DMRs_merged.bed&lt;/strong&gt;: contains DMRs merged from all pairwise DMR callings between two ortets. One file per context. Column headers: chr, start, end, number of comparisons where the DMR occur, avg number of cytosines (when called in multiple DMR callings), avg differential methylation vs. control (when called in multiple DMR callings), avg adjusted p value (when called in multiple DMR callings), avg DMR length (when called in multiple DMR callings).&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;SCRIPTS&lt;/strong&gt;&lt;/p&gt; &lt;ul&gt; &lt;li&gt;&lt;strong&gt;cov_filtering.sh:&lt;/strong&gt; to filter individual positions according to a custom threshold.&lt;/li&gt; &lt;li&gt;&lt;strong&gt;unionbedg_with_NAs.sh:&lt;/strong&gt; to merge information from different samples in a single file taking into account the percentage of missing values per position across the given samples.&lt;/li&gt; &lt;li&gt;&lt;strong&gt;anovas_and_contrasts_boxplots_barplots_cld.r&lt;/strong&gt;: to perform statistical tests for the effect of treatments and ortets on the average global methylation. Each sequence context was analyzed separately.&lt;/li&gt; &lt;li&gt;&lt;strong&gt;CHH_noise_filter.sh:&lt;/strong&gt; to remove cytosines with invariable methylation values across 90% of the samples.&lt;/li&gt; &lt;li&gt;&lt;strong&gt;GlobalMethAvg_calculation.r&lt;/strong&gt;: to calculate global average methylation given a methylation file (CpG.bed, CHG/bed or CHH.bed) and sample file.&lt;/li&gt; &lt;li&gt;&lt;strong&gt;Hclustering_and_PCAs_analysis.r:&lt;/strong&gt; to perform hierarchical clustering, principal component analysis and plot the respective figures.&lt;/li&gt; &lt;li&gt;&lt;strong&gt;ICC_matrices_analysis.r:&lt;/strong&gt; to calculate intraclass correlation coefficients among all pairwise combinations and plot colored grids&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&nbsp;&lt;/p&gt; &lt;p&gt;Annotations are based on the de novo reference genome of the Populus nigra cv. Italica clone uploaded in the ENA project: PRJEB44889. Bisulfite sequencing data can be found under the ENA project: PRJEB51831&lt;/p&gt

Incubación de empresas, actividad emprendedora y generación de conocimiento en el marco de la relación empresa-universidad-gobierno

La presente publicación representa el esfuerzo de investigación en temas trascendentales para el quehacer académico de la Facultad de Economía, ya que incluye contribuciones sobre temas escasamente tratados desde la vertiente económica. En específico, aborda cuestiones sobre gestión del conocimiento, las incubadoras de empresas, el emprendimiento, el desarrollo rural sustentable, los sistemas de innovación, las ciudades del conocimiento y estudios sobre género y la participación de las mujeres en spin-off universitarias.Universidad Autónoma del Estado de Méxic