Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Document clustering. --- Computer networks --- Experiments. --- North Atlantic Treaty Organization.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

On the one hand, the demand for locally produced nuts is increasing in Flanders while on the other hand there is a growing interest in agroforestry to help mitigate the environmental problems caused by conventional agriculture. In this context, a project coordinated by ILVO aims to develop agroforestry with walnut trees in Flanders and in the Netherlands. Part of their research focuses on late-budding varieties in order to delay competition for light with the crop and to avoid late frost damage. Seven long-term experimental trials on late-budding walnut trees were launched in 2021. The purpose of the present study was to identify zones with relatively homogeneous soil properties in four of the sites thanks to soil electromagnetic induction scan data clustering and soil analyses. The second objective was to analyse the impact of this zones on the performance of the walnut trees. Moreover, the methodology applied to this specific trial is intended to be applicable to other contexts. In each site, at least one zone with significant differences in terms of soil texture, water table level or water content has been identified. Although the current results are not conclusive as to the impact of these zones, differences in performance in some of them can be expected in the next few years, as the trees are still too young to draw any conclusions at this time. Finally, this method appears to have value in a broader context and can be applied to other agroforestry trials. Criticisms can however be made about the soil sampling method.

Agroforestry --- Soil clustering --- Walnut tree --- Sciences du vivant > Agriculture & agronomie

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Angular correlations (Nuclear physics) --- Clustering phenomena (Nuclear physics) --- Congresses. --- Congresses.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Uncovering and modeling gene regulatory networks (GRNs) is one of the long-standing
challenges in systems biology. This uncovering implies to computationally predict, from
given gene expression data, direct regulatory interactions between transcription factors
and their target genes. All those predicted direct regulatory interactions form a GRN.
Several techniques have been tested to address this problem. Among those, GENIE3 is one
of the top performing methods. However, it has a big disadvantage, which is its slowness.

Using traditional sequencing methods, only the mean of the gene expression values over
a mix of millions of cells could be obtained. The emergence of new techniques allows the
creation of single-cell RNA-seq data, which contain values corresponding to the expression
level in every single cell. It raises two main challenges. First, a computational challenge,
as it creates much bigger expression matrices than traditional methods. Second, we can
now see different cell types in the data, which we were not able to see before, as we only
had means of expression values from different cells. One strategy is to cluster this data so
that each cluster corresponds to a cell type contained in the data.

Our contribution in this context is first to propose a variant of GENIE3 that uses boosting
in order to make it faster and applicable to single-cell datasets. The results obtained are
very promising, as this transforms GENIE3 from a very slow method to a very fast one,
while having the same - and sometimes better - performance. The boosting method has
however the drawback of depending on many parameters. Our second contribution is to
propose three regulatory network-based methods for cell clustering from single-cell data.
Results obtained were not as good as expected but call for more investigations in this way.
Better results could probably be obtained by further analyzing some parameters.

machine learning --- XGBoost --- GRN inference --- clustering --- single-cell --- Ingénierie, informatique & technologie > Sciences informatiques

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The paper describes an exercise of classification of a subset of five-digit categories of the 2007 ATECO classification system of economic activities. The analysis is grounded on the hypothesis that economic sectors can be clustered according to the competency level required to human resources recently working in industries or services in Italy. The analysis may be useful to evaluate a possible relationship between economic development and education. The analysis consisted of a mapping and then a clustering of the Ateco categories according to the between-distribution dissimilarity of any possible couple of categories. The basic idea was to highlight the Ateco categories that require either more education than others or more education and working experience (human capital) than others, pinpointing, in particular, the categories that require larger percentages of tertiary education and those residing close to territorial hubs. The competency level was measured with a combination of educational attainment and in-service experience of Italian employees, as defined by Istat, the Italian statistical institute. The employees’ educational level was evaluated with the frequency distribution of five (ordinal) classes of education of people employed in 2018 and 2019 in both private and public establishments and offices; the working experience with a logarithmic transform of the average number of in-service years of employees. The analysis highlighted both a sort of input-related classification of the economy and a supply-side classification of the labour market. The results are in line with the theory of the existence of a cluster of creative companies residing close to territorial hubs.

Category Mapping --- Ateco 2007 --- Clustering economic categories --- Human capital --- Creative companies