ClustGeo: an R package for hierarchical clustering with spatial constraints

In this paper, we propose a Ward-like hierarchical clustering algorithm including spatial/geographical constraints. Two dissimilarity matrices $D_0$ and $D_1$ are inputted, along with a mixing parameter $\alpha \in [0,1]$. The dissimilarities can be non-Euclidean and the weights of the observations can be non-uniform. The first matrix gives the dissimilarities in the "feature space" and the second matrix gives the dissimilarities in the "constraint space". The criterion minimized at each stage is a convex combination of the homogeneity criterion calculated with $D_0$ and the homogeneity criterion calculated with $D_1$. The idea is then to determine a value of $\alpha$ which increases the spatial contiguity without deteriorating too much the quality of the solution based on the variables of interest i.e. those of the feature space. This procedure is illustrated on a real dataset using the R package ClustGeo

Drug Absorption Modeling as a Tool to Define the Strategy in Clinical Formulation Development

The purpose of this mini review is to discuss the use of physiologically-based drug absorption modeling to guide the formulation development. Following an introduction to drug absorption modeling, this article focuses on the preclinical formulation development. Case studies are presented, where the emphasis is not only the prediction of absolute exposure values, but also their change with altered input values. Sensitivity analysis of technologically relevant parameters, like the drug's particle size, dose and solubility, is presented as the basis to define the clinical formulation strategy. Taking the concept even one step further, the article shows how the entire design space for drug absorption can be constructed. This most accurate prediction level is mainly foreseen once clinical data is available and an example is provided using mefenamic acid as a model drug. Physiologically-based modeling is expected to be more often used by formulators in the future. It has the potential to become an indispensable tool to guide the formulation development of challenging drugs, which will help minimize both risks and costs of formulation developmen

Multivariate Analysis of Mixed Data: The R Package PCAmixdata

Mixed data arise when observations are described by a mixture of numerical and categorical variables. The R package PCAmixdata extends standard multivariate analysis methods to incorporate this type of data. The key techniques/methods included in the package are principal component analysis for mixed data (PCAmix), varimax-like orthogonal rotation for PCAmix, and multiple factor analysis for mixed multi-table data. This paper gives a synthetic presentation of the three algorithms with details to help the user understand graphical and numerical outputs of the corresponding R functions. The three main methods are illustrated on a real dataset composed of four data tables characterizing living conditions in different municipalities in the Gironde region of southwest France

Advancing Pharmaceutical Dry Milling by Process Analytics and Robustness Testing

The objectives of this work were to implement on-line dynamic image analysis and to introduce a novel at-line flowability analyzer in pharmaceutical dry milling. We used a pilot-scale conical mill and flowability of a placebo granulate was monitored using a powder avalanching analyzer. Experiments were designed and evaluated by means of response surface methodology in conjunction with robustness testing. The process parameters impeller speed and screen size significantly affected the particle size distribution and flow rate of the milled granules. Feeder speed did not affect the particle size, but displayed a statistically significant influence on the flow responses. Robustness testing was able to capture the effect of noise factors on the responses and showed clear differences between different lots of the placebo granulate in addition to temperature-dependent changes in flow behavior. Thus, on-line dynamic image analysis and at-line flowability characterization, together as complementary process analytical tools, provided valuable information. The combined analysis was of particular interest for testing the process and noise factors so that future process development can profit from this advancement in dry millin

Introduction of a Theoretical Splashing Degree to Assess the Performance of Low-Viscosity Oils in Filling of Capsules

These days an alternative to soft capsules is liquid-filled hard capsules. Their filling technology was investigated earlier with highly viscous formulations, while hardly any academic research focused on low-viscosity systems. Accordingly, this work addressed the filling of such oils that are splashing during the dosing process. It was aimed to first study capsule filling, using middle-chain triglycerides as reference oil, in order to then evaluate the concept of a new theoretical splashing degree for different oils. A laboratory-scale filling machine was used that included capsule sealing. Thus, the liquid encapsulation by microspray technology was employed to seal the dosage form. As a result of the study with reference oil, the filling volume and the temperature were found to be significant for the rate of leaking capsules. The filling volume was also important for weight variability of the capsules. However, most critical for this variability was the diameter of the filling nozzle. We proposed a power law for the coefficient of weight variability as a function of the nozzle diameter and the obtained exponent agreed with the proposed theory. Subsequently, a comparison of different oils revealed that the relative splashing degree shared a correlation with the coefficient of the capsule weight variability (Pearson product moment correlation of r = 0.990). The novel theoretical concept was therefore found to be predictive for weight variability of the filled capsules. Finally, guidance was provided for the process development of liquid-filled capsules using low-viscosity oil

Validity of a Power Law Approach to Model Tablet Strength as a Function of Compaction Pressure

Designing quality into dosage forms should not be only based on qualitative or purely heuristic relations. A knowledge space must be generated, in which at least some mechanistic understanding is included. This is of particular interest for critical dosage form parameters like the strength of tablets. In line with this consideration, the scope of the work is to explore the validity range of a theoretically derived power law for the tensile strength of tablets. Different grades of microcrystalline cellulose and lactose, as well as mixtures thereof, were used to compress model tablets. The power law was found to hold true in a low pressure range, which agreed with theoretical expectation. This low pressure range depended on the individual material characteristics, but as a rule of thumb, the tablets having a porosity of more than about 30% or being compressed below 100MPa were generally well explained by the tensile strength relationship. Tablets at higher densities were less adequately described by the theory that is based on large-scale heterogeneity of the relevant contact points in the compact. Tablets close to the unity density therefore require other theoretical approaches. More research is needed to understand tablet strength in a wider range of compaction pressure

L’industrie du sucre et du sirop d’érable au canada

Kuentz L. L’industrie du sucre et du sirop d’érable au Canada. In: La Terre et La Vie, Revue d'Histoire naturelle, tome 3, n°6, 1933. pp. 355-361

Les effets de l'adoption obligatoire des normes IFRS sur les incorporels : le cas de la France.

International audienceCet article examine les effets de l'adoption obligatoire des IFRS sur les incorporels, dans le contexte français. Utilisant un échantillon de 83 entreprises issues du SBF 120, nous recherchons une typologie des pratiques comptables liées aux incorporels à la période de transition aux IFRS. Les résultats font ressortir trois classes d'entreprises affectées différemment par le passage aux normes internationales. La première classe est caractérisée par un changement important avec une forte augmentation du goodwill liée au retraitement d'immobilisations incorporelles comme les parts de marché. Elle permet d'illustrer la spécificité de la réglementation française. La deuxième classe se caractérise par une stabilité s'expliquant par le poids prédominant du goodwill sous référentiel français. Enfin la troisième classe ne subit pas non plus de changement compte tenu de la présence de marques en normes françaises. Le phénomène d'inertie décrit par Nobes (2006) selon lequel les traitements comptables pré-IFRS pourraient perdurer sous IFRS est vérifié

ClustOfVar-based approach for unsupervised learning: Reading of synthetic variables with sociological data

This paper proposes an original data mining method for unsupervised learning, replacing traditional factor analysis with a system of variable clustering. Clustering of variables aims to group together variables that are strongly related to each other, i.e. containing the same information. We recently proposed the ClustOfVar method, specifically devoted to variable clustering, regardless of whether the variables are numeric or categorical in nature. It simultaneously provides homogeneous clusters of variables and their corresponding synthetic variables that can be read as a kind of gradient. In this algorithm, the homogeneity criterion of a cluster is defined by the squared Pearson correlation for the numeric variables and by the correlation ratio for the categorical variables. This method was tested on categorical data relating to French farmers and their perception of the environment. The use of synthetic variables provided us with an original approach of identifying the way farmers reconfigured the questions put to them

ClustOfVar: An R Package for the Clustering of Variables

Clustering of variables is as a way to arrange variables into homogeneous clusters, i.e., groups of variables which are strongly related to each other and thus bring the same information. These approaches can then be useful for dimension reduction and variable selection. Several specific methods have been developed for the clustering of numerical variables. However concerning qualitative variables or mixtures of quantitative and qualitative variables, far fewer methods have been proposed. The R package ClustOfVar was specifically developed for this purpose. The homogeneity criterion of a cluster is defined as the sum of correlation ratios (for qualitative variables) and squared correlations (for quantitative variables) to a synthetic quantitative variable, summarizing "as good as possible" the variables in the cluster. This synthetic variable is the first principal component obtained with the PCAMIX method. Two algorithms for the clustering of variables are proposed: iterative relocation algorithm and ascendant hierarchical clustering. We also propose a bootstrap approach in order to determine suitable numbers of clusters. We illustrate the methodologies and the associated package on small datasets