Ascendant Hierarchical Clustering Research Articles

Preliminary study of the social and demographic profile of street food in Côte d'Ivoire: Eaters of garba.

The study aims to establish the sociodemographic profile of consumers of garba (a street food sold in Côte d'Ivoire), understand its frequency of consumption, and develop a typology of its consumers. We interviewed 547 consumers at 10 points of sale. A factorial analysis of the data collected was performed and a decision tree produced for the social and demographic variables, followed by a multiple correspondence analysis (MCA) and then a hierarchical ascending clustering (HAC). Garba is consumed more by men (70.6%), students (42%), and single people (68.2%). The consumers are mainly Christian (51.7%) or Muslims (35.8%). The principal deciding factors explaining the frequency of individual consumption are age, occupation, and marital status. Four classes of consumers are defined according to the socioprofessional category of the individuals. Garba is highly consumed in 47% of cases, moderately consumed in 35.1% of cases and poorly consumed in 17.9%. Garba is frequently consumed by all sociodemographic levels. Its consumers can be divided into four classes. In view of its high frequency of consumption, a study of its nutritional quality is necessary to ensure guarantee consumer health.

Taking into account Qualitative and Textual Variables in Hierarchical Ascending Clustering (HAC)

In Machine Learning, the clustering methods are the mains unsupervised methods. Their objectives is to partition a set of objects in some homogeneously groups. Clustering methods in general and more particularly Hierarchical Ascending Clustering (HAC) techniques are based on metrics and ultra-metrics. Metrics are used to evaluate the similarities between two objects; and ultra-metrics are used to estimate the similarity of two groups or the similarity of an element and a group. The main characteristic of these metrics and ultra-metrics is the fact that they are only adapted to numerical variables or can be reduced to them. With the advent of Data Mining and Data Science, most of the datasets to be analyzed contain different types of variables. In the same dataset, we can find numeric attributes, qualitative variables and free text fields very often together. Despite this diversity of variables in the same dataset, the existed clustering methods are generally build to use only an unique kind of attribute. In this paper, we propose an approach to take account different types of attributes in the same clustering method. The method proposed is a variant of HAC methods that can take into account both numerical, qualitative and textual data. Our approach is based on a metric call Phi-Similarity we developed in order to estimate the proximity of two objects, each of them is describe by a vector of attributes of different types. The developed method will be implemented with the scientific computing language R and applied to real survey data. A comparison of the results will be made with HAC techniques based on classical metrics with the Ward criterion as aggregation criteria. For classical algorithms, we will limit ourselves to the variables of the database compatible with them. This work of comparison will highlight the gain in precision in terms of classification brought by our method compared to the classic versions of HAC

A new architecture based VAD for speaker diarization/detection systems

This paper deals with the problem of Voice Activity Detection (VAD) used in speaker diarization and detection systems to detect silent areas in speech stream. In fact, an incorrect VAD can cause serious performances degradation of these systems. To overcome this problem, we propose in this work, a new VAD architecture which combines two VAD. The first one is employed in the temporal domain and the second one is performed in the cepstral domain. Classical VAD architectures used either temporal VAD or cepstral one to eliminate the silent segments in audio documents. In this work, we propose to use the both VAD in order to reduce silent detection errors. This new VAD architecture is evaluated on speaker diarization and detection systems using the BIC distance (Bayesian Information Criterion), two different classifiers, Gaussian Mixture Model (GMM) classifier for the segmentation task and Hierarchical Ascending Clustering (HAC) or Support Vector Machine (SVM) classifier for the clustering task. A comparative investigation was done between our new proposed VAD architecture and the classical ones applied in the preprocessing step of the speaker diarization/detection systems. For the evaluation task, the different VAD architectures were tested on telephonic conversations extracted from the NIST-2005 corpus. The results of experiments have shown that the new VAD architecture has considerably enhanced the performances of speaker diarization/detection systems, comparing to the classical VAD architectures. The results obtained by the new VAD architecture are illustrated by a Diarization Error Rate (DER) of only 2.78% and a Speaker Detection Rate (SDR) of 97.67%.

Aromatic Plants from Plateau des Cataractes: Occurrence of the Citronella Chemotype of Cymbopogon flexuosus (Nees ex Steud.) W.Watson Acclimatized in Congo-Brazzaville.

Cymbopogon flexuosus var. flexuosus (citral chemotype) was introduced in a local plantation in 2006 at Plateau des Cataractes, Congo-Brazzaville to remedy the difficult adaptation and low production of essential oil of C. citratus, a source of citral. After some 10 years of acclimatization, C. flexuosus had adapted perfectly with a very high biomass and essential oil production. A citronella chemotype (Cymbopogon flexuosus var. albescens) was unexpectedly identified among lemongrass in the field, grown in a local experimental citronella plot covering 0.25 ha. Its essential oil was extracted by hydrodistillation and analyzed by GC and GC/MS to determine its chemotype. The citronella chemotype indicated by the chemical profile found was confirmed by descriptive statistics (radar plot) and by principal component analysis (PCA) and ascending hierarchical clustering (AHC).

Scelte strategiche e pratiche ambientali nelle Pmi alberghiere. Verso un nuovo modello di business sostenibile

Purpose of the paper: Analysis of the environmental sustainability strategies of small and medium hotel enterprises (SMHEs) in community destinations. The goal is to investigate whether and how the adoption of environmental management practices (EMPs) have changed, and whether and how local key stakeholders have influenced eco-friendly behaviour. Methodology: The research carried out in 2015 in joint with the Statistical Office of Province of Trento involved all the hotels of Trentino (N=1,514) though a CAWI survey (redemption rate 88.9%). The data analysis followed two steps: 1) frequencies analysis addressed to identify the EMPs adopted and to compare the data with the survey conducted in 2005; 2) Multiple Correspondence Analysis and hierarchical ascendant cluster analysis to identify hotel profiles with different environmental commitment. Findings: Increase of EMPs investments. The adoption of EMPs depends on the “proactive” and “reactive” clusters sustainability awareness, on the acknowledged ethical value and on the stakeholders’ power and pressure on the DMO. Research limits: Focus on a single community destination. Practical implications: Small hotels can also adopt EMPs but that would require in primis a change in the traditional business model adding environmental sustainability aims. Stakeholder pressure supports the enterprise strategies. Originality of the paper: The research contributes to the scientific debate on EMPs adoption and stakeholder power and pressure on environmental sustainability strategies of SMHEs.

Hotel Profiles Based on Environmental Management Practices: Evidence from a Study at an Alpine Destination

This paper analyzes the investment in environmental management practices (EMPs) adopted by hotels within a community destination. The aim is twofold: (a) to understand whether hotels have changed their EMPs in the last ten years and, if so, how; (b) to analyze the link between hotels’ environmental commitment and propensity of collaboration among local stakeholders. The research, carried out in 2015, involved all the hotels (N = 1.514) of Trentino, a community destination in the Italian Alps, through a CAWI survey (redemption rate 88.9%). The data analysis followed two steps: (1) frequency analysis to identify the EMPs adopted by hotels and to compare the data with the survey conducted in 2005; (2) multiple correspondence analysis and hierarchical ascendant cluster analysis to identify hotel profiles with different levels of environmental commitment. The results revealed new and original aspects of the propensities of hotels to invest in EMPs and identified three different environmental hotel profiles: not eco-friendly, proactive, and reactive. Proactive and reactive hotels have a greater propensity to invest in EMPs and to collaborate with the Destination Management Organisation and with other hotels. The research contributes to the scientific debate on EMP adoption and the extent to which stakeholder power and pressure influence the environmental sustainability strategies of hotels.

What stockperson behavior during weighing reveals about the relationship between humans and suckling beef cattle: A preliminary study

The human-animal relationship is a key component of human and farm-animal welfare. Farm surveys, particularly for pig, dairy cattle and veal calf production, which include regular human contact, showed that animals may face aversive handling during daily work routines, which can significantly increase their fear of humans. Similar surveys of stockperson behavior towards suckling beef cattle, which are handled less often, are rare. The aim of this preliminary study was therefore to develop a tool for assessing stockperson behavior during the occasional but common weighing procedure on commercial beef farms and then to test its potential to indicate the general human-animal relationship on such farms. Two hundred and thirty interaction sequences of stockpeople handling calves on six farms were filmed during weight monitoring. During them, stockpeople’s vocal and tactile interactions and locomotion were recorded in detail. Additionally, an avoidance test with an unfamiliar human was performed with the farm’s heifers on pasture. Multiple correspondence analysis followed by ascendant hierarchical clustering (AHC) was performed on the sequences to identify groups of practices that stockpeople used during handling. Next, the relationship between the AHC groups according to the time spent weighing and the number of calf vocalizations was analyzed using one-way analyses of variance followed by pairwise comparisons (with Bonferroni correction). Three AHC groups of handling styles that including positive and negative features were identified: (1) mostly “negative” practices (67 sequences) such as hitting calves, twisting their tails or not talking to them; (2) “neutral” practices (62 sequences) such as talking to calves, moving quickly and pushing the calves and (3) mostly “positive” practices (83 sequences) such as touching the calves, not hitting or pushing them and not twisting their tails. The group using “positive” practices spent significantly less time weighing a calf than the two other groups (p < 0.01). In addition, the calves vocalized less with the group using “positive” practices than with that using “neutral” practices. Like for animal production systems that include daily routine handling, our study suggests relationships between stockperson practices during one-time handling events and animal reactions to humans on commercial beef farms. Observing stockpeople during less common handling events could identify poor human-animal relationships in suckling beef cattle production.

Characterization of spatial and temporal combinations of climatic factors affecting yields: An empirical model applied to the French barley belt

The adaptation of genotypes to environmental conditions is one of the main levers for maintaining an acceptable level of production, both in terms of quality and quantity. To breed suitable genotypes and for the farmers to choose the most adapted one to his farm conditions, the factors affecting production must be precisely characterized. Here, we analyzed the impacts of the climatic factors on winter barley yield in 35 départements (French geographic units) over 25 years, by partial least squares (PLS) regression analysis. Using ascendant hierarchical clustering based on PLS results, we defined the main combinations of climatic factors affecting yield in the French barley belt, hereafter referred as “climatic-stress patterns” (CPs).Four CPs captured 27% of total yield variability and widely differ in term of yield. Crops experienced low winter rainfall and few days with heat stress (31.8% of environments- mean yield of 7.2 t ha−1), high winter frost levels and high amounts of precipitation during stem elongation (34.7% of environments- mean yield of 6.5 t ha−1), high temperature during grain filling either with low vernalization temperatures (28.9% of environments- mean yield of 6.2 t ha−1), or high winter rainfall (4.6% of environments –mean yield of 5.5 t ha−1). Two-thirds of the French départements experienced all the four CPS over the years studied. Three clusters of regions with homogeneous frequencies of the CPs were identified.The regions with similar climatic-stress patterns could help breeders to design genotypes better adapted to the different local French growing conditions. It could also help farmers to choose the most appropriate cultivars to grow.

Disentangling Tourette syndrome heterogeneity through hierarchical ascendant clustering.

To explore the heterogeneity of Tourette syndrome as part of a neurodevelopmental spectrum. Using hierarchical ascendant clustering based on tic symptoms, developmental milestones, and neurodevelopmental comorbidities, we analyzed the heterogeneity of Tourette syndrome phenotypes in a sample of 174 children and adolescents with Tourette syndrome referred to a tertiary university clinic. The model yielded three distinct clusters characterized as follows. In cluster 1, we found many neurodevelopmental comorbidities (including intellectual disabilities, autism spectrum disorder, attention-deficit-hyperactivity disorder [ADHD], and learning disabilities) and academic impairments. In cluster 2, patients had no other neurodevelopmental comorbidities. In cluster 3, patients had higher intelligence, a high frequency of attentional impairment, school problems related to both ADHD and unspecific attention difficulties, and handwriting problems related to the tics themselves. Interestingly, clusters did not differ in terms of family history or anxious-depressive comorbidities. The only other differences that emerged were related to prenatal or perinatal risk factors (more represented in cluster 1) and treatment profiles (higher rates of stimulants in cluster 1). We conclude that from a phenotypical perspective, Tourette syndrome is a heterogeneous syndrome with at least three main clusters that may help in addressing the etiological basis of Tourette syndrome and specific rehabilitative and therapeutic approaches. The clustering of Tourette syndrome based on comorbidity with other neurodevelopmental conditions reveals three clusters. A group of patients with Tourette syndrome show school difficulties related to non-specific attention and writing problems. Analysing only children and adolescents helps to distinguish between developmental comorbid conditions and coexistent disorders.

Identification of new sources of resistance for pearl millet downy mildew disease under field conditions

AbstractPearl millet is an important cereal crop for smallholder farmers’ food security in Africa and India. However, its production has stagnated due to several factors such as downy mildew (DM). Thus, a study was conducted to identify new sources of resistance from pearl millet inbred lines derived from a collection of landraces originated from West and Central African countries. A set of 101 lines, including 99 inbred lines from West and Central Africa along with a 7042S and SOSAT C 88 as susceptible and resistant checks, respectively, were evaluated under field conditions with infector rows in Bambey and Nioro research stations during the rainy season of 2016. Data on DM incidence and severity, plant height, flowering time, panicle length and productive tillers were recorded. The results showed highly significant differences among lines for all observed traits. Among the tested lines, 55 including SOSAT C 88 were resistant, 16 moderately resistant and 30 including 7042S were susceptible to the disease. Out of the 55 resistant lines, 20 were disease free. Hierarchical ascendant cluster analysis grouped the lines into three clusters with the DM parameters and plant height as the most discriminant factors. Cluster II contains the susceptible lines, while cluster III comprises the moderately resistant lines. The lines which belongs to cluster I were characterized by their resistance to the disease. Further use of these available new sources of resistance will be very useful for improvement of pearl millet for DM resistance and other agronomic traits.

A Classification of Manuscripts Based on A New Quantitative Method. The Old Latin Witnesses of John's Gospel as Text Case

A new method for grouping manuscripts in clusters is presented with the calculation of distances between readings, then between witnesses. A classification algorithm (" Hierarchical Ascendant Clustering "), achieved through computer-aided processing, enables the construction of trees illustrating the textual taxonomy obtained. This method is applied to the Old Latin witnesses of the Gospel of John, and, in order to provide a study of a reasonable size, to a chapter as a whole (chapter 14). The result basically confirms the text-types identified by Bonatius Fischer, founder of the Vetus Latina Institute, while it invalidates the classification adopted by the current edition of the Vetus Latina of the Gospel of John.

An introduction to wireline log analysis by integration of ascendant hierarchical clustering and k-nearest neighbor methods for permeability prediction using conventional well logs and core data

Permeability is a key parameter in the evaluation of hydrocarbon reservoirs. Conventionally, the core data analysis is utilized to determine the permeability. However, core analysis method is time-consuming and expensive. In this paper, we present an approach for permeability prediction from conventional well logs and core data. Our approach integrates two methods, the ascendant hierarchical clustering, which is used to classify the well log responses into relatively homogeneous subgroups based on electrofacies, and the k-nearest neighbor method, to predict the permeability from the nearest neighbors that belong to a similar subgroup. The procedure is applied on one of the Iranian South West oil reservoirs. The results of this study show that integration of well logs and core data based on electrofacies can reliably predict permeability.

Clustering of longitudinal data by using an extended baseline: A new method for treatment efficacy clustering in longitudinal data.

Heterogeneity in treatment efficacy is a major concern in clinical trials. Clustering may help to identify the treatment responders and the non-responders. In the context of longitudinal cluster analyses, sample size and variability of the times of measurements are the main issues with the current methods. Here, we propose a new two-step method for the Clustering of Longitudinal data by using an Extended Baseline. The first step relies on a piecewise linear mixed model for repeated measurements with a treatment-time interaction. The second step clusters the random predictions and considers several parametric (model-based) and non-parametric (partitioning, ascendant hierarchical clustering) algorithms. A simulation study compares all options of the clustering of longitudinal data by using an extended baseline method with the latent-class mixed model. The clustering of longitudinal data by using an extended baseline method with the two model-based algorithms was the more robust model. The clustering of longitudinal data by using an extended baseline method with all the non-parametric algorithms failed when there were unequal variances of treatment effect between clusters or when the subgroups had unbalanced sample sizes. The latent-class mixed model failed when the between-patients slope variability is high. Two real data sets on neurodegenerative disease and on obesity illustrate the clustering of longitudinal data by using an extended baseline method and show how clustering may help to identify the marker(s) of the treatment response. The application of the clustering of longitudinal data by using an extended baseline method in exploratory analysis as the first stage before setting up stratified designs can provide a better estimation of treatment effect in future clinical trials.

Evaluation of a hierarchical ascendant clustering process implemented in a veterinary syndromic surveillance system

Syndromic surveillance is considered as one of the surveillance components for early warning of health-related events, as it allows detection of aberrations in health indicators before laboratory confirmation. “MoSS-Emergences 2” (MoSS-E2), a tool for veterinary syndromic surveillance, aggregates groups of similar clinical observations by hierarchical ascendant classification (HAC).In the present study, this HAC clustering process was evaluated using a reference set of data that, for the purpose of this evaluation, was a priori divided and defined as Bluetongue (BTV) positive cases (PC) on the one hand and BTV negative cases (NC) on the other hand. By comparing the clustering result of MoSS-E2 with the expected outcome, the sensitivity (the ability to cluster PC together) and specificity (the ability to exclude NC from PC) of the clustering process were determined for this set of data. The stability of the classes obtained with the clustering algorithm was evaluated by comparing the MoSS-E2 generated dendrogram (applying complete linkage) with dendrograms of STATA® software applying average and single linkage methods. To assess the systems’ robustness, the parameters of the distance measure were adjusted according to different scenarios and obtained outcomes were compared to the expected outcome based on the a priori known labels. Rand indexes were calculated to measure similarity between clustering outcomes. The clustering algorithm in its default settings successfully segregated the reference BTV cases from the non-BTV cases, resulting in a sensitivity of 100.0% (95% CI: 89.0–100.0) and a specificity of 100.0% (95% CI: 80.0–100.0) for this set of data. The different linkage methods showed similar clustering results indicating stability of the classes (Rand indexes of respectively 0.77 for average and 0.75 for single linkage). The system proved to be robust when changing the parameters as the BTV cases remained together in meaningful clusters (Rand indexes between 0.72 and 1). The configurable MoSS-E2 system demonstrated its suitability to identify meaningful clusters of clinical syndromes.

Cell Based Associations: A procedure for considering scarce and mixed mineral occurrences in predictive mapping

Cell Based Association is an innovative mineral favorability procedure designed to answer special needs of the mining industry in data wise critical situations where usual favorability methods may not yield satisfactory results. Those situations relate to input data quality (e.g. clustered points, mixed and scarce data, approximate location) or some assumptions that are considered unreasonable (e.g. map areas relevance, conditional independence).The principle of CBA consists in replacing polygons of geological units with a square cell grid (hence the ‘cell-based’). Each cell contains a range of units (‘association’) that are binary coded in terms of their presence (1) or absence (0) within study area. The loss of resolution inherent to this procedure is compensated by the enriched information contained in each cell owing to the notion of (lithological) association.Lithological associations are considered as binary spectra and as such are classified using Ascendant Hierarchical Clustering (AHC) thus obtaining a synthetic map of lithological associations. The prospectivity map shows as favourable the cells of the same AHC classes that the ones including mineral occurrences.It was observed that CBA can distinguish between different ore deposit varieties from a blended mineral occurrences data set. CBA can theoretically include any spatialized data (e.g. geophysics, structural data) as an extra variable to specify classification and narrow favourable areas. Doing so would make it an independent favorability mapping procedure and is still under development.Cell size in a grid is a critical parameter of the procedure; it must be compatible with the looked-for phenomena and should have a sufficient lithological variability.In addition to its use for producing favorability maps, a CBA-derived map could help in understanding the background information contained in geological maps. CBA can also be applied to other fields, such as agriculture and urban planning.

Evaluation of Plant Diversity in the Steppes of White Wormwood of the Region of Saida (Western Algeria)

This work is an evaluation of the plant diversity of the white Wormwood formations (Artemisia herba-alba Asso.) in the steppes rangelands of the south-eastern zone of Sa?da (municipality of Maamora, Western Algeria). For this purpose, twenty-one phytoecological statements are carried out on three sample stations: an exclosure station where the white Wormwood is well developed and two other stations not protected where in one, the white Wormwood is moderately degraded and in the other one, it is strongly degraded. The factorial correspondences analysis (FCA) and the ascending hierarchical clustering (AHC) carried out on the floristic and edaphic parameters measured on these stations led to a hierarchy of the ecological factors determining the diversity of the studied plant formations with white Wormwood. The vegetation studied is marked by the dominance of the therophytes, particularly in the third station (very degraded station), while the exclosure station is essentially characterized by a high recovery rate and an important phytomass.

Elementary school students as authors of a description: stages in the learning of writing and linguistic-discursive styles / Alumnos de tercer y séptimo grado de nivel primario como autores de una descripción: etapas en el aprendizaje de la escritura y estilos lingüístico-discursivos

This paper explores the variety and integration of linguistic resources used by third and seventh graders in elementary schools with different socio-educational characteristics in Northern Patagonia (Argentina), when composing a description about a familiar topic. Texts were analysed according to two steps: a category analysis was performed from a lexicogrammatical and morpho-orthographic approach (unit of analysis: the word), and then a correspondence analysis and a following ascendant hierarchical cluster analysis were applied. Five clusters revealed three stages of learning to write a descriptive text — initial literacy, confirmation, writing to learn — and five linguistic-discursive styles — enumerative, intimate, protagonist, historical-contextual, external observer. These patterns were interpreted in terms of different modes of appropriation and the use of writing, related to the specific characteristics of the written task and the particular socio-educational traits of each grade/school. Students favoured different perspectives, types of information and linguistic formulations in their texts, in a continuum between particularistic, context-dependent principles, and universalistic standard-oriented principles, whose characteristics were closer to the prototypical descriptive text.

An original way to evaluate daily rainfall variability simulated by a regional climate model: the case of South African austral summer rainfall

We discuss the value of a clustering approach as a tool for evaluating daily rainfall output from climate models. Ascendant hierarchical clustering is used to evaluate how well South African recurrent daily rainfall patterns are simulated during the austral summer (December to February 1970–1971 to 1998–1999). A set of 35-km regional climate simulations, run with the WRF model and driven by the ERA40 reanalysis, is chosen as a case study. Six recurrent patterns are identified and compared to the observed clusters obtained by applying the same methodology to 5352 daily rain gauge records. Two of the WRF clusters describe either a persistent and widespread dryness (65% of the days) or patterns similar to the seasonal mean rainfall gradient (13% of the days). The four remaining WRF clusters (∼20% of the days) are wetter; they describe the weakening, conservation or strengthening of the average rainfall gradient. The WRF cluster rainfall patterns and their associated circulation match the observed clusters rather well, but their frequency of occurrence is greatly overestimated by WRF during dry events, and underestimated for near-normal rainfall conditions. The weak model biases found at the seasonal timescale conceal strongly biased intraseasonal rainfall variability. The WRF-simulated rainfall patterns are then temporally or spatially projected on to the observed clusters. Spatial projection proves to be the more useful of these two approaches in quantifying model skill by assessing both the temporal co-variability between WRF and observations, and the rainfall biases of the model with or without temporal dephasing. The WRF model simulates transient rainfall activity partially out of phase with observations, which induces large rainfall biases when temporal dephasing is not removed. Rainfall biases are significantly reduced, however, when temporal dephasing is removed. The clustering approach therefore proves its efficiency to highlight climate model strengths and deficiencies.

Targets for parathyroid hormone in secondary hyperparathyroidism: is a "one-size-fits-all" approach appropriate? A prospective incident cohort study.

BackgroundRecommendations for secondary hyperparathyroidism (SHPT) consider that a “one-size-fits-all” target enables efficacy of care. In routine clinical practice, SHPT continues to pose diagnosis and treatment challenges. One hypothesis that could explain these difficulties is that dialysis population with SHPT is not homogeneous.MethodsEPHEYL is a prospective, multicenter, pharmacoepidemiological study including chronic dialysis patients (≥3 months) with newly SHPT diagnosis, i.e. parathyroid hormone (PTH) ≥500 ng/L for the first time, or initiation of cinacalcet, or parathyroidectomy. Multiple correspondence analysis and ascendant hierarchical clustering on clinico-biological (symptoms, PTH, plasma phosphorus and alkaline phosphatase) and treatment of SHPT (cinacalcet, vitamin D, calcium, or calcium-free calcic phosphate binder) were performed to identify distinct phenotypes.Results305 patients (261 with incident PTH ≥ 500 ng/L; 44 with cinacalcet initiation) were included. Their mean age was 67 ± 15 years, and 60% were men, 92% on hemodialysis and 8% on peritoneal dialysis. Four subgroups of SHPT patients were identified: 1/ “intermediate” phenotype with hyperphosphatemia without hypocalcemia (n = 113); 2/ younger patients with severe comorbidities, hyperphosphatemia and hypocalcemia, despite SHPT multiple medical treatments, suggesting poor adherence (n = 73); 3/ elderly patients with few cardiovascular comorbidities, controlled phospho-calcium balance, higher PTH, and few treatments (n = 75); 4/ patients who initiated cinacalcet (n = 43). The quality criterion of the model had a cut-off of 14 (>2), suggesting a relevant classification.ConclusionIn real life, dialysis patients with newly diagnosed SHPT constitute a very heterogeneous population. A “one-size-fits-all” target approach is probably not appropriate. Therapeutic management needs to be adjusted to the 4 different phenotypes.

Cotyledonary somatic embryos of Pinus pinaster Ait. most closely resemble fresh, maturing cotyledonary zygotic embryos: biological, carbohydrate and proteomic analyses.

Cotyledonary somatic embryos (SEs) of maritime pine are routinely matured for 12 weeks before being germinated and converted to plantlets. Although regeneration success is highly dependent on SEs quality, the date of harvesting is currently determined mainly on the basis of morphological features. This empirical method does not provide any accurate information about embryo quality with respect to storage compounds (proteins, carbohydrates). We first analyzed SEs matured for 10, 12 and 14 weeks by carrying out biological (dry weight, water content) and biochemical measurements (total protein and carbohydrate contents). No difference could be found between collection dates, suggesting that harvesting SEs after 12 weeks is appropriate. Cotyledonary SEs were then compared to various stages, from fresh to fully desiccated, in the development of cotyledonary zygotic embryos (ZEs). We identified profiles that were similar using hierarchical ascendant cluster analysis (HCA). Fresh and dehydrated ZEs could be distinguished, and SEs clustered with fresh ZEs. Both types of embryo exhibited similar carbohydrate and protein contents and signatures. This high level of similarity (94.5 %) was further supported by proteome profiling. Highly expressed proteins included storage, stress-related, late embryogenesis abundant and energy metabolism proteins. By comparing overexpressed proteins in developing and cotyledonary SEs or ZEs, some (23 proteins) could be identified as candidate biomarkers for the late, cotyledonary stage. This is the first report of useful generic protein markers for monitoring embryo development in maritime pine. Our results also suggest that improvements of SEs quality may be achieved if the current maturation conditions are refined.