253 research outputs found
Use of Experimental Design for Peuhl Cheese Process Optimization
This work points out the use of experimental design for peulh cheese making process optimisation. Peulh cheese, a milk coagulum, well-known in certain West African countries (e.g. Benin), is unfortunately ill-known in Côte d’Ivoire (Ivory Coast). However, it could be a transformation way of milk. This work consisting in use of a central composite design enables the determination of optimal process conditions concerning: leaf extract volume added (7 mL), heating temperature (84.12°C) and heating time (15 min). When these optimal conditions are reached, the maximal value for process yield is 58.88 %, the minimal value for TCB is 6.40 min and the value found for totalsolids is 42.75 %. Furthermore, this work has showed that the experimental design is more suitable than the traditional study method called “one factor at a time”. @JASE
Création automatique de classes de signatures manuscrites pour l'authentification en ligne
International audienceNous nous intéressons dans ce papier à l'optimisation d'un système d'authentification par signature manuscrite. Celui-ci est basé sur une approche Coarse To Fine et utilise l'algorithme Dynamic Time Warping ainsi qu'un seuil de décision global pour accepter ou rejeter un signataire. L'optimisation proposée réside dans l'utilisation d'un algorithme de classification non supervisée afin de déterminer automatiquement des classes de signatures. Pour chacune des classes, un seuil de décision spécifique est établi. Dans ces travaux, nous nous sommes plus particulièrement attaché à étudier l'impact de la classification sur les performance. Les résultats expérimentaux sur la base SVC montrent que l'on peut améliorer les performances en diminuant le taux d'erreur égale de 14,4%. Cependant la sensibilité de la classification est très grande et la notion de classe unique pour un signataire semble trop restrictive
Adaptive estimation in circular functional linear models
We consider the problem of estimating the slope parameter in circular
functional linear regression, where scalar responses Y1,...,Yn are modeled in
dependence of 1-periodic, second order stationary random functions X1,...,Xn.
We consider an orthogonal series estimator of the slope function, by replacing
the first m theoretical coefficients of its development in the trigonometric
basis by adequate estimators. Wepropose a model selection procedure for m in a
set of admissible values, by defining a contrast function minimized by our
estimator and a theoretical penalty function; this first step assumes the
degree of ill posedness to be known. Then we generalize the procedure to a
random set of admissible m's and a random penalty function. The resulting
estimator is completely data driven and reaches automatically what is known to
be the optimal minimax rate of convergence, in term of a general weighted
L2-risk. This means that we provide adaptive estimators of both the slope
function and its derivatives
Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes
Functional data are defined as realizations of random functions (mostly
smooth functions) varying over a continuum, which are usually collected with
measurement errors on discretized grids. In order to accurately smooth noisy
functional observations and deal with the issue of high-dimensional observation
grids, we propose a novel Bayesian method based on the Bayesian hierarchical
model with a Gaussian-Wishart process prior and basis function representations.
We first derive an induced model for the basis-function coefficients of the
functional data, and then use this model to conduct posterior inference through
Markov chain Monte Carlo. Compared to the standard Bayesian inference that
suffers serious computational burden and unstableness for analyzing
high-dimensional functional data, our method greatly improves the computational
scalability and stability, while inheriting the advantage of simultaneously
smoothing raw observations and estimating the mean-covariance functions in a
nonparametric way. In addition, our method can naturally handle functional data
observed on random or uncommon grids. Simulation and real studies demonstrate
that our method produces similar results as the standard Bayesian inference
with low-dimensional common grids, while efficiently smoothing and estimating
functional data with random and high-dimensional observation grids where the
standard Bayesian inference fails. In conclusion, our method can efficiently
smooth and estimate high-dimensional functional data, providing one way to
resolve the curse of dimensionality for Bayesian functional data analysis with
Gaussian-Wishart processes.Comment: Under revie
Theoretical Properties of Projection Based Multilayer Perceptrons with Functional Inputs
Many real world data are sampled functions. As shown by Functional Data
Analysis (FDA) methods, spectra, time series, images, gesture recognition data,
etc. can be processed more efficiently if their functional nature is taken into
account during the data analysis process. This is done by extending standard
data analysis methods so that they can apply to functional inputs. A general
way to achieve this goal is to compute projections of the functional data onto
a finite dimensional sub-space of the functional space. The coordinates of the
data on a basis of this sub-space provide standard vector representations of
the functions. The obtained vectors can be processed by any standard method. In
our previous work, this general approach has been used to define projection
based Multilayer Perceptrons (MLPs) with functional inputs. We study in this
paper important theoretical properties of the proposed model. We show in
particular that MLPs with functional inputs are universal approximators: they
can approximate to arbitrary accuracy any continuous mapping from a compact
sub-space of a functional space to R. Moreover, we provide a consistency result
that shows that any mapping from a functional space to R can be learned thanks
to examples by a projection based MLP: the generalization mean square error of
the MLP decreases to the smallest possible mean square error on the data when
the number of examples goes to infinity
p.Ala541Thr variant of MEN1 gene: A non deleterious polymorphism or a pathogenic mutation?
Context
Multiple Endocrine Neoplasia Type 1 (MEN1) is an autosomal dominant inherited syndrome, related to mutations in the MEN1 gene. Controversial data suggest that the nonsynonymous p.Ala541Thr variant, usually considered as a non-pathogenic polymorphism, may be associated with an increased risk of MEN1-related lesions in carriers.
Objective
The aim of this study was to evaluate the pathogenic influence of the p.Ala541Thr variant on clinical and functional outcomes.
Patients and methods
We analysed a series of 55 index patients carrying the p.Ala541Thr variant. Their clinical profile was compared to that of 117 MEN1 patients. The biological impact of the p.Ala541Thr variant on cell growth was additionally investigated on menin-deficient Leydig cell tumour (LCT)10 cells generated from Men1+/Men1− heterozygous knock-out mice, and compared with wild type (WT).
Results
The mean age at first appearance of endocrine lesions was similar in both p.Ala541Thr carriers and MEN1 patients, but no p.Ala541Thr patient had more than one cardinal MEN1 lesion at initial diagnosis. A second MEN1 lesion was diagnosed in 13% of MEN1 patients and in 7% of p.Ala541Thr carriers in the year following preliminary diagnosis. Functional studies on LCT10 cells showed that overexpression of the p.Ala541Thr variant did not inhibit cell growth, which is in direct contrast to results obtained from investigation of WT menin protein.
Conclusion
Taken together, these data raise the question of a potential pathogenicity of the p.Ala541Thr missense variant of menin that commonly occurs within the general population. Additional studies are required to investigate whether it may be involved in a low-penetrance MEN1 phenotype
Lessons from prospective longitudinal follow-up of a French APECED cohort
Background
Autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy syndrome is a rare disease caused by biallelic mutations of the AIRE gene, usually presenting with the triad hypoparathyroidism-adrenal failure-chronic mucocutaneous candidiasis (CMC) and nonendocrine manifestations. The aim of this study was to determine the molecular profile of the AIRE gene, the prevalence of rare manifestations, and to characterize immunological disturbances in a French cohort.
Patients and Methods
A national, multicenter prospective observational study to collect genetic, clinical, biological, and immunological data (NCT03751683).
Results
Twenty-five patients (23 families) were enrolled. Eleven distinct AIRE variants were identified, 2 of which were not previously reported: an intronic variant, c.653-70G > A, and a c.1066del (p.Arg356GlyfsX22) variant (exon 9). The most common was the Finnish variant c.769C > T (16 alleles), followed by the variant c.967_979del13 (15 alleles), which seemed associated with a less severe phenotype. Seventeen out of 25 patients were homozygote. The median number of clinical manifestations was 7; 19/25 patients presented with the hypoparathyroidism-adrenal failure-CMC triad, 8/13 showed pulmonary involvement, 20/25 had ectodermal dystrophy, 8/25 had malabsorption, and 6/23 had asplenia. Fifteen out of 19 patients had natural killer cell lymphopenia with an increase in CD4+ and CD8+ T lymphocytes and an age-dependent alteration of B lymphocyte homeostasis compared with matched controls (P < .001), related to the severity of the disease. All tested sera (n = 18) were positive for anti-interferon-α, 15/18 for anti-IL-22 antibodies, and 13/18 for anti-IL-17F antibodies, without clear phenotypic correlation other than with CMC.
Conclusion
This first prospective cohort showed a high AIRE genotype variability, with 2 new gene variants. The prevalence of potentially life-threatening nonendocrine manifestations was higher with systematic screening. These manifestations could, along with age-dependent B-cell lymphopenia, contribute to disease severity. Systematic screening for all the manifestations of the syndrome would allow earlier diagnosis, supporting vaccination and targeted therapeutic approaches
The Extracytoplasmic Domain of the Mycobacterium tuberculosis Ser/Thr Kinase PknB Binds Specific Muropeptides and Is Required for PknB Localization
The Mycobacterium tuberculosis Ser/Thr kinase PknB has been implicated in the regulation of cell growth and morphology in this organism. The extracytoplasmic domain of this membrane protein comprises four penicillin binding protein and Ser/Thr kinase associated (PASTA) domains, which are predicted to bind stem peptides of peptidoglycan. Using a comprehensive library of synthetic muropeptides, we demonstrate that the extracytoplasmic domain of PknB binds muropeptides in a manner dependent on the presence of specific amino acids at the second and third positions of the stem peptide, and on the presence of the sugar moiety N-acetylmuramic acid linked to the peptide. We further show that PknB localizes strongly to the mid-cell and also to the cell poles, and that the extracytoplasmic domain is required for PknB localization. In contrast to strong growth stimulation by conditioned medium, we observe no growth stimulation of M. tuberculosis by a synthetic muropeptide with high affinity for the PknB PASTAs. We do find a moderate effect of a high affinity peptide on resuscitation of dormant cells. While the PASTA domains of PknB may play a role in stimulating growth by binding exogenous peptidoglycan fragments, our data indicate that a major function of these domains is for proper PknB localization, likely through binding of peptidoglycan fragments produced locally at the mid-cell and the cell poles. These data suggest a model in which PknB is targeted to the sites of peptidoglycan turnover to regulate cell growth and cell division
Prediction of water intake and excretion flows in Holstein dairy cows under thermoneutral conditions
Apnea of prematurity: from cause to treatment
Apnea of prematurity (AOP) is a common problem affecting premature infants, likely secondary to a “physiologic” immaturity of respiratory control that may be exacerbated by neonatal disease. These include altered ventilatory responses to hypoxia, hypercapnia, and altered sleep states, while the roles of gastroesophageal reflux and anemia remain controversial. Standard clinical management of the obstructive subtype of AOP includes prone positioning and continuous positive or nasal intermittent positive pressure ventilation to prevent pharyngeal collapse and alveolar atelectasis, while methylxanthine therapy is a mainstay of treatment of central apnea by stimulating the central nervous system and respiratory muscle function. Other therapies, including kangaroo care, red blood cell transfusions, and CO2 inhalation, require further study. The physiology and pathophysiology behind AOP are discussed, including the laryngeal chemoreflex and sensitivity to inhibitory neurotransmitters, as are the mechanisms by which different therapies may work and the potential long-term neurodevelopmental consequences of AOP and its treatment
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