Laboratoire Angevin de Recherche en Mathématiques

Inter-observer agreement on activity and fibrosis scores used in chronic viral hepatitis has only been studied under selected conditions. The aim of this study was to identify the sources of variability due to specimen characteristics and observers. This study included 254 liver specimens and 15 pathologists and used the Metavir score. In 44 specimens scored by 4 academic pathologists, agreement of Metavir score was good overall, but better for fibrosis (kappa = 0.59) than for activity (kappa = 0.43) and poor for lobular necrosis (kappa = 0.15). The mean agreement was better for senior (0.60 +/- 0.24) than junior pathologists (0.52 +/- 0.30, P < .05). Mean intrabserver agreement was better than inter-observer agreement (0.77 +/- 0.18 vs. 0.58 +/- 0.26, P < .01). In 157 specimens scored by 2 expert pathologists (one senior, one junior), agreement of Metavir score was only good but greatly improved after consensus reading (fibrosis: kappa = 0.48 and 0.77, activity: kappa = 0.44 and 0.70, respectively, before and after consensus). Several causes of disagreement were identified: specimen length, fibrosis class number, observer bias, and putative causes related to Metavir score or specimen. In an intercenter evaluation involving 59 specimens, 1 expert and 10 nonacademic pathologists, agreement was very poor and did not improve over 5 years for activity (kappa = 0.22-0.25) or fibrosis (kappa = 0.13-0.18). In conclusion, the level of experience (specialization, duration, and location of practice) has more influence on agreement than the characteristics of the specimen (length, fibrosis class number, miscellaneous factors). Agreement can be improved by experienced pathologist or consensus reading.

Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract—from raw images—meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropy-based approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

Organic photovoltaic solar cells bere an important potential of development in the search for low-cost modules for the production of domestic electricity. We review the principles and techniques needed for their development: organic semiconductors, their transport properties and photophysical characteristics, photovoltaic molecule and polymer structures, device technologies, electrical and optical behaviour of the cells, state of the art, limitations and perspectives. Despite some recent record efficiencies, research on organic solar cells is still in its infancy when stability and efficiency have to be compared with the performances of silicon cells. A nominal 10% solar efficiency is the research target for the next few years.

Multiscale entropy (MSE) analysis was introduced in the 2002 to evaluate the complexity of a time series by quantifying its entropy over a range of temporal scales. The algorithm has been successfully applied in different research fields. Since its introduction, a number of modifications and refinements have been proposed, some aimed at increasing the accuracy of the entropy estimates, others at exploring alternative coarse-graining procedures. In this review, we first describe the original MSE algorithm. Then, we review algorithms that have been introduced to improve the estimation of MSE. We also report a recent generalization of the method to higher moments.

OBJECTIVE: The DEPDC5 (DEP domain-containing protein 5) gene, encoding a repressor of the mTORC1 signaling pathway, has recently emerged as a major gene mutated in familial focal epilepsies. We aimed to further extend the role of DEPDC5 to focal cortical dysplasias (FCDs). METHODS: Seven patients from 4 families with DEPDC5 mutations and focal epilepsy associated with FCD were recruited and investigated at the clinical, neuroimaging, and histopathological levels. The DEPDC5 gene was sequenced from genomic blood and brain DNA. RESULTS: All patients had drug-resistant focal epilepsy, 5 of them underwent surgery, and 1 had a brain biopsy. Electroclinical phenotypes were compatible with FCD II, although magnetic resonance imaging (MRI) was typical in only 4 cases. Histopathology confirmed FCD IIa in 2 patients (including 1 MRI-negative case) and showed FCD I in 2 other patients, and remained inconclusive in the last 2 patients. Three patients were seizure-free postsurgically, and 1 had a worthwhile improvement. Sequencing of blood DNA revealed truncating DEPDC5 mutations in all 4 families; 1 mutation was found to be mosaic in an asymptomatic father. A brain somatic DEPDC5 mutation was identified in 1 patient in addition to the germline mutation. INTERPRETATION: Germline, germline mosaic, and brain somatic DEPDC5 mutations may cause epilepsy associated with FCD, reinforcing the link between mTORC1 pathway and FCDs. Similarly to other mTORopathies, a "2-hit" mutational model could be responsible for cortical lesions. Our study also indicates that epilepsy surgery is a valuable alternative in the treatment of drug-resistant DEPDC5-positive focal epilepsies, even if the MRI is unremarkable.

As a synergistic integration between spectroscopy and imaging technologies, spectral imaging modalities have been emerged to tackle quality evaluation dilemmas by proposing different designs with effective and practical applications in food and agriculture. With the advantage of acquiring spatio-spectral data across a wide range of the electromagnetic spectrum, the state-of-the-art multispectral imaging in tandem with different multivariate chemometric analysis scenarios has been successfully implemented not only for food quality and safety control purposes, but also in dealing with critical research challenges in seed science and technology. This paper will shed some light on the fundamental configuration of the systems and give a birds-eye view of all recent approaches in the acquisition, processing and reproduction of multispectral images for various applications in seed quality assessment and seed phenotyping issues. This review article continues from where earlier review papers stopped but it only focused on fully-operated multispectral imaging systems for quality assessment of different sorts of seeds. Thence, the review comprehensively highlights research attempts devoted to real implementations of only fully-operated multispectral imaging systems and does not consider those ones that just utilized some key wavelengths extracted from hyperspectral data analyses without building independent multispectral imaging systems. This makes this article the first attempt in briefing all published papers in multispectral imaging applications in seed phenotyping and quality monitoring by providing some examples and research results in characterizing physicochemical quality traits, predicting physiological parameters, detection of defect, pest infestation and seed health.

We report a prospective quantitative image analysis study of C cells in 57 normal autopsy thyroid glands, serially sectioned and wholly embedded in paraffin; all slides were immunohistochemically stained for calcitonin. Computerized quantitative image analysis was performed on 47 cases to measure C cell surface area and parenchymatous surface area after immunoperoxidase staining for calcitonin. The method was time-effective, with a good reproducibility. C cells were mainly found in the middle third of each lobe. Important inter-individual variations were observed; the maximum C cell surface area in a section (Amax) ranged from 28 x 10(3) to 470 x 10(3) microns2 (mean, 167 x 10(3) microns2) among 42 adults. Of particular interest was the important difference observed between sexes; Amax was twice as high in men (mean, 201 x 10(3) microns2) as in women (mean, 91 x 10(3) microns2; P = 0.0009). Moreover, 14 (33%) adult subjects [2 women (15%) and 12 men (41%)] fulfilled C cell hyperplasia criteria, i.e. at least 3 fields at x 100 magnification containing more than 50 C cells, suggesting that a substantial part of the normal adult population could have C cell hyperplasia.

Morphological abnormalities of foraminiferal tests have been studied in cultures of Ammonia tepida and A. beccarii. Regular asexual reproduction cycles of A. tepida show the influence of high salinity on the morphology of juveniles and on the ontogenetic process. A low rate of abnormalities (about 1%) occurs under normal saline conditions (37‰). These are mainly ‘‘double tests’ ’ or a protuberance on the spiral side, or an abnormal arrange-ment of the first several chambers. A high rate of abnor-malities among the juvenile forms (about 50%) occurs under hypersaline conditions (50‰). These abnormalities include an abnormal shape of the proloculus or of the first several chambers, a modification of the coiling plane of the first several chambers, the development of two whorls from one proloculus, or the fusion of two juveniles. Some adults exposed to hypersaline conditions before the end of their growth have also shown a strong response that is manifested either in abnormal-sized chambers, or in numerous chambers with a complex arrangement. In paralic and coastal environments characterized either by salinity fluctuations or by hypersalinity, morphological abnormalities are similar to those seen in cultures, and can be explain by the same processes. Other phenomena can cause abnormalities in the test. Asexual reproduction can lead to the construction of ab-normal chambers on the umbilical side. Decalcification can result in the formation of small additional chambers or new chambers located between two septal walls of the decalcified initial chamber. Regeneration may occur af-ter test damage; this feature is generally recognizable by the scars present on the test. To have utility for various environmental applications we recommend using the term ‘‘deformations’ ’ to de-scribe abnormalities formed by modification of the test during the life of the foraminifer, and the term ‘‘mal-formations’ ’ only to describe abnormalities resulting from an anomaly in the ontogenetic development pro-cess. When the origin of the abnormality is not evident, the words ‘‘morphological abnormalities’ ’ or ‘‘abnormal tests’ ’ can be used.

Alzheimer's disease (AD) causes impairments affecting instrumental activities of daily living (IADL). Transdisciplinary research in neuropsychology and virtual reality has fostered the development of ecologically valid virtual tools for the assessment of IADL, using simulations of real life activities. Few studies have examined the benefits of this approach in AD patients. Our aim was to examine the utility of a non-immersive virtual coffee task (NI-VCT) for assessment of IADL in these patients. We focus on the assessment results obtained from a group of 24 AD patients on a task designed to assess their ability to prepare a virtual cup of coffee, using a virtual coffee machine. We compared performance on the virtual task to an identical daily living task involving the actual preparation of a cup of coffee, as well as to global cognitive, executive, and caregiver-reported IADL functioning. Relative to 32 comparable, healthy elderly (HE) controls, AD patients performed worse than HE controls on all tasks. Correlation analyses revealed that NI-VCT measures were related to all other neuropsychological measures. Moreover, regression analyses demonstrated that performance on the NI-VCT predicted actual task performance and caregiver-reported IADL functioning. Our results provide initial support for the utility of our virtual kitchen for assessment of IADL in AD patients.

L'objectif de cet article est de mettre en parallèle les connaissances acquises dans le milieu académique sur l'influence des différents facteurs d'ambiance sur les réactions des clients en magasin avec les pratiques des distributeurs, afin de présenter un marketing spécifique et émergent: le marketing sensoriel du point de vente.

Light and current generation functions from a rubrene/fullerene heterostructure are integrated into an efficient organic dual device (see figure). Electroluminescence, with the characteristic rubrene color, has a turn-on voltage less than 1 V. Solar-power conversion efficiency reaches 3 % with a 5.3 mA cm–2 short-circuit current density and almost 1 V open- circuit voltage under AM 1.5 illumination. The half-bandgap electroluminescence turn-on voltage is interpreted as an Auger fountain effect. Supporting information for this article is available on the WWW under http://www.wiley-vch.de/contents/jc_2089/2007/c1052_s.pdf or from the author. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Laser speckle is a complex interference phenomenon that can easily be understood, in concept, but is difficult to predict mathematically, because it is a stochastic process. The use of laser speckle to produce images, which can carry many types of information, is called laser speckle imaging (LSI). The biomedical applications of LSI started in 1981 and, since then, many scientists have improved the laser speckle theory and developed different imaging techniques. During this process, some inconsistencies have been propagated up to now. These inconsistencies should be clarified in order to avoid errors in future works. This review presents a review of the laser speckle theory used in biomedical applications. Moreover, we also make a review of the practical concepts that are useful in the construction of laser speckle imagers. This study is not only an exposition of the concepts that can be found in the literature but also a critical analysis of the investigations presented so far. Concepts like scatterers velocity distribution, effect of static scatterers, optimal speckle size, light penetration angle, and contrast computation algorithms are discussed in detail.

We demonstrate experimentally a double-clad Er:Yb co-doped dual amplifier passive mode-locked figure-of-eight fiber laser that generates high energy, width, and amplitude tunable dissipative soliton resonance square pulses. In our laser system, each loop contains an amplifier that controls a characteristic of the output pulse. The amplitude and width of the output beam can be controlled continuously but, dependently, according to the pump power of each amplifier. The pulse width can be tuned in a range of almost 360 ns while the peak power varies from 8 to 120 W. On maximum possible pumping from both sides without having a pulse break, we report square pulses with 10 μJ energy per pulse with a signal-to-noise ratio of 60 dB.

BACKGROUND: Monitoring the timing of seedling emergence and early development via high-throughput phenotyping with computer vision is a challenging topic of high interest in plant science. While most studies focus on the measurements of leaf area index or detection of specific events such as emergence, little attention has been put on the identification of kinetics of events of early seedling development on a seed to seed basis. RESULT: Imaging systems screened the whole seedling growth process from the top view. Precise annotation of emergence out of the soil, cotyledon opening, and appearance of first leaf was conducted. This annotated data set served to train deep neural networks. Various strategies to incorporate in neural networks, the prior knowledge of the order of the developmental stages were investigated. Best results were obtained with a deep neural network followed with a long short term memory cell, which achieves more than 90% accuracy of correct detection. CONCLUSION: This work provides a full pipeline of image processing and machine learning to classify three stages of plant growth plus soil on the different accessions of two species of red clover and alfalfa but which could easily be extended to other crops and other stages of development.

Multiscale permutation entropy (MPE) has recently been proposed to evaluate complexity of time series. MPE has numerous advantages over other multiscale complexity measures, such as its simplicity, robustness to noise and its low computational cost. However, MPE may loose statistical reliability as the scale factor increases, because the coarse-graining procedure used in the MPE algorithm reduces the length of the time series as the scale factor grows. To overcome this drawback, we introduce the refined composite MPE (RCMPE). Through applications on both synthetic and real data, we show that RCMPE is much less dependent on the signal length than MPE. In this sense, RCMPE is more reliable than MPE. RCMPE could therefore replace MPE for short times series or at large scale factors.

During bone loss, osteoblast population can be replaced by adipose tissue. This apparent reciprocal relationship between decreased bone density and increased fat formation can be explained by an imbalance in the production of bone-forming and fat-forming cells in the marrow cavity. Thus, osteoblast and adipocyte pathways seem more closely and inversely related. In the present study, we investigated the effects of dexamethasone (dex) and calcitriol [1,25(OH)(2)D(3)] on proliferation and differentiation of osteoblasts and adipocytes in rat bone marrow stromal cell cultures. Stromal cells were grown in primoculture in presence of dex and subcultivated in presence of dex and/or 1,25(OH)(2)D(3). Total cell proliferation, osteoblast and adipocyte-cells number, and -mRNA specific markers were used to study the effects of hormonal treatment on stromal cells. Total cell proliferation was stimulated by dex and inhibited by 1,25(OH)(2)D(3). Dex increased osteoblast and adipocyte cell population whereas calcitriol decreased bone-forming cell number and increased fat cell population. The presence of both hormones led to a strong decrease in osteoblastic cells and to a strong increase in adipocytic cell number. Dex induced mRNA osteoblastic markers expression like bone sialoprotein (BSP) and osteocalcin (OC) and an adipocyte marker expression, the fatty acid binding protein aP2. Calcitriol decreased the dex-induced BSP expression but stimulated slightly OC and aP2 mRNA. The effects of both hormones was to increase strongly OC and aP2 mRNA. These results support that, in rat bone marrow, adipocyte proliferation and differentiation are stimulated by glucocorticoids and calcitriol which act synergically, whereas osteoblastic cell proliferation and differentiation are increased by dex and inhibited by 1,25(OH)(2)D(3).

Cross-entropy was introduced in 1996 to quantify the degree of asynchronism between two time series. In 2009, a multiscale cross-entropy measure was proposed to analyze the dynamical characteristics of the coupling behavior between two sequences on multiple scales. Since their introductions, many improvements and other methods have been developed. In this review we offer a state-of-the-art on cross-entropy measures and their multiscale approaches.

Abstract In recent years, the application of metaheuristic techniques to solve multi‐objective optimization problems has become an active research area. Solving this kind of problems involves obtaining a set of P areto‐optimal solutions in such a way that the corresponding P areto front fulfils the requirements of convergence to the true P areto front and uniform diversity. Most of the studies on metaheuristics for multi‐objective optimization are focused on E volutionary A lgorithms, and some of the state‐of‐the‐art techniques belong this class of algorithms. Our goal in this paper is to study open research lines related to metaheuristics but focusing on less explored areas to provide new perspectives to those researchers interested in multi‐objective optimization. In particular, we focus on non‐evolutionary metaheuristics, hybrid multi‐objective metaheuristics, parallel multi‐objective optimization and multi‐objective optimization under uncertainty. We analyze these issues and discuss open research lines.

Aquaponics is an innovative, smart, and sustainable agricultural technology that integrates aquaculture (farming of fish) with hydroponics in growing vegetable crops symbiotically. The correct implementation of aquaponics helps in providing healthy organic foods with low consumption of water and chemical fertilizers. Numerous research attempts have been directed toward real implementations of this technology feasibly and reliably at large commercial scales and adopting it as a new precision technology. For better management of such technology, there is an urgent need to use the Internet of things (IoT) and smart sensing systems for monitoring and controlling all operations involved in the aquaponic systems. Thence, the objective of this article is to comprehensively highlight research endeavors devoted to the utilization of automated, fully operated aquaponic systems, by discussing all related aquaponic parameters aligned with smart automation scenarios and IoT supported by some examples and research results. Furthermore, an attempt to find potential gaps in the literature and future contributions related to automated aquaponics was highlighted. In the scope of the reviewed research works in this article, it is expected that the aquaponics system supported with smart control units will become more profitable, intelligent, accurate, and effective.

This paper will introduce the use of the approximate Bayesian computation (ABC) algorithm for model selection and parameter estimation in structural dynamics. ABC is a likelihood-free method typically used when the likelihood function is either intractable or cannot be approached in a closed form. To circumvent the evaluation of the likelihood function, simulation from a forward model is at the core of the ABC algorithm. The algorithm offers the possibility to use different metrics and summary statistics representative of the data to carry out Bayesian inference. The efficacy of the algorithm in structural dynamics is demonstrated through three different illustrative examples of nonlinear system identification: cubic and cubic-quintic models, the Bouc-Wen model and the Duffing oscillator. The obtained results suggest that ABC is a promising alternative to deal with model selection and parameter estimation issues, specifically for systems with complex behaviours.&#13;\n&#13;\n