Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Bacterial flagellin elicits innate immune defenses and cardiac dysfunction in vivo

Résumé Les agents pathogènes responsables d'infection entraînent chez l'hôte deux types de réponses immunes, la première, non spécifique, dite immunité innée, la seconde, spécifique à l'agent concerné, dite immunité adaptative. L'immunité innée, qui représente la première ligne de défense contre les pathogènes, est liée à la reconnaissance par les cellules de l'hôte de structures moléculaires propres aux micro-organismes (« Pathogen-Associated Molecular Patterns », PAMPs), grâce à des récepteurs membranaires et cytoplasmiques (« Pattern Recognition Receptors », PRRs) identifiant de manière spécifique ces motifs moléculaires. Les récepteurs membranaires impliqués dans ce processus sont dénommés toll-like récepteurs, ou TLRS. Lorsqu'ils sont activés par leur ligand spécifique, ces récepteurs activent des voies de signalisation intracellulaires initiant la réponse inflammatoire non spécifique et visant à éradiquer l'agent pathogène. Les deux voies de signalisation impliquées dans ce processus sont la voie des « Mitogen-Activated Protein Kinases » (MAPKs) et celle du « Nuclear Factor kappaB » (NF-κB), dont l'activation entraîne in fine l'expression de protéines de l'inflammation dénommées cytokines, ainsi que certaines enzymes produisant divers autres médiateurs inflammatoires. Dans certaines situations, cette réponse immune peut être amplifiée de manière inadéquate, entraînant chez l'hôte une réaction inflammatoire systémique exagérée, appelée sepsis. Le sepsis peut se compliquer de dysfonctions d'organes multiples (sepsis sévère), et dans sa forme la plus grave, d'un collapsus cardiovasculaire, définissant le choc septique. La défaillance circulatoire du choc septique touche les vaisseaux sanguins d'une part, le coeur d'autre part, réalisant un tableau de «dysfonction cardiaque septique », dont on connaît mal les mécanismes pathogéniques. Les bactéries à Gram négatif peuvent déclencher de tels phénomènes, notamment en libérant de l'endotoxine, qui active les voies de l'immunité innée par son interaction avec un toll récepteur, le TLR4. Outre l'endotoxine, la plupart des bactéries à Gram négatif relâchent également dans leur environnement une protéine, la flagelline, qui est le constituant majeur du flagelle bactérien, organelle assurant la mobilité de ces micro-organismes. Des données récentes ont indiqué que la flagelline active, dans certaines cellules, les voies de l'immunité innée en se liant au récepteur TLRS. On ne connaît toutefois pas les conséquences de l'interaction flagelline-TLRS sur le développement de l'inflammation et des dysfonctions d'organes au cours du sepsis. Nous avons par conséquent élaboré le présent travail en formulant l'hypothèse que la flagelline pourrait déclencher une telle inflammation et représenter ainsi un médiateur potentiel de la dysfonction d'organes au cours du sepsis à Gram négatif, en nous intéressant plus particulièrement àl'inflammation et à la dysfonction cardiaque. Dans la première partie de ce travail, nous avons étudié les effets de la flagelline sur l'activation du NF-κB et des MAPKs, et sur l'expression de cytokines inflammatoires au niveau du myocarde in vitro (cardiomyocytes en culture) et in vivo (injection de flagelline recombinante à des souris). Nous avons observé tout d'abord que le récepteur TLRS est fortement exprimé au niveau du myocarde. Nous avons ensuite démontré que la flagelline active la voie du NF-κB et des MAP kinases (p38 et JNK), stimule la production de cytokines et de chemokines inflammatoires in vitro et in vivo, et entraîne l'activation de polynucléaires neutrophiles dans le tissu cardiaque in vivo. Finalement, au plan fonctionnel, nous avons pu montrer que la flagelline entraîne une dilatation et une réduction aiguë de la contractilité du ventricule gauche chez la souris, reproduisant les caractéristiques de la dysfonction cardiaque septique. Dans la deuxième partie, nous avons déterminé la distribution du récepteur TLRS dans les autres organes majeurs de la souris (poumon, foie, intestin et rein}, et avons caractérisé dans ces organes l'effet de la flagelline sur l'activation du NF-κB et des MAPKs, l'expression de cytokines, et l'induction de l'apoptose. Nous avons démontré que le TLRS est exprimé de façon constitutive dans ces organes, et que l'injection de flagelline y déclenche les cascades de l'immunité innée et de processus apoptotiques. Finalement, nous avons également déterminé que la flagelline entraîne une augmentation significative de multiples cytokines dans le plasma une à six heures après son injection. En résumé, nos données démontrent que la flagelline bactérienne (a) entraîne une inflammation et une dysfonction importantes du myocarde et (b) active de manière très significative les mécanismes d'immunité innée dans les principaux organes et entraîne une réponse inflammatoire systémique. Par conséquent, la flagelline peut représenter un médiateur puissant de l'inflammation et de la dysfonction d'organes, notamment du coeur, au cours du choc septique déclenché par les bactéries à Gram négatif. Summary Pathogenic microorganisms trigger two kinds of immune responses in the host. The first one is immediate and non-specific and is termed innate immunity, whereas the second one, specifically targeted at the invading agent, is termed adaptative immunity. Innate immunity, which represents the first line of defense against invading pathogens, confers the host the ability to recognize molecular structures common to many microbial pathogens, ("Pathogen-Associated Molecular Patterns", PAMPs), through cytosolic or membrane-associated receptors ("Pattern Recognition Receptors", PRRs), the latter being represented by a family of receptors termed "toll-like receptors or TLRs". Once activated by the binding of their specific ligand, these receptors activate intracellular signaling pathways, which initiate the non-specific inflammatory response aimed at eradicating the pathogens. The two pathways implicated in this process are the mitogen-activated protein kinases (MAPK) and the nuclear factor kappa B (NF-κB) signaling pathways, whose activation elicit in fine the expression of inflammatory proteins termed cytokines, as well as various enzymes producing a wealth of additional inflammatory mediators. In some circumstances, the innate immune response can become amplified and dysregulated, triggering an overwhelming systemic inflammatory response in the host, identified as sepsis. Sepsis can be associated with multiple organ dysfunction (severe sepsis), and in its most severe form, with cardiovascular collapse, defming septic shock. The cardiovascular failure associated with septic shock affects blood vessels as well as the heart, resulting in a particular form of acute heart failure termed "septic cardiac dysfunction ", whose pathogenic mechanisms remain partly undefined. Gram-negative bacteria can initiate such phenomena, notably by releasing lipopolysaccharide (LPS), which activates innate immune signaling by interacting with its specific toll receptor, the TLR4. Besides LPS, most Gram-negative bacteria also release flagellin into their environment, which is the main structural protein of the bacterial flagellum, an appendage extending from the outer bacterial membrane, responsible for the motility of the microorganism. Recent data indicated that flagellin activate immune responses upon binding to its receptor, TLRS, in various cell types. However, the role of flagellin/TLRS interaction in the development of inflammation and organ dysfunction during sepsis is not known. Therefore, we designed the present work to address the hypothesis that flagellin might trigger such inflammatory responses and thus represent a potential mediator of organ dysfunction during Gram-negative sepsis, with a particular emphasis on cardiac inflammation and contractile dysfunction. In the first part of this work, we investigated the effects of flagellin on NF-κB and MAPK activation and the generation of pro-inflammatory mediators within the heart in vitro (cultured cardiomyocytes) and in vivo (injection of recombinant flagellin into mice). We first observed that TLRS protein is strongly expressed by the myocardium. We then demonstrated that flagellin activates NF-κB and MAP kinases (p38 and JNK), upregulates the transcription of pro-inflammatory cytokines and chemokines in vitro and in vivo, and stimulates the activation of polymorphonuclear neutrophils within the heart in vivo. Finally, we demonstrated that flagellin triggers acute cardiac dilation, and a significant reduction of left ventricular contractility, mimicking characteristics of clinical septic cardiac dysfunction. In the second part, we determined the TLRS distribution in other mice major organs (lung, liver, gut and kidney) and we characterized in these organs the effects of flagellin on NF-κB and MAPK activation, on the expression of pro-inflammatory çytokines, and on the induction of apoptosis. We demonstrated that TLRS protein is constitutively expressed and that flagellin activates prototypical innate immune responses and pro-apoptotic pathways in all these organs. Finally, we also observed that flagellin induces a significant increase of multiple cytokines in the plasma from 1 to 6 hours after its intravenous administration. Altogether, these data provide evidence that bacterial flagellin (a) triggers an important inflammatory response and an acute dysfunction of the myocardium, and (b) significantly activates the mechanisms of innate immunity in most major organs and elicits a systemic inflammatory response. In consequence, flagellin may represent a potent mediator of inflammation and multiple organ failure, notably cardiac dysfunction, during Gram-negative septic shock.

Innate signaling promotes formation of regulatory nitric oxide-producing dendritic cells limiting T-cell expansion in experimental autoimmune myocarditis.

BACKGROUND: Activation of innate pattern-recognition receptors promotes CD4+ T-cell-mediated autoimmune myocarditis and subsequent inflammatory cardiomyopathy. Mechanisms that counterregulate exaggerated heart-specific autoimmunity are poorly understood. METHODS AND RESULTS: Experimental autoimmune myocarditis was induced in BALB/c mice by immunization with α-myosin heavy chain peptide and complete Freund's adjuvant. Together with interferon-γ, heat-killed Mycobacterium tuberculosis, an essential component of complete Freund's adjuvant, converted CD11b(hi)CD11c(-) monocytes into tumor necrosis factor-α- and nitric oxide synthase 2-producing dendritic cells (TipDCs). Heat-killed M. tuberculosis stimulated production of nitric oxide synthase 2 via Toll-like receptor 2-mediated nuclear factor-κB activation. TipDCs limited antigen-specific T-cell expansion through nitric oxide synthase 2-dependent nitric oxide production. Moreover, they promoted nitric oxide synthase 2 production in hematopoietic and stromal cells in a paracrine manner. Consequently, nitric oxide synthase 2 production by both radiosensitive hematopoietic and radioresistant stromal cells prevented exacerbation of autoimmune myocarditis in vivo. CONCLUSIONS: Innate Toll-like receptor 2 stimulation promotes formation of regulatory TipDCs, which confine autoreactive T-cell responses in experimental autoimmune myocarditis via nitric oxide. Therefore, activation of innate pattern-recognition receptors is critical not only for disease induction but also for counterregulatory mechanisms, protecting the heart from exaggerated autoimmunity.

Representing diffusion MRI in 5D for segmentation of white matter tracts with a level set method.

We present a method for segmenting white matter tracts from high angular resolution diffusion MR. images by representing the data in a 5 dimensional space of position and orientation. Whereas crossing fiber tracts cannot be separated in 3D position space, they clearly disentangle in 5D position-orientation space. The segmentation is done using a 5D level set method applied to hyper-surfaces evolving in 5D position-orientation space. In this paper we present a methodology for constructing the position-orientation space. We then show how to implement the standard level set method in such a non-Euclidean high dimensional space. The level set theory is basically defined for N-dimensions but there are several practical implementation details to consider, such as mean curvature. Finally, we will show results from a synthetic model and a few preliminary results on real data of a human brain acquired by high angular resolution diffusion MRI.

Low Mannan-binding lectin serum levels are associated with complicated Crohn's disease and reactivity to oligomannan (ASCA).

OBJECTIVES: Mannan-binding lectin (MBL) acts as a pattern-recognition molecule directed against oligomannan, which is part of the cell wall of yeasts and various bacteria. We have previously shown an association between MBL deficiency and anti-Saccharomyces cerevisiae mannan antibody (ASCA) positivity. This study aims at evaluating whether MBL deficiency is associated with distinct Crohn's disease (CD) phenotypes. METHODS: Serum concentrations of MBL and ASCA were measured using ELISA (enzyme-linked immunosorbent assay) in 427 patients with CD, 70 with ulcerative colitis, and 76 healthy controls. CD phenotypes were grouped according to the Montreal Classification as follows: non-stricturing, non-penetrating (B1, n=182), stricturing (B2, n=113), penetrating (B3, n=67), and perianal disease (p, n=65). MBL was classified as deficient (<100 ng/ml), low (100-500 ng/ml), and normal (500 ng/ml). RESULTS: Mean MBL was lower in B2 and B3 CD patients (1,503+/-1,358 ng/ml) compared with that in B1 phenotypes (1,909+/-1,392 ng/ml, P=0.013). B2 and B3 patients more frequently had low or deficient MBL and ASCA positivity compared with B1 patients (P=0.004 and P<0.001). Mean MBL was lower in ASCA-positive CD patients (1,562+/-1,319 ng/ml) compared with that in ASCA-negative CD patients (1,871+/-1,320 ng/ml, P=0.038). In multivariate logistic regression modeling, low or deficient MBL was associated significantly with B1 (negative association), complicated disease (B2+B3), and ASCA. MBL levels did not correlate with disease duration. CONCLUSIONS: Low or deficient MBL serum levels are significantly associated with complicated (stricturing and penetrating) CD phenotypes but are negatively associated with the non-stricturing, non-penetrating group. Furthermore, CD patients with low or deficient MBL are significantly more often ASCA positive, possibly reflecting delayed clearance of oligomannan-containing microorganisms by the innate immune system in the absence of MBL.

Cancer cell-autonomous contribution of type I interferon signaling to the efficacy of chemotherapy.

Some of the anti-neoplastic effects of anthracyclines in mice originate from the induction of innate and T cell-mediated anticancer immune responses. Here we demonstrate that anthracyclines stimulate the rapid production of type I interferons (IFNs) by malignant cells after activation of the endosomal pattern recognition receptor Toll-like receptor 3 (TLR3). By binding to IFN-α and IFN-β receptors (IFNARs) on neoplastic cells, type I IFNs trigger autocrine and paracrine circuitries that result in the release of chemokine (C-X-C motif) ligand 10 (CXCL10). Tumors lacking Tlr3 or Ifnar failed to respond to chemotherapy unless type I IFN or Cxcl10, respectively, was artificially supplied. Moreover, a type I IFN-related signature predicted clinical responses to anthracycline-based chemotherapy in several independent cohorts of patients with breast carcinoma characterized by poor prognosis. Our data suggest that anthracycline-mediated immune responses mimic those induced by viral pathogens. We surmise that such 'viral mimicry' constitutes a hallmark of successful chemotherapy.

New perspectives in the use of ink evidence in forensic science: Part II. Development and testing of mathematical algorithms for the automatic comparison of ink samples analysed by HPTLC

In the first part of this research, three stages were stated for a program to increase the information extracted from ink evidence and maximise its usefulness to the criminal and civil justice system. These stages are (a) develop a standard methodology for analysing ink samples by high-performance thin layer chromatography (HPTLC) in reproducible way, when ink samples are analysed at different time, locations and by different examiners; (b) compare automatically and objectively ink samples; and (c) define and evaluate theoretical framework for the use of ink evidence in forensic context. This report focuses on the second of the three stages. Using the calibration and acquisition process described in the previous report, mathematical algorithms are proposed to automatically and objectively compare ink samples. The performances of these algorithms are systematically studied for various chemical and forensic conditions using standard performance tests commonly used in biometrics studies. The results show that different algorithms are best suited for different tasks. Finally, this report demonstrates how modern analytical and computer technology can be used in the field of ink examination and how tools developed and successfully applied in other fields of forensic science can help maximising its impact within the field of questioned documents.

A multidimensional segmentation evaluation for medical image data.

Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.

Microtubule-associated protein-2 immunoreactivity: a useful tool in the differential diagnosis of low-grade neuroepithelial tumors.

Complex and variable morphological phenotypes pose a major challenge to the histopathological classification of neuroepithelial tumors. This applies in particular for low-grade gliomas and glio-neuronal tumors. Recently, we and others have identified microtubule-associated protein-2 (MAP2) as an immunohistochemical marker expressed in the majority of glial tumors. Characteristic cell morphologies can be recognized by MAP2 immunoreactivity in different glioma entities, i.e., process sparse oligodendroglial versus densely ramified astrocytic elements. Here, we describe MAP2-immunoreactivity patterns in a large series of various neuroepithelial tumors and related neoplasms (n = 960). Immunohistochemical analysis led to the following conclusions: (1) specific pattern of MAP2-positive tumor cells can be identified in 95% of glial neoplasms; (2) ependymal tumors do not express MAP2 in their rosette-forming cell component; (3) tumors of the pineal gland as well as malignant embryonic tumors are also characterized by abundant MAP2 immunoreactivity; (4) virtually no MAP2 expression can be observed in the neoplastic glial component of glio-neuronal tumors, i.e. gangliogliomas; (5) malignant glial tumor variants (WHO grade III or IV) exhibit different and less specific MAP2 staining patterns compared to their benign counterparts (WHO grade I or II); (6) with the exception of melanomas and small cell lung cancers, MAP2 expression is very rare in metastatic and non-neuroepithelial tumors; (7) glial MAP2 expression was not detected in 56 non-neoplastic lesions. These data point towards MAP2 as valuable diagnostic tool for pattern recognition and differential diagnosis of low-grade neuroepithelial tumors.

Probabilistic base calling of Solexa sequencing data.

BACKGROUND: Solexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags (up to 36 bases) by parallel sequencing-by-synthesis of DNA colonies. The processing and statistical analysis of such high-throughput data poses new challenges; currently a fair proportion of the tags are routinely discarded due to an inability to match them to a reference sequence, thereby reducing the effective throughput of the technology. RESULTS: We propose a novel base calling algorithm using model-based clustering and probability theory to identify ambiguous bases and code them with IUPAC symbols. We also select optimal sub-tags using a score based on information content to remove uncertain bases towards the ends of the reads. CONCLUSION: We show that the method improves genome coverage and number of usable tags as compared with Solexa's data processing pipeline by an average of 15%. An R package is provided which allows fast and accurate base calling of Solexa's fluorescence intensity files and the production of informative diagnostic plots.

Monitoring of posture allocations and activities by a shoe-based wearable sensor.

Monitoring of posture allocations and activities enables accurate estimation of energy expenditure and may aid in obesity prevention and treatment. At present, accurate devices rely on multiple sensors distributed on the body and thus may be too obtrusive for everyday use. This paper presents a novel wearable sensor, which is capable of very accurate recognition of common postures and activities. The patterns of heel acceleration and plantar pressure uniquely characterize postures and typical activities while requiring minimal preprocessing and no feature extraction. The shoe sensor was tested in nine adults performing sitting and standing postures and while walking, running, stair ascent/descent and cycling. Support vector machines (SVMs) were used for classification. A fourfold validation of a six-class subject-independent group model showed 95.2% average accuracy of posture/activity classification on full sensor set and over 98% on optimized sensor set. Using a combination of acceleration/pressure also enabled a pronounced reduction of the sampling frequency (25 to 1 Hz) without significant loss of accuracy (98% versus 93%). Subjects had shoe sizes (US) M9.5-11 and W7-9 and body mass index from 18.1 to 39.4 kg/m2 and thus suggesting that the device can be used by individuals with varying anthropometric characteristics.

Predicting clinical scores from magnetic resonance scans in Alzheimer's disease.

Machine learning and pattern recognition methods have been used to diagnose Alzheimer's disease (AD) and mild cognitive impairment (MCI) from individual MRI scans. Another application of such methods is to predict clinical scores from individual scans. Using relevance vector regression (RVR), we predicted individuals' performances on established tests from their MRI T1 weighted image in two independent data sets. From Mayo Clinic, 73 probable AD patients and 91 cognitively normal (CN) controls completed the Mini-Mental State Examination (MMSE), Dementia Rating Scale (DRS), and Auditory Verbal Learning Test (AVLT) within 3months of their scan. Baseline MRI's from the Alzheimer's disease Neuroimaging Initiative (ADNI) comprised the other data set; 113 AD, 351 MCI, and 122 CN subjects completed the MMSE and Alzheimer's Disease Assessment Scale-Cognitive subtest (ADAS-cog) and 39 AD, 92 MCI, and 32 CN ADNI subjects completed MMSE, ADAS-cog, and AVLT. Predicted and actual clinical scores were highly correlated for the MMSE, DRS, and ADAS-cog tests (P<0.0001). Training with one data set and testing with another demonstrated stability between data sets. DRS, MMSE, and ADAS-Cog correlated better than AVLT with whole brain grey matter changes associated with AD. This result underscores their utility for screening and tracking disease. RVR offers a novel way to measure interactions between structural changes and neuropsychological tests beyond that of univariate methods. In clinical practice, we envision using RVR to aid in diagnosis and predict clinical outcome.

Role of the superior parietal lobules in letter-identity processing within strings: FMRI evidence from skilled and dyslexic readers.

Traditionally, the ventral occipito-temporal (vOT) area, but not the superior parietal lobules (SPLs), is thought as belonging to the neural system of visual word recognition. However, some dyslexic children who exhibit a visual attention span disorder - i.e. poor multi-element parallel processing - further show reduced SPLs activation when engaged in visual multi-element categorization tasks. We investigated whether these parietal regions further contribute to letter-identity processing within strings. Adult skilled readers and dyslexic participants with a visual attention span disorder were administered a letter-string comparison task under fMRI. Dyslexic adults were less accurate than skilled readers to detect letter identity substitutions within strings. In skilled readers, letter identity differs related to enhanced activation of the left vOT. However, specific neural responses were further found in the superior and inferior parietal regions, including the SPLs bilaterally. Two brain regions that are specifically related to substituted letter detection, the left SPL and the left vOT, were less activated in dyslexic participants. These findings suggest that the left SPL, like the left vOT, may contribute to letter string processing.

Syk kinase signalling couples to the Nlrp3 inflammasome for anti-fungal host defence.

Fungal infections represent a serious threat, particularly in immunocompromised patients. Interleukin-1beta (IL-1beta) is a key pro-inflammatory factor in innate antifungal immunity. The mechanism by which the mammalian immune system regulates IL-1beta production after fungal recognition is unclear. Two signals are generally required for IL-1beta production: an NF-kappaB-dependent signal that induces the synthesis of pro-IL-1beta (p35), and a second signal that triggers proteolytic pro-IL-1beta processing to produce bioactive IL-1beta (p17) via Caspase-1-containing multiprotein complexes called inflammasomes. Here we demonstrate that the tyrosine kinase Syk, operating downstream of several immunoreceptor tyrosine-based activation motif (ITAM)-coupled fungal pattern recognition receptors, controls both pro-IL-1beta synthesis and inflammasome activation after cell stimulation with Candida albicans. Whereas Syk signalling for pro-IL-1beta synthesis selectively uses the Card9 pathway, inflammasome activation by the fungus involves reactive oxygen species production and potassium efflux. Genetic deletion or pharmalogical inhibition of Syk selectively abrogated inflammasome activation by C. albicans but not by inflammasome activators such as Salmonella typhimurium or the bacterial toxin nigericin. Nlrp3 (also known as NALP3) was identified as the critical NOD-like receptor family member that transduces the fungal recognition signal to the inflammasome adaptor Asc (Pycard) for Caspase-1 (Casp1) activation and pro-IL-1beta processing. Consistent with an essential role for Nlrp3 inflammasomes in antifungal immunity, we show that Nlrp3-deficient mice are hypersusceptible to Candida albicans infection. Thus, our results demonstrate the molecular basis for IL-1beta production after fungal infection and identify a crucial function for the Nlrp3 inflammasome in mammalian host defence in vivo.

Intégration de méthodes de data mining dans le renseignement criminel : analyse par des structures issues de la théorie des graphes dans le profilage des stupéfiants

Data mining can be defined as the extraction of previously unknown and potentially useful information from large datasets. The main principle is to devise computer programs that run through databases and automatically seek deterministic patterns. It is applied in different fields of application, e.g., remote sensing, biometry, speech recognition, but has seldom been applied to forensic case data. The intrinsic difficulty related to the use of such data lies in its heterogeneity, which comes from the many different sources of information. The aim of this study is to highlight potential uses of pattern recognition that would provide relevant results from a criminal intelligence point of view. The role of data mining within a global crime analysis methodology is to detect all types of structures in a dataset. Once filtered and interpreted, those structures can point to previously unseen criminal activities. The interpretation of patterns for intelligence purposes is the final stage of the process. It allows the researcher to validate the whole methodology and to refine each step if necessary. An application to cutting agents found in illicit drug seizures was performed. A combinatorial approach was done, using the presence and the absence of products. Methods coming from the graph theory field were used to extract patterns in data constituted by links between products and place and date of seizure. A data mining process completed using graphing techniques is called ``graph mining''. Patterns were detected that had to be interpreted and compared with preliminary knowledge to establish their relevancy. The illicit drug profiling process is actually an intelligence process that uses preliminary illicit drug classes to classify new samples. Methods proposed in this study could be used \textit{a priori} to compare structures from preliminary and post-detection patterns. This new knowledge of a repeated structure may provide valuable complementary information to profiling and become a source of intelligence.