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.

Toll-like receptor agonists synergize with CD40L to induce either proliferation or plasma cell differentiation of mouse B cells.

In a classical dogma, pathogens are sensed (via recognition of Pathogen Associated Molecular Patterns (PAMPs)) by innate immune cells that in turn activate adaptive immune cells. However, recent data showed that TLRs (Toll Like Receptors), the most characterized class of Pattern Recognition Receptors, are also expressed by adaptive immune B cells. B cells play an important role in protective immunity essentially by differentiating into antibody-secreting cells (ASC). This differentiation requires at least two signals: the recognition of an antigen by the B cell specific receptor (BCR) and a T cell co-stimulatory signal provided mainly by CD154/CD40L acting on CD40. In order to better understand interactions of innate and adaptive B cell stimulatory signals, we evaluated the outcome of combinations of TLRs, BCR and/or CD40 stimulation. For this purpose, mouse spleen B cells were activated with synthetic TLR agonists, recombinant mouse CD40L and agonist anti-BCR antibodies. As expected, TLR agonists induced mouse B cell proliferation and activation or differentiation into ASC. Interestingly, addition of CD40 signal to TLR agonists stimulated either B cell proliferation and activation (TLR3, TLR4, and TLR9) or differentiation into ASC (TLR1/2, TLR2/6, TLR4 and TLR7). Addition of a BCR signal to CD40L and either TLR3 or TLR9 agonists did not induce differentiation into ASC, which could be interpreted as an entrance into the memory pathway. In conclusion, our results suggest that PAMPs synergize with signals from adaptive immunity to regulate B lymphocyte fate during humoral immune response.

Morphometric shape analysis using learning vector quantization neural networks - an example distinguishing two microtine vole species

Closely related species may be very difficult to distinguish morphologically, yet sometimes morphology is the only reasonable possibility for taxonomic classification. Here we present learning-vector-quantization artificial neural networks as a powerful tool to classify specimens on the basis of geometric morphometric shape measurements. As an example, we trained a neural network to distinguish between field and root voles from Procrustes transformed landmark coordinates on the dorsal side of the skull, which is so similar in these two species that the human eye cannot make this distinction. Properly trained neural networks misclassified only 3% of specimens. Therefore, we conclude that the capacity of learning vector quantization neural networks to analyse spatial coordinates is a powerful tool among the range of pattern recognition procedures that is available to employ the information content of geometric morphometrics.

Adaptive model selection using empirical complexities

Given $n$ independent replicates of a jointly distributed pair $(X,Y)\in {\cal R}^d \times {\cal R}$, we wish to select from a fixed sequence of model classes ${\cal F}_1, {\cal F}_2, \ldots$ a deterministic prediction rule $f: {\cal R}^d \to {\cal R}$ whose risk is small. We investigate the possibility of empirically assessingthe {\em complexity} of each model class, that is, the actual difficulty of the estimation problem within each class. The estimated complexities are in turn used to define an adaptive model selection procedure, which is based on complexity penalized empirical risk.The available data are divided into two parts. The first is used to form an empirical cover of each model class, and the second is used to select a candidate rule from each cover based on empirical risk. The covering radii are determined empirically to optimize a tight upper bound on the estimation error. An estimate is chosen from the list of candidates in order to minimize the sum of class complexity and empirical risk. A distinguishing feature of the approach is that the complexity of each model class is assessed empirically, based on the size of its empirical cover.Finite sample performance bounds are established for the estimates, and these bounds are applied to several non-parametric estimation problems. The estimates are shown to achieve a favorable tradeoff between approximation and estimation error, and to perform as well as if the distribution-dependent complexities of the model classes were known beforehand. In addition, it is shown that the estimate can be consistent,and even possess near optimal rates of convergence, when each model class has an infinite VC or pseudo dimension.For regression estimation with squared loss we modify our estimate to achieve a faster rate of convergence.

Immunogenetics of invasive aspergillosis.

Invasive aspergillosis is one of the most important infections in hematopoietic stem cell transplant recipients, with an incidence rate of 5-15% and an associated mortality of 30-60%. It remains unclear why certain patients develop invasive aspergillosis while others, undergoing identical transplant regimen and similar post transplant immunosuppression, do not. Over the last decade, pattern recognition receptors such as Toll-like receptors (TLRs) and the C-type lectin receptors (CLRs) have emerged as critical components of the innate immune system. By detecting specific molecular patterns from invading microbes and initiating inflammatory and subsequent adaptive immune responses, pattern recognition receptors are strategically located at the molecular interface of hosts and pathogens. Polymorphisms in pattern recognition receptors and downstream signaling molecules have been associated with increased or decreased susceptibility to infections, suggesting that their detection may have an increasing impact on the treatment and prevention of infectious diseases in the coming years. Infectious risk stratification may be particularly relevant for patients with hematologic malignancies, because of the high prevalence and severity of infections in this population. This review summarizes the innate immune mechanisms involved in Aspergillus fumigatus detection and the role of host genetic polymorphisms in susceptibility to invasive aspergillosis.

A surface-based approach to quantify local cortical gyrification.

The high complexity of cortical convolutions in humans is very challenging both for engineers to measure and compare it, and for biologists and physicians to understand it. In this paper, we propose a surface-based method for the quantification of cortical gyrification. Our method uses accurate 3-D cortical reconstruction and computes local measurements of gyrification at thousands of points over the whole cortical surface. The potential of our method to identify and localize precisely gyral abnormalities is illustrated by a clinical study on a group of children affected by 22q11 Deletion Syndrome, compared to control individuals.

Experimental cerebral malaria develops independently of caspase recruitment domain-containing protein 9 signaling.

The outcome of infection depends on multiple layers of immune regulation, with innate immunity playing a decisive role in shaping protection or pathogenic sequelae of acquired immunity. The contribution of pattern recognition receptors and adaptor molecules in immunity to malaria remains poorly understood. Here, we interrogate the role of the caspase recruitment domain-containing protein 9 (CARD9) signaling pathway in the development of experimental cerebral malaria (ECM) using the murine Plasmodium berghei ANKA infection model. CARD9 expression was upregulated in the brains of infected wild-type (WT) mice, suggesting a potential role for this pathway in ECM pathogenesis. However, P. berghei ANKA-infected Card9(-/-) mice succumbed to neurological signs and presented with disrupted blood-brain barriers similar to WT mice. Furthermore, consistent with the immunological features associated with ECM in WT mice, Card9(-/-) mice revealed (i) elevated levels of proinflammatory responses, (ii) high frequencies of activated T cells, and (iii) CD8(+) T cell arrest in the cerebral microvasculature. We conclude that ECM develops independently of the CARD9 signaling pathway.

New multiple matched filter: design and experimental realization

A method of making a multiple matched filter which allows the recognition of different characters in successive planes in simple conditions is proposed. The generation of the filter is based on recording on the same plate the Fourier transforms of the different patterns to be recognized, each of which is affected by different spherical phase factors because the patterns have been placed at different distances from the lens. This is proved by means of experiments with a triple filter which allows satisfactory recognition of three characters.

Histone deacetylase inhibitors impair innate immune responses to Toll-like receptor agonists and to infection.

Regulated by histone acetyltransferases and deacetylases (HDACs), histone acetylation is a key epigenetic mechanism controlling chromatin structure, DNA accessibility, and gene expression. HDAC inhibitors induce growth arrest, differentiation, and apoptosis of tumor cells and are used as anticancer agents. Here we describe the effects of HDAC inhibitors on microbial sensing by macrophages and dendritic cells in vitro and host defenses against infection in vivo. HDAC inhibitors down-regulated the expression of numerous host defense genes, including pattern recognition receptors, kinases, transcription regulators, cytokines, chemokines, growth factors, and costimulatory molecules as assessed by genome-wide microarray analyses or innate immune responses of macrophages and dendritic cells stimulated with Toll-like receptor agonists. HDAC inhibitors induced the expression of Mi-2β and enhanced the DNA-binding activity of the Mi-2/NuRD complex that acts as a transcriptional repressor of macrophage cytokine production. In vivo, HDAC inhibitors increased the susceptibility to bacterial and fungal infections but conferred protection against toxic and septic shock. Thus, these data identify an essential role for HDAC inhibitors in the regulation of the expression of innate immune genes and host defenses against microbial pathogens.

On-axis joint transform correlation based on a four-level power spectrum

We propose a method to obtain a single centered correlation with use of a joint transform correlator. We analyze the required setup to carry out the whole process optically, and we also present experimental results.

Phase-only filters codified with Burckhardt's method

We show that Burckhardt's method is available to codify phase-only filters with amplitude-only variations. Correlation experimental results are given.

Effects of thresholding level variation in fringe binarization of multiobject joint transform correlation

It is possible to improve the fringe binarization method of joint transform correlation by choosing a suitable threshold level.

Controlled-intensity detection peaks in a binary joint transform correlator

In multiobject pattern recognition the height of the correlation peaks should be controlled when the power spectrum of ajoint transform correlator is binarized. In this paper a method to predetermine the value of detection peaks is demonstrated. The technique is based on a frequency-variant threshold in order to remove the intraclass terms and on a suitable factor to normalize the binary joint power spectrum. Digital simulations and experimental hybrid implementation of this method were carried out.

Diffraction theory of optimized low-resolution Fresnel encoded lenses

A mathematical model describing the behavior of low-resolution Fresnel encoded lenses (LRFEL's) encoded in any low-resolution device (e.g., a spatial light modulator) has recently been developed. From this model, an LRFEL with a short focal length was optimized by our imposing the maximum intensity of light onto the optical axis. With this model, analytical expressions for the light-amplitude distribution, the diffraction efficiency, and the frequency response of the optimized LRFEL's are derived.