Heat perception and signalling in plants: a tortuous path to thermotolerance.

An accurate assessment of the rising ambient temperature by plant cells is crucial for the timely activation of various molecular defences before the appearance of heat damage. Recent findings have allowed a better understanding of the early cellular events that take place at the beginning of mild temperature rise, to timely express heat-shock proteins (HSPs), which will, in turn, confer thermotolerance to the plant. Here, we discuss the key components of the heat signalling pathway and suggest a model in which a primary sensory role is carried out by the plasma membrane and various secondary messengers, such as Ca(2+) ions, nitric oxide (NO) and hydrogen peroxide (H(2) O(2) ). We also describe the role of downstream components, such as calmodulins, mitogen-activated protein kinases and Hsp90, in the activation of heat-shock transcription factors (HSFs). The data gathered for land plants suggest that, following temperature elevation, the heat signal is probably transduced by several pathways that will, however, coalesce into the final activation of HSFs, the expression of HSPs and the onset of cellular thermotolerance.

Intranasal administration of the synthetic polypeptide from the C-terminus of the circumsporozoite protein of Plasmodium berghei with the modified heat-labile toxin of Escherichia coli (LTK63) induces a complete protection against malaria challenge.

Needle-free procedures are very attractive ways to deliver vaccines because they diminish the risk of contamination and may reduce local reactions, pain or pain fear especially in young children with a consequence of increasing the vaccination coverage for the whole population. For this purpose, the possible development of a mucosal malaria vaccine was investigated. Intranasal immunization was performed in BALB/c mice using a well-studied Plasmodium berghei model antigen derived from the circumsporozoite protein with the modified heat-labile toxin of Escherichia coli (LTK63), which is devoid of any enzymatic activity compared to the wild type form. Here, we show that intranasal administration of the two compounds activates the T and B cell immune response locally and systemically. In addition, a total protection of mice is obtained upon a challenge with live sporozoites.

Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar

In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Statistical assessment of dataset shift and model portability in multi-angle in-track image acquisitions

In this study we propose an evaluation of the angular effects altering the spectral response of the land-cover over multi-angle remote sensing image acquisitions. The shift in the statistical distribution of the pixels observed in an in-track sequence of WorldView-2 images is analyzed by means of a kernel-based measure of distance between probability distributions. Afterwards, the portability of supervised classifiers across the sequence is investigated by looking at the evolution of the classification accuracy with respect to the changing observation angle. In this context, the efficiency of various physically and statistically based preprocessing methods in obtaining angle-invariant data spaces is compared and possible synergies are discussed.

Rufinamide Attenuates Mechanical Allodynia in a Model of Neuropathic Pain in the Mouse and Stabilizes Voltage-gated Sodium Channel Inactivated State.

BACKGROUND:: Voltage-gated sodium channels dysregulation is important for hyperexcitability leading to pain persistence. Sodium channel blockers currently used to treat neuropathic pain are poorly tolerated. Getting new molecules to clinical use is laborious. We here propose a drug already marketed as anticonvulsant, rufinamide. METHODS:: We compared the behavioral effect of rufinamide to amitriptyline using the Spared Nerve Injury neuropathic pain model in mice. We compared the effect of rufinamide on sodium currents using in vitro patch clamp in cells expressing the voltage-gated sodium channel Nav1.7 isoform and on dissociated dorsal root ganglion neurons to amitriptyline and mexiletine. RESULTS:: In naive mice, amitriptyline (20 mg/kg) increased withdrawal threshold to mechanical stimulation from 1.3 (0.6-1.9) (median [95% CI]) to 2.3 g (2.2-2.5) and latency of withdrawal to heat stimulation from 13.1 (10.4-15.5) to 30.0 s (21.8-31.9), whereas rufinamide had no effect. Rufinamide and amitriptyline alleviated injury-induced mechanical allodynia for 4 h (maximal effect: 0.10 ± 0.03 g (mean ± SD) to 1.99 ± 0.26 g for rufinamide and 0.25 ± 0.22 g to 1.92 ± 0.85 g for amitriptyline). All drugs reduced peak current and stabilized the inactivated state of voltage-gated sodium channel Nav1.7, with similar effects in dorsal root ganglion neurons. CONCLUSIONS:: At doses alleviating neuropathic pain, amitriptyline showed alteration of behavioral response possibly related to either alteration of basal pain sensitivity or sedative effect or both. Side-effects and drug tolerance/compliance are major problems with drugs such as amitriptyline. Rufinamide seems to have a better tolerability profile and could be a new alternative to explore for the treatment of neuropathic pain.

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland.

PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.

Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms.

The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.

Selective inhibition of JNK with a peptide inhibitor attenuates pain hypersensitivity and tumor growth in a mouse skin cancer pain model.

Cancer pain significantly affects the quality of cancer patients, and current treatments for this pain are limited. C-Jun N-terminal kinase (JNK) has been implicated in tumor growth and neuropathic pain sensitization. We investigated the role of JNK in cancer pain and tumor growth in a skin cancer pain model. Injection of luciferase-transfected B16-Fluc melanoma cells into a hindpaw of mouse induced robust tumor growth, as indicated by increase in paw volume and fluorescence intensity. Pain hypersensitivity in this model developed rapidly (<5 days) and reached a peak in 2 weeks, and was characterized by mechanical allodynia and heat hyperalgesia. Tumor growth was associated with JNK activation in tumor mass, dorsal root ganglion (DRG), and spinal cord and a peripheral neuropathy, such as loss of nerve fibers in the hindpaw skin and induction of ATF-3 expression in DRG neurons. Repeated systemic injections of D-JNKI-1 (6 mg/kg, i.p.), a selective and cell-permeable peptide inhibitor of JNK, produced an accumulative inhibition of mechanical allodynia and heat hyperalgesia. A bolus spinal injection of D-JNKI-1 also inhibited mechanical allodynia. Further, JNK inhibition suppressed tumor growth in vivo and melanoma cell proliferation in vitro. In contrast, repeated injections of morphine (5 mg/kg), a commonly used analgesic for terminal cancer, produced analgesic tolerance after 1 day and did not inhibit tumor growth. Our data reveal a marked peripheral neuropathy in this skin cancer model and important roles of the JNK pathway in cancer pain development and tumor growth. JNK inhibitors such as D-JNKI-1 may be used to treat cancer pain.

Activities of fosfomycin and rifampin on planktonic and adherent Enterococcus faecalis strains in an experimental foreign-body infection model.

Enterococcal implant-associated infections are difficult to treat because antibiotics generally lack activity against enterococcal biofilms. We investigated fosfomycin, rifampin, and their combinations against planktonic and adherent Enterococcus faecalis (ATCC 19433) in vitro and in a foreign-body infection model. The MIC/MBClog values were 32/>512 μg/ml for fosfomycin, 4/>64 μg/ml for rifampin, 1/2 μg/ml for ampicillin, 2/>256 μg/ml for linezolid, 16/32 μg/ml for gentamicin, 1/>64 μg/ml for vancomycin, and 1/5 μg/ml for daptomycin. In time-kill studies, fosfomycin was bactericidal at 8× and 16× MIC, but regrowth of resistant strains occurred after 24 h. With the exception of gentamicin, no complete inhibition of growth-related heat production was observed with other antimicrobials on early (3 h) or mature (24 h) biofilms. In the animal model, fosfomycin alone or in combination with daptomycin reduced planktonic counts by ≈4 log10 CFU/ml below the levels before treatment. Fosfomycin cleared planktonic bacteria from 74% of cage fluids (i.e., no growth in aspirated fluid) and eradicated biofilm bacteria from 43% of cages (i.e., no growth from removed cages). In combination with gentamicin, fosfomycin cleared 77% and cured 58% of cages; in combination with vancomycin, fosfomycin cleared 33% and cured 18% of cages; in combination with daptomycin, fosfomycin cleared 75% and cured 17% of cages. Rifampin showed no activity on planktonic or adherent E. faecalis, whereas in combination with daptomycin it cured 17% and with fosfomycin it cured 25% of cages. Emergence of fosfomycin resistance was not observed in vivo. In conclusion, fosfomycin showed activity against planktonic and adherent E. faecalis. Its role against enterococcal biofilms should be further investigated, especially in combination with rifampin and/or daptomycin treatment.

Murine model of pneumonia caused by Parachlamydia acanthamoebae

The role of Parachlamydia acanthamoebae as an agent of pneumonia is suggested by sero-epidemiological studies, molecular surveys and by the permissivity of macrophages, lung fibroblasts and pneumocytes to this obligate intracellular bacteria. We thus developed a murine model of pneumonia due to Parachlamydia. Mice were inoculated intratracheally with Parachlamydia acanthamoebae. Pneumonia-associated mortality was of 50% 5 days post-inoculation. Lungs histopathology was characterized by purulent and interstitial pneumonia. The presence of Parachlamydia in the lesions was demonstrated by PCR, immunohistochemistry and electron microscopy. Moreover, living Parachlamydia could be recovered from the lungs of infected mice using amoebal co-culture. All control mice inoculated with heat-inactivated bacteria were free of symptoms and survived. Thus, we demonstrated that Parachlamydia induce a severe pneumonia in mice. This animal model, which confirms the third and fourth Koch postulates, may be suitable to test in vivo efficient therapeutic regimens against Parachlamydia.

A bovine model of a respiratory Parachlamydia acanthamoebae infection.

The aim of this study was to evaluate the pathogenicity of Parachlamydia (P.) acanthamoebae as a potential agent of lower respiratory tract disease in a bovine model of induced lung infection. Intrabronchial inoculation with P. acanthamoebae was performed in healthy calves aged 2-3 months using two challenge doses: 10(8) and 10(10) bacteria per animal. Controls received 10(8) heat-inactivated bacteria. Challenge with 10(8) viable Parachlamydia resulted in a mild degree of general indisposition, whereas 10(10) bacteria induced a more severe respiratory illness becoming apparent 1-2 days post inoculation (dpi), affecting 9/9 (100%) animals and lasting for 6 days. The extent of macroscopic pulmonary lesions was as high as 6.6 (6.0)% [median (range)] of lung tissue at 2-4 dpi and correlated with parachlamydial genomic copy numbers detected by PCR, and with bacterial load estimated by immunohistochemistry in lung tissue. Clinical outcome, acute phase reactants, pathological findings and bacterial load exhibited an initial dose-dependent effect on severity. Animals fully recovered from clinical signs of respiratory disease within 5 days. The bovine lung was shown to be moderately susceptible to P. acanthamoebae, exhibiting a transient pneumonic inflammation after intrabronchial challenge. Further studies are warranted to determine the precise pathophysiologic pathways of host-pathogen interaction.

3-D density structure and geological evolution of Stromboli volcano (Aeolian Islands, Italy) inferred from land-based and sea-surface gravity data

We present the first density model of Stromboli volcano (Aeolian Islands, Italy) obtained by simultaneously inverting land-based (543) and sea-surface (327) relative gravity data. Modern positioning technology, a 1 x 1 m digital elevation model, and a 15 x 15 m bathymetric model made it possible to obtain a detailed 3-D density model through an iteratively reweighted smoothness-constrained least-squares inversion that explained the land-based gravity data to 0.09 mGal and the sea-surface data to 5 mGal. Our inverse formulation avoids introducing any assumptions about density magnitudes. At 125 m depth from the land surface, the inferred mean density of the island is 2380 kg m(-3), with corresponding 2.5 and 97.5 percentiles of 2200 and 2530 kg m-3. This density range covers the rock densities of new and previously published samples of Paleostromboli I, Vancori, Neostromboli and San Bartolo lava flows. High-density anomalies in the central and southern part of the island can be related to two main degassing faults crossing the island (N41 and NM) that are interpreted as preferential regions of dyke intrusions. In addition, two low-density anomalies are found in the northeastern part and in the summit area of the island. These anomalies seem to be geographically related with past paroxysmal explosive phreato-magmatic events that have played important roles in the evolution of Stromboli Island by forming the Scari caldera and the Neostromboli crater, respectively. (C) 2014 Elsevier B.V. All rights reserved.