Early Changes in Soil Metabolic Diversity and Bacterial Community Structure in Sugarcane under Two Harvest Management Systems

Preharvest burning is widely used in Brazil for sugarcane cropping. However, due to environmental restrictions, harvest without burning is becoming the predominant option. Consequently, changes in the microbial community are expected from crop residue accumulation on the soil surface, as well as alterations in soil metabolic diversity as of the first harvest. Because biological properties respond quickly and can be used to monitor environmental changes, we evaluated soil metabolic diversity and bacterial community structure after the first harvest under sugarcane management without burning compared to management with preharvest burning. Soil samples were collected under three sugarcane varieties (SP813250, SP801842 and RB72454) and two harvest management systems (without and with preharvest burning). Microbial biomass C (MBC), carbon (C) substrate utilization profiles, bacterial community structure (based on profiles of 16S rRNA gene amplicons), and soil chemical properties were determined. MBC was not different among the treatments. C-substrate utilization and metabolic diversity were lower in soil without burning, except for the evenness index of C-substrate utilization. Soil samples under the variety SP801842 showed the greatest changes in substrate utilization and metabolic diversity, but showed no differences in bacterial community structure, regardless of the harvest management system. In conclusion, combined analysis of soil chemical and microbiological data can detect early changes in microbial metabolic capacity and diversity, with lower values in management without burning. However, after the first harvest, there were no changes in the soil bacterial community structure detected by PCR-DGGE under the sugarcane variety SP801842. Therefore, the metabolic profile is a more sensitive indicator of early changes in the soil microbial community caused by the harvest management system.

Water Erosion on an Oxisol under Integrated Crop-Forest Systems in a Transitional Area between the Amazon and Cerrado Biomes

ABSTRACT Water erosion is one of the main factors driving soil degradation, which has large economic and environmental impacts. Agricultural production systems that are able to provide soil and water conservation are of crucial importance in achieving more sustainable use of natural resources, such as soil and water. The aim of this study was to evaluate soil and water losses in different integrated production systems under natural rainfall. Experimental plots under six different land use and cover systems were established in an experimental field of Embrapa Agrossilvipastoril in Sinop, state of Mato Grosso, Brazil, in a Latossolo Vermelho-Amarelo Distrófico (Udox) with clayey texture. The treatments consisted of perennial pasture (PAS), crop-forest integration (CFI), eucalyptus plantation (EUC), soybean and corn crop succession (CRP), no ground cover (NGC), and forest (FRS). Soil losses in the treatments studied were below the soil loss limits (11.1 Mg ha-1 yr-1), with the exception of the plot under bare soil (NGC), which exhibited soil losses 30 % over the tolerance limit. Water losses on NGC, EUC, CRP, PAS, CFI and FRS were 33.8, 2.9, 2.4, 1.7, 2.4, and 0.5 % of the total rainfall during the period of study, respectively.

AMCHP Issue Brief: Environmental Scan: State Title V Program Response to Autism Spectrum Disorder and Other Developmental Disabilities, December 2009

The increasing incidence of children identified and diagnosed with Autism Spectrum Disorders (ASD) and other developmental disabilities (DD) poses a major challenge to Title V and other programs as they try to meet the diverse and sometimes complex needs of these children. However, those state that have initiated coordinated efforts to meet the needs of these children cross systems have had the opportunity to form and/or strengthen relationships with new partners. In addition, these coordinated efforts will allow states to develop new policies, programs and financing mechanisms addressing the health of children with ASD, which may also strengthen the system of care for all Children and Youth with Special Health Care Needs.

Small RNAs controlled by two-component systems.

Two-component systems (TCSs) allow bacteria to monitor diverse environmental cues and to adjust gene expression accordingly at the transcriptional level. It has been recently recognized that prokaryotes also regulate many genes and operons at a posttranscriptional level with the participation of small, noncoding RNAs which serve to control translation initiation and stability of target mRNAs, either directly by establishing antisense interactions or indirectly by antagonizing RNA-binding proteins. Interestingly, the expression of a subset of these small RNAs is regulated by TCSs and in this way, the small RNAs expand the scope of genetic control exerted by TCSs. Here we review the regulatory mechanisms and biological relevance ofa number of small RNAs under TCS control in Gram-negative and -positive bacteria. These regulatory systems govern, for instance, porin-dependent permeability of the outer membrane, quorum-sensing control of pathogenicity, or biocontrol activity. Most likely, this emerging and rapidly expanding field of molecular microbiology will provide more and more examples in the near future.

Immunoproteomics for Serological Diagnosis of Hypersensitivity Pneumonitis Caused by Environmental Microorganisms.

Diagnosis of immunoallergenic pathologies due to microorganisms such as hypersensitivity pneumonitis includes detection of circulating specific antibodies. Detection of precipitins has classically been performed using immunoprecipitation techniques with crude antigenic extracts from microorganisms implicated as etiologic agents. However, these techniques lack standardization because of the different composition of fungal antigenic extracts from one batch to another. Therefore, there is high interest in developing standardized serological diagnostic methods using recombinant antigens. Immunoproteomics have proved to be useful for identifying the immunogenic proteins in several microorganisms linked to hypersensitivity pneumonitis. With this approach, the causative microorganisms are first isolated from the environment of patients. Then the proteins are separated by two-dimensional electrophoresis and revealed by Western blotting with sera of different patients suffering from the disease compared to sera of asymptomatic exposed controls. Immunoreactive proteins are identified by mass spectrometry. Identified immunoreactive proteins found to be specific markers for the disease could be subsequently produced as recombinant antigens using various expression systems to develop ELISA tests. Using recombinant antigens, standardized ELISA techniques can be developed, with sensitivity and specificity reaching 80% and 90%, respectively, and more if using a combination of several antigens. Immunoproteomics can be applied to any environmental microorganisms, with the aim of proposing panels of recombinant antigens able to improve the sensitivity and standardization of serologic diagnosis of hypersensitivity pneumonitis, but also other mold-induced allergic diseases such as allergic broncho pulmonary aspergillosis or asthma.

Assessing the environmental impact of water consumption by energy crops grown in Spain

The environmental impact of the water consumption of four typical crop rotations grown in Spain, including energy crops, was analyzed and compared against Spanish agricultural and natural reference situations. The life cycle assessment (LCA) methodology was used for the assessment of the potential environmental impact of blue water (withdrawal from water bodies) and green water (uptake of soil moisture) consumption. The latter has so far been disregarded in LCA. To account for green water, two approaches have been applied: the first accounts for the difference in green water demand of the crops and a reference situation. The second is a green water scarcity index, which measures the fraction of the soil-water plant consumption to the available green water. Our results show that, if the aim is to minimize the environmental impacts of water consumption, the energy crop rotations assessed in this study were most suitable in basins in the northeast of Spain. In contrast, the energy crops grown in basins in the southeast of Spain were associated with the greatest environmental impacts. Further research into the integration of quantitative green water assessment in LCA is crucial in studies of systems with a high dependence on green water resources.

Dynamic Modelling of Material Flows and Sustainable Resource Use: Case Studies in Regional Metabolism and Space Life Support Systems

Sustainable resource use is one of the most important environmental issues of our times. It is closely related to discussions on the 'peaking' of various natural resources serving as energy sources, agricultural nutrients, or metals indispensable in high-technology applications. Although the peaking theory remains controversial, it is commonly recognized that a more sustainable use of resources would alleviate negative environmental impacts related to resource use. In this thesis, sustainable resource use is analysed from a practical standpoint, through several different case studies. Four of these case studies relate to resource metabolism in the Canton of Geneva in Switzerland: the aim was to model the evolution of chosen resource stocks and flows in the coming decades. The studied resources were copper (a bulk metal), phosphorus (a vital agricultural nutrient), and wood (a renewable resource). In addition, the case of lithium (a critical metal) was analysed briefly in a qualitative manner and in an electric mobility perspective. In addition to the Geneva case studies, this thesis includes a case study on the sustainability of space life support systems. Space life support systems are systems whose aim is to provide the crew of a spacecraft with the necessary metabolic consumables over the course of a mission. Sustainability was again analysed from a resource use perspective. In this case study, the functioning of two different types of life support systems, ARES and BIORAT, were evaluated and compared; these systems represent, respectively, physico-chemical and biological life support systems. Space life support systems could in fact be used as a kind of 'laboratory of sustainability' given that they represent closed and relatively simple systems compared to complex and open terrestrial systems such as the Canton of Geneva. The chosen analysis method used in the Geneva case studies was dynamic material flow analysis: dynamic material flow models were constructed for the resources copper, phosphorus, and wood. Besides a baseline scenario, various alternative scenarios (notably involving increased recycling) were also examined. In the case of space life support systems, the methodology of material flow analysis was also employed, but as the data available on the dynamic behaviour of the systems was insufficient, only static simulations could be performed. The results of the case studies in the Canton of Geneva show the following: were resource use to follow population growth, resource consumption would be multiplied by nearly 1.2 by 2030 and by 1.5 by 2080. A complete transition to electric mobility would be expected to only slightly (+5%) increase the copper consumption per capita while the lithium demand in cars would increase 350 fold. For example, phosphorus imports could be decreased by recycling sewage sludge or human urine; however, the health and environmental impacts of these options have yet to be studied. Increasing the wood production in the Canton would not significantly decrease the dependence on wood imports as the Canton's production represents only 5% of total consumption. In the comparison of space life support systems ARES and BIORAT, BIORAT outperforms ARES in resource use but not in energy use. However, as the systems are dimensioned very differently, it remains questionable whether they can be compared outright. In conclusion, the use of dynamic material flow analysis can provide useful information for policy makers and strategic decision-making; however, uncertainty in reference data greatly influences the precision of the results. Space life support systems constitute an extreme case of resource-using systems; nevertheless, it is not clear how their example could be of immediate use to terrestrial systems.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

The Adoption of Technologies, Policies, and Systems for Improving Fleet Fuel Performance, 2007

The dramatic rise in fuel prices and growing environmental concerns are pressing freight transportation companies to pursue new systems and methods to improve fuel efficiency and reduce their environmental impact. While select major carriers appear to be leading efforts to adopt technologies that support a dramatic improvement in fuel performance, there appears to be little understanding as to the breadth and depth of efforts being taken by the broader motor carrier community, consisting of over 20,000 companies of all sizes. The purpose of this study was to investigate the level of adoption of technologies and policies to support improved fuel efficiency among motor carrier fleets.

Is sustainability certification for biochar the answer to environmental risks?

Biochar has the potential to make a major contribution to the mitigation of climate change, and enhancement of plant production. However, in order for biochar to fulfill this promise, the industry and regulating bodies must take steps to manage potential environmental threats and address negative perceptions. The potential threats to the sustainability of biochar systems, at each stage of the biochar life cycle, were reviewed. We propose that a sustainability framework for biochar could be adapted from existing frameworks developed for bioenergy. Sustainable land use policies, combined with effective regulation of biochar production facilities and incentives for efficient utilization of energy, and improved knowledge of biochar impacts on ecosystem health and productivity could provide a strong framework for the development of a robust sustainable biochar industry. Sustainability certification could be introduced to provide confidence to consumers that sustainable practices have been employed along the production chain, particularly where biochar is traded internationally.

Effect of spatial processes on the evolution of ecological niches and dispersal in metacommunity systems

RésuméLa coexistence de nombreuses espèces différentes a de tout temps intrigué les biologistes. La diversité et la composition des communautés sont influencées par les perturbations et l'hétérogénéité des conditions environnementales. Bien que dans la nature la distribution spatiale des conditions environnementales soit généralement autocorrélée, cet aspect est rarement pris en compte dans les modèles étudiant la coexistence des espèces. Dans ce travail, nous avons donc abordé, à l'aide de simulations numériques, la coexistence des espèces ainsi que leurs caractéristiques au sein d'un environnement autocorrélé.Afin de prendre en compte cet élément spatial, nous avons développé un modèle de métacommunauté (un ensemble de communautés reliées par la dispersion des espèces) spatialement explicite. Dans ce modèle, les espèces sont en compétition les unes avec les autres pour s'établir dans un nombre de places limité, dans un environnement hétérogène. Les espèces sont caractérisées par six traits: optimum de niche, largeur de niche, capacité de dispersion, compétitivité, investissement dans la reproduction et taux de survie. Nous nous sommes particulièrement intéressés à l'influence de l'autocorrélation spatiale et des perturbations sur la diversité des espèces et sur les traits favorisés dans la métacommunauté. Nous avons montré que l'autocorrélation spatiale peut avoir des effets antagonistes sur la diversité, en fonction du taux de perturbations considéré. L'influence de l'autocorrélation spatiale sur la capacité de dispersion moyenne dans la métacommunauté dépend également des taux de perturbations et survie. Nos résultats ont aussi révélé que de nombreuses espèces avec différents degrés de spécialisation (i.e. différentes largeurs de niche) peuvent coexister. Toutefois, les espèces spécialistes sont favorisées en absence de perturbations et quand la dispersion est illimitée. A l'opposé, un taux élevé de perturbations sélectionne des espèces plus généralistes, associées avec une faible compétitivité.L'autocorrélation spatiale de l'environnement, en interaction avec l'intensité des perturbations, influence donc de manière considérable la coexistence ainsi que les caractéristiques des espèces. Ces caractéristiques sont à leur tour souvent impliquées dans d'importants processus, comme le fonctionnement des écosystèmes, la capacité des espèces à réagir aux invasions, à la fragmentation de l'habitat ou aux changements climatiques. Ce travail a permis une meilleure compréhension des mécanismes responsables de la coexistence et des caractéristiques des espèces, ce qui est crucial afin de prédire le devenir des communautés naturelles dans un environnement changeant.AbstractUnderstanding how so many different species can coexist in nature is a fundamental and long-standing question in ecology. Community diversity and composition are known to be influenced by heterogeneity in environmental conditions and disturbance. Though in nature the spatial distribution of environmental conditions is frequently autocorrelated, this aspect is seldom considered in models investigating species coexistence. In this work, we thus addressed several questions pertaining to species coexistence and composition in spatially autocorrelated environments, with a numerical simulations approach.To take into account this spatial aspect, we developed a spatially explicit model of metacommunity (a set of communities linked by dispersal of species). In this model, species are trophically equivalent, and compete for space in a heterogeneous environment. Species are characterized by six life-history traits: niche optimum, niche breadth, dispersal, competitiveness, reproductive investment and survival rate. We were particularly interested in the influence of environmental spatial autocorrelation and disturbance on species diversity and on the traits of the species favoured in the metacommunity. We showed that spatial autocorrelation can have antagonistic effects on diversity depending on disturbance rate. Similarly, spatial autocorrelation interacted with disturbance rate and survival rate to shape the mean dispersal ability observed in the metacommunity. Our results also revealed that many species with various degrees of specialization (i.e. different niche breadths) can coexist together. However specialist species were favoured in the absence of disturbance, and when dispersal was unlimited. In contrast, high disturbance rate selected for more generalist species, associated with low competitive ability.The spatial structure of the environment, together with disturbance and species traits, thus strongly impacts species diversity and, more importantly, species composition. Species composition is known to affect several important metacommunity properties such as ecosystem functioning, resistance and reaction to invasion, to habitat fragmentation and to climate changes. This work allowed a better understanding of the mechanisms responsible for species composition, which is of crucial importance to predict the fate of natural metacommunities in changing environments

Harmonization ofsignificant environmental aspects and their risk assessment in UPM paper and pulp mills

Työn tavoite onharmonisoida yhtenäiset rakenteet UPM:n paperi- ja sellutehtaiden merkittävilleympäristönäkökohdille sekä niiden ympäristöriskienhallintajärjestelmille. Näin saavutetaan yhteneväiset tavoitteet ja analysointikeinot yrityksen yksiköille. Harmonisointiprosessi on osa koko yrityksen ympäristöhallintajärjestelmän kehittämistä. Ja konsernin EMS -prosessi puolestaan konvergoi konsernin integroidun johtamisjärjestelmän kehitystä. Lisäksi työn tapaustutkimuksessa selvitettiin riskienhallintajärjestelmien integroitumispotentiaalia. Sen avulla saavutettaisiin paremmin suuren yrityksen synergia-etuja ja vuorovaikutteisuutta toimijoiden kesken, sekä parannettaisiin riskienhallintajärjestelmän mukautuvuutta ja käytettävyyttä. Työssä käsitellään kolmea esimerkkiä, joiden pohjalta tehdään esitys harmonisoiduille merkittäville ympäristönäkökohdille sekä riskienhallintajärjestelmien parametreille. Tutkimusongelmaa lähestytään haastattelujen, kirjallisuuden, yrityksen PWC:llä teettämän selvityksen sekä omien päätelmien avulla. Lisäksi työssä esitetään ympäristöhallintajärjestelmän tehokkuuden todentaminen ympäristösuorituskyvyn muuttujiin suhteutettuna. Pohjana jatkuvan kehityksen päämäärälle on organisaatio-oppiminen, niin yksittäisen työntekijän, tiimien kuin eri yksiköiden kesken. Se antaa sysäyksen aineettoman omaisuuden, kuten ympäristö-osaamisen, hyödyntämiseen parhaalla mahdollisella tavalla. Tärkeimpinä lopputuloksina työssä ovat ehdotukset harmonisoiduille merkittäville ympäristönäkökohdille sekä ympäristöriskienhallintajärjestelmän määritetyille komponenteille. Niitä ovat määritelmät ja skaalat riskien todennäköisyydelle, seurauksille sekä riskiluokille. Työn viimeisenä osana luodaan pohja tapaustutkimuksen avulla Rauman tehtaan jätevedenpuhdistamon kahden erilaisen riskienhallintajärjestelmän integroitumiselle.

Ambient intelligence application based on environmental measurements performed with an assistant mobile robot

This paper proposes the use of an autonomous assistant mobile robot in order to monitor the environmental conditions of a large indoor area and develop an ambient intelligence application. The mobile robot uses single high performance embedded sensors in order to collect and geo-reference environmental information such as ambient temperature, air velocity and orientation and gas concentration. The data collected with the assistant mobile robot is analyzed in order to detect unusual measurements or discrepancies and develop focused corrective ambient actions. This paper shows an example of the measurements performed in a research facility which have enabled the detection and location of an uncomfortable temperature profile inside an office of the research facility. The ambient intelligent application has been developed by performing some localized ambient measurements that have been analyzed in order to propose some ambient actuations to correct the uncomfortable temperature profile.

Environmental and T cell-intrinsic factors limit the expansion of neonatal follicular T helper cells but may be circumvented by specific adjuvants.

Follicular Th (T(FH)) cells have emerged as a new Th subset providing help to B cells and supporting their differentiation into long-lived plasma cells or memory B cells. Their differentiation had not yet been investigated following neonatal immunization, which elicits delayed and limited germinal center (GC) responses. We demonstrate that neonatal immunization induces CXCR5(high)PD-1(high) CD4(+) T(FH) cells that exhibit T(FH) features (including Batf, Bcl6, c-Maf, ICOS, and IL-21 expression) and are able to migrate into the GCs. However, neonatal T(FH) cells fail to expand and to acquire a full-blown GC T(FH) phenotype, as reflected by a higher ratio of GC T(FH)/non-GC CD4(+) T cells in immunized adults than neonates (3.8 × 10(-3) versus 2.2 × 10(-3), p = 0.01). Following the adoptive transfer of naive adult OT-II CD4(+) T cells, OT-II T(FH) cells expand in the vaccine-draining lymph nodes of immunized adult but not infant recipients, whereas naive 2-wk-old CD4(+) OT-II cells failed to expand in adult hosts, reflecting the influence of both environmental and T cell-intrinsic factors. Postponing immunization to later in life increases the number of T(FH) cells in a stepwise manner, in direct correlation with the numbers of GC B cells and plasma cells elicited. Remarkably, adjuvantation with CpG oligonucleotides markedly increased GC T(FH) and GC B cell neonatal responses, up to adult levels. To our knowledge, this is the first demonstration that the T(FH) cell development limits early life GC responses and that adjuvants/delivery systems supporting T(FH) differentiation may restore adultlike early life GC B cell responses.

Settlement evolution, field systems and sedimentary processes in a Mediterranean mid-mountain (Montseny Massif, Catalunya)

This paper presents the first results of a current research project about human – environmental interactions in the Montseny Massif. Our work sets out to integrate two research lines in the studied area: - Archaeological and archaeo-morphological surveys in a lower part of the mountains in order to characterize the evolution of the settlements and field systems. - The geological and geomorphological characterization of the slope and terrace deposits in relation with field systems and archaeological data. First results point out the intensive occupation of these inland areas during the Iberian and the Roman periods. Post-Roman sediments show different processes of erosion.