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In the total matching problem, one is given a graph G with weights on the vertices and edges. The goal is to find a maximum weight set of vertices and edges that is the non-incident union of a stable set and a matching. We consider the natural formulation of the problem as an integer program (IP), with variables corresponding to vertices and edges. Let M = M(G) denote the constraint matrix of this IP. We define Delta(G) as the maximum absolute value of the determinant of a square submatrix of M. We show that the total matching problem can be solved in strongly polynomial time provided Delta(G) <= Delta for some constant Delta is an element of Z(>= 1). We also show that the problem of computing Delta(G) admits an FPT algorithm. We also establish further results on Delta(G) when G is a forest.

Urban computing techniques harnessing digital mobility traces can assume a pivotal role in comprehending travel patterns and behavioral dynamics within an urban context. These methodologies yield invaluable insights for transportation planners, enabling them to make informed decisions and augment the overall efficiency of urban transportation systems. The goal of this research is to analyze the parking phenomenon in urban areas and develop predictive models for parking-related indicators. Specifically, we introduce two quantitative metrics: the Average Parking Time and the Average Number of Simultaneously Parked Vehicles, aimed at characterizing parking activities from temporal and volumetric perspectives. A large collection of raw GPS traces from a sample of private cars in the metropolitan areas of Rome was used to investigate and model the spatiotemporal dynamics of parking demand and saturation on public streets. Our investigation entailed the application of a diverse array of Machine Learning and Deep Learning techniques, encompassing statistical models, Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs), for the prediction of these indicator values. In our experiments, we found that the 3D-CLoST model excelled in accurately predicting parking indicators compared to other techniques. Interestingly, we also observed that statistical models were able to achieve performance levels that were similar to those of more complex models.

We introduce QuaterGCN, a spectral Graph Convolutional Network (GCN) with quaternion-valued weights at whose core lies the Quaternionic Laplacian, a quaternion-valued Laplacian matrix by whose proposal we generalize two widely-used Laplacian matrices: the classical Laplacian (defined for undirected graphs) and the complex-valued Sign-Magnetic Laplacian (proposed to handle digraphs with weights of arbitrary sign). In addition to its generality, our Quaternionic Laplacian is the only Laplacian to completely preserve the topology of a digraph, as it can handle graphs and digraphs containing antiparallel pairs of edges (digons) of different weights without reducing them to a single (directed or undirected) edge as done with other Laplacians. Experimental results show the superior performance of QuaterGCN compared to other state-of-the-art GCNs, particularly in scenarios where the information the digons carry is crucial to successfully address the task at hand.

In recent years, studying and predicting mobility patterns in urban environments has become increasingly important as accurate and timely information on current and future vehicle flows can successfully increase the quality and availability of transportation services (e.g., sharing services). However, predicting the number of incoming and outgoing vehicles for different city areas is challenging due to the nonlinear spatial and temporal dependencies typical of urban mobility patterns. In this work, we propose STREED-Net, a novel autoencoder architecture featuring time-distributed convolutions, cascade hierarchical units and two distinct attention mechanisms (one spatial and one temporal) that effectively captures and exploits complex spatial and temporal patterns in mobility data for short-term flow prediction problem. The results of a thorough experimental analysis using real-life data are reported, indicating that the proposed model improves the state-of-the-art for this task.

Recent years have witnessed the emerging of novel shared mobility solutions that provide diffused on-demand access to transportation. The widespread adoption of these solutions, particularly electric mopeds (e-mopeds), is expected to bring important benefits such as the reduction of noise and atmospheric pollution, and road congestion, with extensive repercussions on liveability and quality of life in urban areas. Currently, almost no effort has been devoted to exploring the adoption patterns of e-moped sharing services, therefore, optimal management and allocation of vehicles appears to be a problem for service managers. In this study, we tried to demonstrate the validity of the hypothesis that the adoption of electric mopeds depends on the built environment and demographic aspects of each neighbourhood. In detail, we singled out three features concerning the area characteristics (distance from centre, walkability, concentration of places) and one about the population (education index). The results obtained on a real world case study show the strong impact these factors have in determining the adoption of e-moped sharing services. Finally, an analysis was conducted on the possible role that the electric moped sharing can play in social equalization by studying the interactions between rich and poor neighbourhoods. The results of the analyses conducted indicate that communities within a city tend to aggregate by wealth and isolate themselves from one another (social isolation): very few interactions, in terms of trajectories, have been observed between the richest and poorest areas of the city under study.

Le città sono in continua evoluzione a causa dei cambiamenti e delle interazioni con i loro cittadini. Sono sistemi complessi composti da individui, veicoli e infrastrutture che costituiscono il cuore della vita moderna e dello sviluppo sociale. Un fattore chiave che guida questa crescita è l'avanzamento della mobilità verso uno scenario più intelligente e tecnologico, noto anche come Smart Mobility. Questo concetto implica il passaggio da un sistema di trasporto tradizionale a uno più innovativo, in cui un tessuto infrastrutturale intelligente collega le parti interessate e le entità per fornire soluzioni efficienti e sostenibili. I ricercatori in questo campo pongono una forte enfasi sulla comprensione e sull'identificazione dei modelli di spostamento degli individui, al fine di migliorare i servizi e, più in generale, la vivibilità delle città. L'avvento della Data Science ha rivelato l'importanza dello sviluppo e dell'applicazione di tecniche di Intelligenza Artificiale nella creazione di sistemi urbani automatizzati e intelligenti. Sebbene l'IA sia sempre più utilizzata nella Smart Mobility, ci sono ancora molte sfide da affrontare. Per questo motivo, in questa tesi, l'IA e la Scienza dei Trasporti vengono combinate per risolvere problemi di mobilità attraverso l'estrazione di informazioni dai dati di mobilità e lo sviluppo di algoritmi sofisticati. Tramite un'ampia sperimentazione, dimostriamo che le soluzioni proposte migliorano effettivamente la comprensione e l'applicazione dell'intelligenza artificiale ai problemi di mobilità. In particolare, ci concentriamo inizialmente sull'elaborazione dei dati e sull'estrazione della conoscenza da essi. Per spiegare l'adozione di veicoli per la mobilità condivisa, proponiamo l'uso di caratteristiche geografiche (distanza dal centro, walkability, concentrazione di punti di interesse) e demografiche (indice di istruzione). Inoltre, presentiamo un algoritmo di ottimizzazione multi-obiettivo per identificare, Cities are constantly evolving due to the changes and interactions with their citizens. They are complex systems made up of individuals, vehicles, and infrastructures that form the heart of modern life and social development. A key factor driving this growth is the advancement of mobility towards a more intelligent and technological scenario, also known as Smart Mobility. This concept involves moving from a traditional transportation system to a more advanced one, where an intelligent infrastructure fabric connects stakeholders and entities to provide efficient, smart, and sustainable solutions. Researchers in this field place a strong emphasis on understanding and identifying individuals' displacement patterns, in order to enhance the services and, more in general, the livability of cities. The advent of Data Science has revealed the importance of developing and applying Artificial Intelligence techniques in the creation of automated and intelligent city systems. Although AI is increasingly used in Smart Mobility, there are still many challenges to be addressed. For this reason, in this thesis, AI and Transportation Science are combined to develop solutions to mobility-related problems by extracting knowledge from mobility data and developing sophisticated algorithms. We show, through extensive experimentation, that the proposed solutions effectively improve the understanding and application of Artificial Intelligence in mobility problems. In particular, we initially focus on data processing and knowledge extraction. In order to explain the adoption of shared mobility vehicles, we propose the use of geographic (distance from center, walkability, concentration of places) and demographic features (education index). Additionally, we present a multi-objective optimization algorithm to identify behavioral communities, taking into account shared mobility usage patterns, distance between areas, and structural information (via Map Embeddings). We also explore the theoretic

Mobile applications and portable devices are being used extensively in the healthcare sector due to their rapid development. Wearable devices having sensors can be used to collect, analyze, and transmit the vital signs of the wearer. In this paper, we have proposed a wearable expert system that supports and monitors the skier during his activity. This research work is motivated by the need to provide rapid assistance to skiers, especially during off-piste skiing, where its more dangerous, and seeking help is difficult with mishaps. Our approach mainly focuses on proposing an expert system that integrates wearable devices (helmet, goggles, digital watch) with the skier’s smartphone. We present an architecture model and knowledge artifacts to design a wearable expert system for skiing.

E-scooter sharing lets people rent an e-scooter while the system owner manages the fleet. Relocation is fundamental to increase system utilization and revenues, but it is also an expensive task. In this paper we aim at assessing the benefits of relocation while quantifying its economic costs. For this, we rely on trace driven simulations where we build upon millions of actual rentals from two cities, Austin and Louisville. Firstly, we build prediction models to estimate which areas will present a surplus or a lack of e-scooters. We compare a simple stationary model with a state-of-art deep-learning model. Secondly, we replay the exact same traces to quantify the benefits of a relocation heuristic, comparing different system options. Our results show that relocation is fundamental to maximize the number of trips the system can satisfy. Interestingly, even a light and simple relocation policy with few relocations per hour can improve the percentage of satisfied trips by up to 42%. This can also translate in a fleet size reduction without impacting the performances. However, when projected into the economic benefits, the additional costs of relocation must be carefully considered to avoid wasting its benefits.

Characterizing urban communities is essential for understanding citizens' needs and neighborhood-wise dynamics. Discriminating factors are population mobility patterns, neighborhood structural characteristics, and distance to other areas of the city. Available approaches focus on one aspect and, often, suffer from isolated nodes and excessive geographical fragmentation of solutions. For these reasons, we formulate the problem of urban community clustering considering all three aspects and provide an algorithm that combines hierarchical aggregation with node adjustment and relocation. We evaluate our approach on a real-world data set and the obtained results show its efficacy. Finally, we also show the importance of using map embedding for characterizing neighborhood from the structural standpoint.

The problem of reliably predicting vehicle flows is paramount for traffic management, risk assessment, and public safety. It is a challenging problem as it is influenced by multiple factors, such as spatio-temporal dependencies with external factors (as events and weather conditions). In recent years, with the exponential data growth and technological advancement, deep learning has been adopted to approach urban mobility problems by addressing spatial dependency with convolutional neural networks and the temporal one with recurrent neural networks. We propose a spatio-temporal flow prediction framework, called 3D-CLoST, that exploits the synergy between 3D convolution and long short-term memory (LSTM) networks to jointly learn the characteristics of the space-time correlation from low to high levels. To the best of our knowledge, no method currently proposes such a structure for this problem. The results achieved on the two real-world datasets show that 3D-CLoST can learn behaviors from the data effectively.

Thin-slices multi-detector computed tomography (MDCT) plays a key role in the differential diagnosis of interstitial lung disease (ILD). However, thin-slices MDCT has a limited ability to detect active inflammation, which is an important target of newly developed ILD drug therapy. Magnetic resonance imaging (MRI), thanks to its multi-parameter capability, provides better tissue characterisation than thin-slices MDCT.Our aim was to summarise the current status of MRI applications in ILD and to propose an ILD-MRI protocol. A systematic literature search was conducted for relevant studies on chest MRI in patients with ILD.We retrieved 1246 papers of which 55 original papers were selected for the review. We identified 24 studies comparing image quality of thin-slices MDCT and MRI using several MRI sequences. These studies described new MRI sequences to assess ILD parenchymal abnormalities, such as honeycombing, reticulation and ground-glass opacity. Thin-slices MDCT remains superior to MRI for morphological imaging. However, recent studies with ultra-short echo-time MRI showed image quality comparable to thin-slices MDCT. Several studies demonstrated the added value of chest MRI by using functional imaging, especially to detect and quantify inflammatory changes.We concluded that chest MRI could play a role in ILD patients to differentiate inflammatory and fibrotic changes and to assess efficacy of new ILD drugs.

[Aim]: To study the efficiency of multifunctional polymer-based superparamagnetic iron oxide nanoparticles (bioferrofluids) as a T2 magnetic resonance contrast agent and their uptake and toxicity in liver. [Materials & methods]: Mice were intravenously injected with bioferrofluids and Endorem®. The magnetic resonance efficiency, uptake and in vivo toxicity were investigated by means of magnetic resonance imaging (MRI) and histological techniques. [Results]: Bioferrofluids are a good T2 contrast agent with a higher r2/r1 ratio than Endorem. Bioferrofluids have a shorter blood circulation time and persist in liver for longer time period compared with Endorem. Both bioferrofluids and Endorem do not generate any noticeable histological lesions in liver over a period of 60 days post-injection. [Conclusion]: Our bioferrofluids are powerful diagnostic tool without any observed toxicity over a period of 60 days post-injection.

We solve a 20-year old problem posed by Yannakakis and prove that no polynomial-size linear program (LP) exists whose associated polytope projects to the traveling salesman polytope, even if the LP is not required to be symmetric. Moreover, we prove that this holds also for the cut polytope and the stable set polytope. These results were discovered through a new connection that we make between one-way quantum communication protocols and semidefinite programming reformulations of LPs.

We solve a 20-year old problem posed by Yannakakis and prove that there exists no polynomial-size linear program (LP) whose associated polytope projects to the traveling salesman polytope, even if the LP is not required to be symmetric. Moreover, we prove that this holds also for the cut polytope and the stable set polytope. These results were discovered through a new connection that we make between one-way quantum communication protocols and semidefinite programming reformulations of LPs.

Cortical reorganization occurring in multiple sclerosis(MS)patients isthoughtto playakeyrole in limiting the effect of structural tissuedamage. Conversely, its exhaustionmaycontribute to the irreversible disability that accumulates with disease progression. Several aspects of MS-related cortical reorganization, including the overall functional effect and likely modulation by therapies, still remain to be elucidated. The aim of this work was to assess the extent of functional cortical reorganization and its brain structural/pathological correlates in Dark Agouti rats with experimental autoimmune encephalomyelitis (EAE), a widely accepted preclinical model of chronic MS. Morphological and functional MRI (fMRI) were performed before disease induction and during the relapsing and chronic phases of EAE. During somatosensory stimulation of the right forepaw, fMRI demonstrated that cortical reorganization occurs in both relapsing and chronic phases of EAE with increased activated volume and decreased laterality index versus baseline values. Voxel-based morphometry demonstrated gray matter (GM) atrophy in the cerebral cortex, and both GM and white matter atrophy were assessed by ex vivo pathology of the sensorimotor cortex and corpus callosum. Neuroinflammation persisted in the relapsing and chronic phases, with dendritic spine density in the layer IV sensory neurons inversely correlating with the number of cluster of differentiation 45-positive inflammatory lesions. Our work provides an innovative experimental platform that may be pivotal for the comprehension of key mechanisms responsible for the accumulation of irreversible brain damage and for the development of innovative therapies to reduce disability in EAE/MS.

Experimental Autoimmune Encephalomyelitis (EAE) is a good model of Multiple Sclerosis in rodents. We have applied fMRI to invesigate the alteration in functional response in rats induced with EAE. Cortical activation is altered in EAE rat brain, compared to controls. fMRI showed an increase in the activated volume involving also the cortex ipsilateral and some extra-cortical areas at the observed time points. These results demonstrate brain cortex remodeling in EAE, a remakable feature of MS. The present model seems to be a relevant tool in preclinical MS studies.

In this paper, we propose a cognitive semantic approach to represent part-whole relations. We base our proposal on the theory of conceptual spaces, focusing on prototypical structures in part-whole relations. Prototypical structures are not accounted for in traditional mereological formalisms. In our account, parts and wholes are represented in distinct conceptual spaces; parts are joined to form wholes in a structure space. The structure space allows systematic similarity judgments between wholes, taking into consideration shared parts and their configurations. A point in the structure space denotes a particular part structure; regions in the space represent different general types of part structures. We argue that the structural space can represent prototype effects: structural types are formed around typical arrangements of parts. We also show how structure space captures the variations in part structure of a given concept across different domains. In addition, we discuss how some taxonomies of part-whole relations can be understood within our framework. © 2013 Marta Olivetti Belardinelli and Springer-Verlag.

Unambiguous definition of spatial position and orientation has crucial importance for robotics. In this paper we propose an ontology about positioning. It is part of a more extensive core ontology being developed by the IEEE RAS Working Group on ontologies for robotics and automation. The core ontology should provide a common ground for further ontology development in the field. We give a brief overview of concepts in the core ontology and then describe an integrated approach for representing quantitative and qualitative position information. © 2013 IEEE

In this paper, we present the current results of the newly formed IEEE-RAS Working Group, named Ontologies for Robotics and Automation. In particular, we introduce a core ontology that encompasses a set of terms commonly used in Robotics and Automation along with the methodology we have adopted. Our work uses ISO/FDIS 8373 standard developed by the ISO/TC184/SC2 Working Group as a reference. This standard defines, in natural language, some generic terms which are common in Robotics and Automation such as robot, robotic device, etc. Furthermore, we discuss the ontology development process employed along with the problems and decisions taken

Molecular lipid biomarkers (hydrocarbons, alcohols, sterols and fatty acids) and compound-specific isotope analysis of suspended particulate organic matter (SPM) and surface sediments of the Mackenzie Shelf and slope (southeast Beaufort Sea, Arctic Ocean) were studied in summer 2009. The concentrations of the molecular lipid markers, characteristic of known organic matter sources, were grouped and used as proxies to evaluate the relative importance of fresh algal, detrital algal, fossil, C3 terrestrial plants, bacterial and zooplankton material in the organic matter (OM) of this area. Fossil and detrital algal contributions were the major fractions of the freshwater SPM from the Mackenzie River with ~34% each of the total molecular biomarkers. Fresh algal, C3 terrestrial, bacterial and zooplanktonic components represented much lower percentages, 17, 10, 4 and <1%, respectively. In marine SPM from the Mackenzie slope, the major contributions were fresh and detrital algal components (>80%), with a minor contribution of fossil and C3 terrestrial biomarkers. Characterization of the sediments revealed a major sink of refractory algal material mixed with some fresh algal material, fossil hydrocarbons and a small input of C3 terrestrial sources. In particular, the sediments from the shelf and at the mouth of the Amundsen Gulf presented the highest contribution of detrital algal material (60–75%), whereas those from the slope contained the highest proportion of fossil (40%) and C3 terrestrial plant material (10%). Overall, considering that the detrital algal material is marine derived, autochthonous sources contributed more than allochthonous sources to the OM lipid pool. Using the ratio of an allochthonous biomarker (normalized to total organic carbon, TOC) found in the sediments to those measured at the river mouth water, we estimated that the fraction of terrestrial material preserved in the sediments accounted for 30–40% of the total carbon in the inner shelf sediments

The production of transparent exopolymeric particles (TEP) by the coccolithophores, Emiliania huxleyi, Calcidiscus leptoporus and Syracosphaera pulchra was investigated in batch cultures. The abundance, size spectra and carbon content of TEP were examined during the exponential growth phase of both haploid and diploid life stages grown under ambient (400 atm) and elevated (760 atm) CO2 partial pressure (pCO(2)) conditions. Results showed species- and life stage-specific differences in TEP production rate (day(1)) derived from abundance and carbon content of TEP. At 400 atm, TEP production rate was the highest in the diploid stage of S. pulchra and E. huxleyi, while TEP carbon content per cell was the highest in the diploid stage of C. leptoporus. At 760 atm, TEP production rate increased in almost all species and was closely related to the cell growth rates (except in the diploid stage of C. leptoporus), while the slope values of the regression lines between TEP size distribution and concentration decreased. This means that the contribution of smaller size TEP was relatively more important than larger TEP in the high pCO(2) treatment. Elevated pCO(2) is potentially able to alter TEP size distribution. TEP-C content cell(1) generally decreased with increasing pCO(2). TEP-C accounted for 124 of the cell particulate organic carbon production and was inversely related to increasing pCO(2). TEP production by C. leptoporus and S. pulchra has not previously been documented. The amount of organic carbon released as TEP by these coccolithophores is comparable to and may even exceed TEP production by some diatoms.

Multiple Sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) is used for MS first diagnosis and the evaluation of CNS damage extension during disease course while information about cortical area functional reorganization can be obtained by means of functional MRI (fMRI). The clinical patterns of disease evolution are highly variable and scarcely correlate with structural MRI-detected CNS damage. This phenomenon has been referred to as clinical/MRI paradox. Experimental Autoimmune Encephalomyelitis (EAE) is the animal model for MS and can be induced in Dark Agouti (DA) rat reproducing the condition experienced by the most MS patients, i.e. the remitting-relapsing form. fMRI observations in MS are already available in humans, but deeper knowledge on its usefulness might be gained using reliable animal models. EAE was induced by syngenic spinal cord intrafootpad administration with clinical disease onset around 10 days post EAE induction (dpi) and the worst clinical condition reached on 14dpi without a complete symptom resolution in main animals up to 45dpi. The brain plasticity was investigated by means of serial fMRI acquisitions performed before, 30 and 60 days after EAE induction. A train of squared pulses electrical stimulation (frequency=3Hz, current=2mA, duration=0.5ms) was delivered to the left forepaw during acquisition of MRI sensitive to Blood-Volume. A single stimulation protocol was composed of 30 images under rest condition and 10 images acquired during stimulation. After appropriate image analysis, performed using the FSL software package, the brain region activated by the applied stimulus was determined. The week before EAE induction, electrical stimulation resulted in a localized response only in the contralateral sensory motor cortex according to previously reported results. Thirty and 60dpi, the activated area was greatly increased covering large regions of both contra a

Multiple Sclerosis (MS) is a chronic inflammatory demyelination disease of the central nervous system. It is characterized by blood brain barrier leakage during exacerbation phases and impairment in remyelination processes which are both detected by means of magnetic resonance imaging (MRI). Despite this technique is mainly used as diagnostic morphological tool for active and quiescent lesions, functional NMR (fMRI) may be used in MS patients to investigate the functional reorganization of brain cortex observed to overcome brain lesions. fMRI observations are already available in humans, but deeper knowledge on its usefulness might be gained using reliable animals models. By means of fMRI we investigated the brain plasticity in a chronic model of MS, i.e. Experimental Autoimmune Encephalomyelitis (EAE) in the Dark Agouti (DA) rat strain. Serial fMRI with somatosensory stimulation [1] and acquisitions were performed before, 30 and 60 days after EAE induction (dpi). Briefly electrical stimulation was delivered to the left forepaw during acquisition of MR images sensitive to Blood-Volume. A single stimulation protocol was composed of 30 images under rest condition and 10 images acquired during stimulation. After appropriate image analysis, performed using the FSL software package [2], the brain region activated by the applied stimulus was determined. Prior to EAE induction, electrical stimulation resulted in a localized response in the contralateral sensory motor cortex. Thirty and 60dpi, the activated area was greatly increased covering large regions of both contra and ipsilateral somatosensory cortex and extending also to extra-cortical regions. Our results show that DA EAE reproduces a remarkable findings observed in MS patients, i.e. the functional reorganization of motor cortex. It remains to be investigated whether this effect could represent an innovative platform for testing new therapeutic approaches for MS. References [1] Van Camp N, et al., NMR in Biomedicin

Molecular lipid biomarkers (hydrocarbons, alcohols, sterols and fatty acids) and compound specific isotope analysis of suspended particulate organic matter (SPM) and surface sediments of the Mackenzie Shelf and slope (Southeast Beaufort Sea, Arctic Ocean), were studied in summer 2009. The concentrations of the molecular lipid markers, characteristic of known organic matter sources, were grouped and used as proxies to evaluate the relative importance of fresh algal, detrital algal, fossil, C3 terrestrial plants, bacterial and zooplankton material in the sedimentary organic matter (OM). Fossil and detrital algal contributions were the major fractions of the freshwater SPM from the Mackenzie River with ~34% each of the total molecular biomarkers. Fresh algal, C3 terrestrial, bacterial and zooplanktonic components represented much lower percentages, 17, 10, 4 and < 1%, respectively. In marine SPM from the Mackenzie slope, the major contributions were fresh and detrital algal components (> 80%) with a minor contribution of fossil and C3 terrestrial biomarkers. Characterization of the sediments revealed a major sink of refractory algal material mixed with some fresh algal material, fossil hydrocarbons and a small input of C3 terrestrial sources. In particular, the sediments from the shelf and at the mouth of the Amundsen Gulf presented the highest contribution of detrital algal material (60–75%) whereas those from the slope contained the highest proportion of fossil (40%) and C3 terrestrial plant material (10%). Overall, considering that the detrital algal material is marine derived, autochthonous sources contributed more than allochthonous sources to the OM lipid pool. Using the ratio of an allochthonous biomarker (normalized to total organic carbon, TOC) found in the sediments to those measured at the river mouth water, we estimated that the fraction of terrestrial material preserved in the sediments accounted for 30–40% of the total carbon in the inner shelf sediments

The emergence of ocean acidification as a significant threat to calcifying organisms in marine ecosystems creates a pressing need to understand the physiological and molecular mechanisms by which calcification is affected by environmental parameters. We report here, for the first time, changes in gene expression induced by variations in pH/pCO2 in the widespread and abundant coccolithophore Emiliania huxleyi. Batch cultures were subjected to increased partial pressure of CO2 (pCO2; i.e. decreased pH), and the changes in expression of four functional gene classes directly or indirectly related to calcification were investigated. Increased pCO2 did not affect the calcification rate and only carbonic anhydrase transcripts exhibited a significant down-regulation. Our observation that elevated pCO2 induces only limited changes in the transcription of several transporters of calcium and bicarbonate gives new significant elements to understand cellular mechanisms underlying the early response of E. huxleyi to CO2-driven ocean acidification., The emergence of ocean acidification as a significant threat to calcifying organisms in marine ecosystems creates a pressing need to understand the physiological and molecular mechanisms by which calcification is affected by environmental parameters. We report here, for the first time, changes in gene expression induced by variations in pH/pCO2 in the widespread and abundant coccolithophore Emiliania huxleyi. Batch cultures were subjected to increased partial pressure of CO2 (pCO2; i.e. decreased pH), and the changes in expression of four functional gene classes directly or indirectly related to calcification were investigated. Increased pCO2 did not affect the calcification rate and only carbonic anhydrase transcripts exhibited a significant down-regulation. Our observation that elevated pCO2 induces only limited changes in the transcription of several transporters of calcium and bicarbonate gives new significant elements to understand cellular mechanisms underlying the early response of E. huxleyi to CO2-driven ocean acidification.

The response of Emiliania huxleyi (Lohmann) W. W. Hay et H. Mohler, Calcidiscus leptoporus (G. Murray et V. H. Blackman) J. Schiller, and Syracosphaera pulchra Lohmann to elevated partial pressure of carbon dioxide (pCO2) was investigated in batch cultures. We reported on the response of both haploid and diploid life stages of these three species. Growth rate, cell size, particulate inorganic carbon (PIC), and particulate organic carbon (POC) of both life stages were measured at two different pCO2 (400 and 760 parts per million [ppm]), and their organic and inorganic carbon production were calculated. The two life stages within the same species generally exhibited a similar response to elevated pCO2, the response of the haploid stage being often more pronounced than that of the diploid stage. The growth rate was consistently higher at elevated pCO2, but the response of other processes varied among species. Calcification rate of C. leptoporus and of S. pulchra did not change at elevated pCO2, whereas it increased in E. huxleyi. POC production and cell size of both life stages of S. pulchra and of the haploid stage of E. huxleyi markedly decreased at elevated pCO2. It remained unaltered in the diploid stage of E. huxleyi and C. leptoporus and increased in the haploid stage of the latter. The PIC:POC ratio increased in E. huxleyi and was constant in C. leptoporus and S. pulchra. Elevated pCO2 has a significant effect on these three coccolithophore species, the haploid stage being more sensitive. This effect must be taken into account when predicting the fate of coccolithophores in the future ocean., The response of Emiliania huxleyi (Lohmann) W. W. Hay et H. Mohler, Calcidiscus leptoporus (G. Murray et V. H. Blackman) J. Schiller, and Syracosphaera pulchra Lohmann to elevated partial pressure of carbon dioxide (pCO2) was investigated in batch cultures. We reported on the response of both haploid and diploid life stages of these three species. Growth rate, cell size, particulate inorganic carbon (PIC), and particulate organic carbon (POC) of both life stages were measured at two different pCO2 (400 and 760 parts per million [ppm]), and their organic and inorganic carbon production were calculated. The two life stages within the same species generally exhibited a similar response to elevated pCO2, the response of the haploid stage being often more pronounced than that of the diploid stage. The growth rate was consistently higher at elevated pCO2, but the response of other processes varied among species. Calcification rate of C. leptoporus and of S. pulchra did not change at elevated pCO2, whereas it increased in E. huxleyi. POC production and cell size of both life stages of S. pulchra and of the haploid stage of E. huxleyi markedly decreased at elevated pCO2. It remained unaltered in the diploid stage of E. huxleyi and C. leptoporus and increased in the haploid stage of the latter. The PIC:POC ratio increased in E. huxleyi and was constant in C. leptoporus and S. pulchra. Elevated pCO2 has a significant effect on these three coccolithophore species, the haploid stage being more sensitive. This effect must be taken into account when predicting the fate of coccolithophores in the future ocean.

Growth rate, C:N ratio, phospholipid-derived fatty acids (PLFA) distribution and organic carbon isotope composition, Growth rate, C:N ratio, phospholipid-derived fatty acids (PLFA) distribution and organic carbon isotope composition

Conservation of biological diversity, a goal institutionalised through international agreements such as the Convention on Biological Diversity, or the Habitats Directive in Europe, has important implications for the management of all natural resources, as it requires a re-arrangement of management practices in a multi-faceted fashion. While protected areas may contribute to well-being globally, they might thus impose disproportionately high costs at the local level. This led us to evaluate processes and outcomes of policies governing biological diversity not only in terms of how just, but in terms of how fair they are. With a particular focus on the conflicts arising among different groups of stakeholders when establishing protected areas for biodiversity conservation we shed light on the role fairness plays for effective management which we introduce as problem of complex social dilemma. The framework developed is applied in the discussion of two case studies: the first one considers biodiversity protection in the Alpine region Verwall, Austria, highlighting the importance of procedural fairness; the second case study considers the Small Island of Braila, Romania, highlighting the relevance of distributional fairness. We conclude that if biodiversity conservation is hampered by complex social dilemma the success of partnership approaches may critically depend not only on aspects of distributional and procedural fairness but also on trust in effective monitoring and sanctioning systems that counteract the underlying social dilemma and support a fair sharing of costs and benefits that meets the subjective fairness appraisals of the partners. &nbsp