391 research outputs found

    Azathioprine-Induced Peripheral T Cell Apoptosis And Drug Response In Patients With Crohn’s Disease

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    Background and Aim: the long time interval for a trial of thioupurine therapy and the potential side effects in spite of the proven efficacy, do not encourage their use as early therapeutic option in Crohn’s Disease (CD). The development of tests predictive of responsiveness represents a major attempt in the clinical management of CD patients. Azathioprine (AZA) is able to induce apoptosis of T cells; therefore we analyzed the “in vitro” thiopurine-induced T cells apoptosis in a group of CD patients with known response to a previous treatment with AZA. Methods: peripheral CD4+ T cells from 16 CD patients were stimulated with antiCD3/CD28 mAbs in the presence or absence of AZA or 6-MP or 6-thioguanine; apoptosis was assessed using Annexin V staining. Results: Apoptosis stimulation index (% of apoptotic cells in the presence of thiopurine / % of apoptotic cells in their absence) was significantly lower in non responder when compared to responder patients (1.46 (0.97-1.8) vs. 2.19 (1.58-2.65) median (range), respectively; p=0.002 by Mann Whitney test). Conclusions: evaluation of apoptosis stimulation index of peripheral CD4+T cell induced by AZA might represent a parameter useful for a proper selection of CD patients candidate to thiopurine treatment

    Towards Real-World Data Streams for Deep Continual Learning

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    Continual Learning deals with Artificial Intelligent agents striving to learn from an ever-ending stream of data. Recently, Deep Continual Learning focused on the design of new strategies to endow Artificial Neural Networks with the ability to learn continuously without forgetting previous knowledge. In fact, the learning process of any Artificial Neural Network model is well-known to lack the sufficient stability to preserve existing knowledge when learning new information. This phenomenon, called catastrophic forgetting or simply forgetting, is considered one of the main obstacles for the design of effective Continual Learning agents. However, existing strategies designed to mitigate forgetting have been evaluated on a restricted set of Continual Learning scenarios. The most used one is, by far, the Class-Incremental scenario applied on object detection tasks. Even though it drove interest in Continual Learning, Class-Incremental scenarios strongly constraint the properties of the data stream, thus limiting its ability to model real-world environments. The core of this thesis concerns the introduction of three Continual Learning data streams, whose design is centered around specific real-world environments properties. First, we propose the Class- Incremental with Repetition scenario, which builds a data stream including both the introduction of new concepts and the repetition of previous ones. Repetition is naturally present in many environments and it constitutes an important source of information. Second, we formalize the Continual Pre-Training scenario, which leverages a data stream of unstructured knowledge to keep a pre-trained model updated over time. One important objective of this scenario is to study how to continuously build general, robust representations that does not strongly depend on the specific task to be solved. This is a fundamental property of real-world agents, which build cross-task knowledge and then adapts it to specific needs. Third, we study Continual Learning scenarios where data streams are composed by temporally-correlated data. Temporal correlation is ubiquitous and lies at the foundation of most environments we, as humans, experience during our life. We leverage Recurrent Neural Networks as our main model, due to their intrinsic ability to model temporal correlations. We discovered that, when applied to recurrent models, Continual Learning strategies behave in an unexpected manner. This highlights the limits of the current experimental validation, mostly focused on Computer Vision tasks. Ultimately, the introduction of new data streams contributed to deepen our understanding of how Artificial Neural Networks learn continuously. We discover that forgetting strongly depends on the properties of the data stream and we observed large changes from one data stream to another. Moreover, when forgetting is mild, we were able to effectively mitigate it with simple strategies, or even without any specific ones. Loosening the focus on forgetting allows us to turn our attention to other interesting problems, outlined in this thesis, like (i) separation between continual representation learning and quick adaptation to novel tasks, (ii) robustness to unbalanced data streams and (iii) ability to continuously learn temporal correlations. These objectives currently defy existing strategies and will likely represent the next challenge for Continual Learning research

    Lo Stato trofico dello stagno di Platamona

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    Seasonal changes of nutrient concentrations and other chemical and biological parameters in Platamona's pond were investigated to asses its trophic level. The pond shows eutrophic. There is a large range of seasonal variations of totale alcalinity concentrations (3.3 - 6.9 meq l-1). The littoral and submersed macrophytes are very plentiful and the mean annual value of clorophyll a (16 mg m-3) is high. Nevertheless the mean of concentrations of nitrate nitrogen (106 mg m-3), ammonia nitrogen (30 mg m-3) and reactive phosphorus (4 m-3) are low

    Variations of biofouling communities in an off-shore fish cage farm from North-Western Sardinia = Variazioni del biofouling in un allevamento ittico in gabbie off-shore della Sardegna nord-occidentale

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    Biofouling variations were studied in a fish farming facility near Alghero (Italy) between November 2007 and November 2008. Net panels suitable for the settlement of encrusting organisms were immersed in cages in which large and small gilthead seabream specimens were reared. Significant differences in biofouling biomass and coverage were observed between cages containing fish and controls. The results obtained revealed that gilthead seabream can exert a crucial role in controlling biofouling growth, independently from its size

    <i>Posidonia oceanica</i> (L.) Delile in Sardinia: knowledges and perspectives

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    A summary of the research regarding Posidonia oceanica in Sardinia is presented; this consists of a series of studies regarding the phenology of the plant and some considerations on the conservation of the prairies. Finally, the Authors describe a proposition for the protection of the plant in Sardinia

    Fitoplancton, nutrienti algali e stato trofico del lago Bunnari (Sardegna settentrionale)

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    Seasonal changes of nutrient concentrations, density and composition of phytoplankton and chlorophyll a contents in Bunnari lake were investigated to assess limnological conditions and trophyc status. Only 19 phytoplanktonic species are identified and only 2 species, during the seasonal succession, show high density and biomass; however seasonal succession is altered by a Cupper treatment with purpose of algal control. The 100% dominance of some species allow a good evaluation of chlorophyll aphytoplankton volume ratios. Since many of species found in the lake are typical of productive environments and the values of phytoplankton density and biomass, chlorophyll a and nutrient concentrations are very high, it has been argued a high eutrophic level of the lake

    Sulla presenza di <i>Caulerpa racemosa</i> (Forsskål) J. Agardh in Sardegna

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    Nel presente contributo viene segnalato un nuovo sito di rinvenimento di Caulerpa racemosa, localizzato a sud della Sardegna nel Golfo di Cagliari, nelle adiacenze dell'area industriale di Sarroch. È significativa la presenza dell'alga in prossimità del porto industriale dove attraccano numerose petroliere provenienti dal Golfo Persico via Suez. È possibile quindi che data la grande resistenza dell'alga, frammenti del tallo possano essere rimasti impigliati nell'ancora. L'area marina risulta particolarmente adatta all'insediamento dell'alga perché risente della pressione antropica della vicina zona industriale, dell'elevata densità insediativa e della presenza di stagni e lagune di ampie dimensioni nelle immediate vicinanze. Il fondale da una prima prospezione è ricoperto da matte morta di notevoli proporzioni con radi ciuffi di Posidonia e con vaste zone di accumulo di materia organica particellata. Il sito presenta un' elevata torbidità dovuta al tipo di sedimento e bassi fondali. Il presente contributo analizza inoltre l'espansione di Caulerpa racemosa in Mediterraneo, riportando alcune considerazioni biogeografiche

    Interventi comuni per la difesa dell'ambiente marino quale elemento unificante del parco transfrontaliero

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    Si riportano alcune esperienze di ricerca ambientale e gestionale propedeutiche alla creazione del Parco Internazionale delle Bocche di Bonifacio tra i ricercatori del Parco Nazionale dell'Arcipelago di La Maddalena e del Parco Regionale della Corsica

    Produttività primaria del Lago di Baratz

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    Il presente lavoro si inquadra in una più vasta ricerca di carattere paleolimnologico, limnologico, floristico e fitogeografico dell'unico lago naturale della Sardegna. In particolare, le ricerche riguardano l'attuazione del metodo che in ambiente acquatico permette la stima della produttività primaria in situ e in generale definisce il metabolismo della comunità dell'ambiente lacustre

    Continual Learning with Gated Incremental Memories for sequential data processing

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    The ability to learn in dynamic, nonstationary environments without forgetting previous knowledge, also known as Continual Learning (CL), is a key enabler for scalable and trustworthy deployments of adaptive solutions. While the importance of continual learning is largely acknowledged in machine vision and reinforcement learning problems, this is mostly under-documented for sequence processing tasks. This work proposes a Recurrent Neural Network (RNN) model for CL that is able to deal with concept drift in input distribution without forgetting previously acquired knowledge. We also implement and test a popular CL approach, Elastic Weight Consolidation (EWC), on top of two different types of RNNs. Finally, we compare the performances of our enhanced architecture against EWC and RNNs on a set of standard CL benchmarks, adapted to the sequential data processing scenario. Results show the superior performance of our architecture and highlight the need for special solutions designed to address CL in RNNs.Comment: Accepted as a conference paper at 2020 International Joint Conference on Neural Networks (IJCNN 2020). Part of 2020 IEEE World Congress on Computational Intelligence (IEEE WCCI 2020
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