Database of spatial distribution of non indigenous species in Spanish marine waters

Research in marine Spanish waters are focused on several actions to achieve an effectively management on protected areas, with the active participation of the stakeholders and research as basic tools for decision-making. Among these actions, there is one about the knowledge and control on NIS. One of its objectives is the creation of NIS factsheets, which are going to be added to the National Marine Biodiversity Geographical System (GIS) providing complementary information about taxonomic classification, common names, taxonomic synonyms, species illustrations, identification morphological characters, habitat in the native and introduced regions, biological and ecological traits, GenBank DNA sequences, world distribution, first record and evolution in the introduced areas, likely pathways of introduction, effects in the habitats and interaction with native species, and potential management measures to apply. The database will also provide data for (1) the European online platforms, (2) the environmental assessment for the Descriptor 2 (D2-NIS) of the EU Marine Strategy Framework Directive (MSFD), as well as (3) supporting decisions made by stakeholders. It is the result of extensive collaboration among scientist, manager’s and citizen science in the Spanish North-Atlantic, South-Atlantic, Gibraltar Strait-Alboran, Levantine-Balearic and Canary Islands marine divisions, providing an updated overview of the spatial distribution of relevant extended and invasive NIS of recent and established NIS introduced by maritime transport and aquaculture pathways, as well as on cryptogenic or native species in expansion due to the climatic water warming trend

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Generating the Structure of a Fuzzy Rule under Uncertainty

The aim of this paper is to present a method for identifying the structure of a rule in a fuzzy model. For this purpose, an ATMS shall be used (Zurita 1994). An algorithm obtaining the identification of the structure will be suggested (Castro 1995). The minimal structure of the rule (with respect to the number of variables that must appear in the rule) will be found by this algorithm. Furthermore, the identification parameters shall be obtained simultaneously. The proposed method shall be applied for classification to an example. The Iris Plant Database shall be learnt for all three kinds of plants. 1 INTRODUCTION If we want to describe a system, it is necessary to know which are the inputs and the outputs of the system, and, more importantly, the relationship between them. This function, in most cases, is not easy to achieve, and in many others, it contains highly complicated mathematical relationships. So, it would be interesting, if this input-output conection could be obtained dir..

An Inductive Learning Algorithm in Fuzzy Systems

The aim of this paper is to present a method for identifying the structure of a rule in a fuzzy model. For this purpose, an ATMS shall be used. An algorithm obtaining the identification of the structure will be suggested. The minimal structure of the rule (with respect to the number of variables that must appear in the rule) will be found by this algorithm. Furthermore, the identification parameters shall be obtained simultaneously. The proposed method shall be applied for classification to an example. The Iris Plant Database shall be learnt for all three kinds of plants. Keywords: Fuzzy logic, automatic learning, environment, Truth Maintenance System. 1 Introduction If we want to describe a system, it is necessary to know which are the inputs and the outputs of the system, and, more importantly, the relationship between them. This function, in most cases, is not easy to achieve, and in many others, it contains highly complicated mathematical relationships. So, it would be interesti..

Non-Monotonic Reasoning in Multivalued and Fuzzy Logic

The aim of this paper is to provide a tool which makes possible non-monotonic reasoning in a propositional knowledge system based on multivalued logic with certainty factors and fuzzy logic. The support system which results in the non-monotony will be a Truth Maintenance System (TMS). Particularly, we will use ATMS (TMS based on assumptions) defined by De Kleer. From this ATMS we will extend its use in case we have monotonic reasoning systems based on [0,1] valued logic and fuzzy logic. The latter case will be designed to reason with fuzzy truth values, although a parallel approach can be made by using directly linguistic labels. Keywords: Truth Maintenance System, Fuzzy Logic, Multivalued Logic, Knowledge Base System, Inconsistency. 1 Introduction Knowledge systems are used with ever greater efficacy in a great deal of scientific and social activities. Not long ago, was measured this efficacy according to the quality of knowledge representation and to the correct working of the reas..

A Heuristic in Rules Based Systems for Searching of Inconsistencies

The aim of this work is to present heuristic algorithms in order to determine possible inconsistences of a knowledge base system (KBS). These algorithms will be based on the measure of an associated probability to the set of P complex formulations (set of single logic propositions)[3]. Results will be assessed and further remarks concerning the valuation of the KBS will be considered. Keywords: Knowledge Based Systems, Validation, Inconsistency, Propositional Logic, Probabilistic Logic. 1 Introduction Let us consider a finite set of simple bivaluated propositions: P = fp 1 ; : : : ; p n g: A probabilistic structure can be defined on the set of complex propositions of P [11]. As is well known [1], the set CP of all complex propositions from P is, except logical equivalencies, the free Boolean algebra over P . Let V be the set of all valuations over P : V = fv : P ! f0; 1gg: Therefore, there will be 2 n different valuations over P , jV j = 2 n . The subset V r ` V of all valuati..