Spatial distribution patterns of the non-native European catfish, Silurus glanis, from multiple online sources - a case study for the River Tagus (Iberian Peninsula)

Effective management of invasive fishes depends on the availability of updated information about their distribution and spatial dispersion. Forensic analysis was performed using online and published data on the European catfish, Silurus glanis L., a recent invader in the Tagus catchment (Iberian Peninsula). Eighty records were obtained mainly from anglers’ fora and blogs, and more recently from www.youtube.com. Since the first record in 1998, S. glanis expanded its geographic range by 700 km of river network, occurring mainly in reservoirs and in high-order reaches. Human-mediated and natural dispersal events were identified, with the former occurring during the first years of invasion and involving movements of >50 km. Downstream dispersal directionality was predominant. The analysis of online data from anglers was found to provide useful information on the distribution and dispersal patterns of this non-native fish, and is potentially applicable as a preliminary, exploratory assessment tool for other non-native fishes.

An Integrated Soft Computing Approach to Hughes Syndrome Risk Assessment

The AntiPhospholipid Syndrome (APS) is an acquired autoimmune disorder induced by high levels of antiphospholipid antibodies that cause arterial and veins thrombosis, as well as pregnancy-related complications and morbidity, as clinical manifestations. This autoimmune hypercoagulable state, usually known as Hughes syndrome, has severe consequences for the patients, being one of the main causes of thrombotic disorders and death. Therefore, it is required to be preventive; being aware of how probable is to have that kind of syndrome. Despite the updated of antiphospholipid syndrome classification, the diagnosis remains difficult to establish. Additional research on clinically relevant antibodies and standardization of their quantification are required in order to improve the antiphospholipid syndrome risk assessment. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a computational framework based on Artificial Neural Networks. The proposed model allows for improving the diagnosis, classifying properly the patients that really presented this pathology (sensitivity higher than 85%), as well as classifying the absence of APS (specificity close to 95%).