DEVELOPMENT OF A GEOREFERENCED ARCHAEOLOGICAL INFORMATION DATA BASE FOR ELEUTHERNA IN CRETE

[EN] Cultural Heritage Information Management Systems (CHIMS) have been developed in order to achieve the georeference of the items in the Cultural Heritage database. Eleutherna in Crete is one of the most significant archaeological sites in Greece, with. many buildings constructed and destroyed during its long life. Hence, it is easily understandable that this vast archaeological site is complicated and difficult to understand, even by experts. In this paper the development of a Cultural Heritage Management System, called ARCHAEOsystem, is described and analyzed. The system has as geographic base a recent orthophoto of the area and for its design several parameters were taken into account. The conceptual design of the data base with the Entity-Relational (E-R) model preceded the development of this object oriented system. This E-R model is described and evaluated for its operability. After the initial experimental operation of the system, eventual practical problems will be identified and confronted. Finally, presentation of future prospects is being attempted and eventual uses of such a system are proposed.Tapinaki, SI.; Georgopoulos, A.; Ioannidis, C.; Frentzos, E.; Stampolidis, N.; Maragoudakis, N. (2016). DEVELOPMENT OF A GEOREFERENCED ARCHAEOLOGICAL INFORMATION DATA BASE FOR ELEUTHERNA IN CRETE. En 8th International congress on archaeology, computer graphics, cultural heritage and innovation. Editorial Universitat Politècnica de València. 333-336. https://doi.org/10.4995/arqueologica8.2015.3558OCS33333

Cabbage and fermented vegetables : From death rate heterogeneity in countries to candidates for mitigation strategies of severe COVID-19

Large differences in COVID-19 death rates exist between countries and between regions of the same country. Some very low death rate countries such as Eastern Asia, Central Europe, or the Balkans have a common feature of eating large quantities of fermented foods. Although biases exist when examining ecological studies, fermented vegetables or cabbage have been associated with low death rates in European countries. SARS-CoV-2 binds to its receptor, the angiotensin-converting enzyme 2 (ACE2). As a result of SARS-CoV-2 binding, ACE2 downregulation enhances the angiotensin II receptor type 1 (AT(1)R) axis associated with oxidative stress. This leads to insulin resistance as well as lung and endothelial damage, two severe outcomes of COVID-19. The nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is the most potent antioxidant in humans and can block in particular the AT(1)R axis. Cabbage contains precursors of sulforaphane, the most active natural activator of Nrf2. Fermented vegetables contain many lactobacilli, which are also potent Nrf2 activators. Three examples are: kimchi in Korea, westernized foods, and the slum paradox. It is proposed that fermented cabbage is a proof-of-concept of dietary manipulations that may enhance Nrf2-associated antioxidant effects, helpful in mitigating COVID-19 severity.Peer reviewe

Nrf2-interacting nutrients and COVID-19 : time for research to develop adaptation strategies

There are large between- and within-country variations in COVID-19 death rates. Some very low death rate settings such as Eastern Asia, Central Europe, the Balkans and Africa have a common feature of eating large quantities of fermented foods whose intake is associated with the activation of the Nrf2 (Nuclear factor (erythroid-derived 2)-like 2) anti-oxidant transcription factor. There are many Nrf2-interacting nutrients (berberine, curcumin, epigallocatechin gallate, genistein, quercetin, resveratrol, sulforaphane) that all act similarly to reduce insulin resistance, endothelial damage, lung injury and cytokine storm. They also act on the same mechanisms (mTOR: Mammalian target of rapamycin, PPAR gamma:Peroxisome proliferator-activated receptor, NF kappa B: Nuclear factor kappa B, ERK: Extracellular signal-regulated kinases and eIF2 alpha:Elongation initiation factor 2 alpha). They may as a result be important in mitigating the severity of COVID-19, acting through the endoplasmic reticulum stress or ACE-Angiotensin-II-AT(1)R axis (AT(1)R) pathway. Many Nrf2-interacting nutrients are also interacting with TRPA1 and/or TRPV1. Interestingly, geographical areas with very low COVID-19 mortality are those with the lowest prevalence of obesity (Sub-Saharan Africa and Asia). It is tempting to propose that Nrf2-interacting foods and nutrients can re-balance insulin resistance and have a significant effect on COVID-19 severity. It is therefore possible that the intake of these foods may restore an optimal natural balance for the Nrf2 pathway and may be of interest in the mitigation of COVID-19 severity

User Modeling and Plan Recognition under Conditions of Uncertainty

Abstract. For the present paper, we endeavor with the issue of identification of a user’s plan, in terms of user modeling under uncertainty. Unlike the majority of existing natural language understanding engines, the presented framework automatically encodes semantic representation of a user’s query using a Bayesian networks framework. The structure of the networks is determined from annotated dialogue corpora, thus eliminating the monotonous and costly process of manually coding domain knowledge. The conditional probability tables are computed accurately from the available data, obtained from the same set of dialogue acts. In order to cope with words absent from our restricted dialogue corpus, we have incorporated a separate, offline module, which estimates their semantic role from both medical and general raw text corpora, correlating them with known lexical-semantically similar words or predefined topics. Lexical similarity is identified on the basis of both contextual similarity and co-occurrence in conjunctive expressions, while extraction of word–topic correlations is possible due to the labeled nature of the available medical corpus, obtained from the Hellenic National Organization for Medicines. The evaluation of the platform was performed against an existing language understanding module of the DIKTIS medical system, the architecture of which is based on manually embedded domain knowledge. Obtained results depict noteworthy improvement in the context of efficiently identifying the core goals of a user. The presented approach demonstrates a 24% recognition improvement using the automatic domain knowledge extraction engine, augmented with the unknown terms resolving component

Combining bayesian and support vector machines learning to automatically complete syntactical information for HPSG-like formalisms

Learning Bayesian Belief Networks (BBN) from corpora and incorporating the extracted inferring knowledge with a Support Vector Machines (SVM) classifier has been applied to the automatic acquisition of verb subcategorization frames for Modern Greek. We have made use of minimal linguistic resources, such as basic morphological tagging and phrase chunking, to demonstrate that verb subcategorization, which is of great significance for developing robust natural language human computer interaction systems, could be achieved using large corpora, without having any general-purpose syntactic parser at all. Moreover, by taking advantage of the plethora in unlabeled data found in text corpora in addition to some available labeled examples, we overcome the expensive task of annotating the whole set of training data and the performance of the subcategorization frames learner is increased. We argue that a classifier generated from BBN and SVM is well suited for learning to identify verb subcategorization frames. Empirical results will support this claim. Performance has been methodically evaluated using two different corpora, one balanced and one domain-specific in order to determine the unbiased behavior of the trained models. Limited training data are proved to endow with satisfactory results. We have been able to achieve precision exceeding 90 % on the identification of subcategorization frames which were not known beforehand. The obtained valid frames have been used to fill out the subcategorization field of verb entries in an HPSG-like lexicon using the LKB grammar development environment