University of Szeged
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«Noi cercavamo una lingua: al buona, per verità, non avevamo pensato» Il Manzoni linguista: la teoria sulla formazione dell’Italiano, vincolata ai concetti di Dialetto - Oralità - Tosco-fiorentino - Uso
Full text linkAlessandro Manzoni è una delle personalità più importanti nella storia e nelle cultura linguistica italiana. Tuttavia, non risulta sufficientemente noto il suo ruolo da linguista ante litteram.
Egli arrivò a riconoscere ed acclarare la vera essenza della lingua: l’oralità a base sociale; e dimostrò come quello che tutti i sistemi consideravano Lingua, cioè il toscano letterario, era scientificamente e inevitabilmente compromesso con gli elementi concreti di oralità-dialetti-uso.
Di contro alle teorie contemporanee più accreditate, che ignorarono il dato sociale, come quella di Antonio Cesari e il purismo, l’eredità illuministica di M. Cesarotti, raccolta da classicisti come Vincenzo Monti, e i grammatici come Michele Ponza, attesterà non solo che la lingua della conversazione quotidiana, della stragrande maggioranza della popolazione, fosse regolarmente un dialetto; ma sottolineerà, come un’arma vincente, che nonostante questo apparente divario, esistesse un ‘parlare’ sovraregionale e comune.
Per questo motivo il dialetto diventa l’elemento principale, la novità, della sua e della nostra trattazione, in seno all’Italiano (dialettal-popolare, cioè, comune). Il Manzoni anticipa le teorie che hanno costituito la storia della lingua e della linguistica moderna, e cioè che l’acquisizione di un unico idioma non sarebbe mai avvenuta senza adattamenti, e l’italiano è il frutto di un compromesso con l’elemento territoriale. Così, ripercorrendo i suoi Scritti linguistici e focalizzando l’attenzione su questi elementi basilari, speriamo che questa raccolta di dati sia un altro contributo che concorra nell’obiettivo di assegnare al Manzoni i suoi meriti, e rinvigorire il pensiero che vuole vederlo citato nel novero dei linguisti
Az asszembliszóma azonosítása, mint fázisszeparált riboszóma-naszcens fehérje komplex, valamint működésének vizsgálata
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Connections Between the Landscape Structure-related Regulation Ecosystem Services and Air Quality (Pm10) on a European Scale
Full text linkÉletkorfüggő morfo-elektrofiziológiai különbségek és a dendritikus Ca2+-dinamika vizsgálata emberi agykérgi piramissejtekben
Full text linkL1 ORF1p Is an Early Diagnostic Marker of Cancer and Its Precursor Syndromes
Full text linkCancer is currently the second leading cause of death in the European Union, following cardiovascular diseases. In recent years, its incidence has risen, largely due to an aging population, which places a significant burden on healthcare systems and society. Several studies have shown that the development of cancer is directly linked to the activity of L1 (L1) elements, the only currently active mobile genetic elements in the human genome, which make up about 17% of it. While most L1 elements are inactive due to mutations, some remain active and may contribute to genomic instability, influence gene expression, and potentially lead to diseases, including cancer. This thesis investigates the expression pattern of the ORF1p protein, encoded by L1, in various human cancers and explores its potential as a diagnostic biomarker. Using immunohistochemistry on tissue microarrays, the study analyzed 590 samples from 21 different tumor types, as well as samples from cervical intraepithelial neoplasia (CIN) and normal tissues. The results revealed that L1-ORF1p is minimally expressed in most normal somatic tissues but is frequently expressed in a wide range of cancers, including cervical cancer, non-small cell lung cancer, esophageal cancer, and basal cell carcinoma of the skin. Elevated L1-ORF1p expression was notably associated with higher clinical stages and histological grades, indicating a link to cancer progression. Moreover, intratumoral heterogeneity in L1-ORF1p expression was observed, with higher expression in less differentiated tumor areas. The study found a significant correlation between L1-ORF1p overexpression and mutations in tumor suppressor genes, particularly TP53. Tumors with mutated TP53 exhibited higher levels of L1-ORF1p than those with wild-type TP53, suggesting that the loss of function of TP53 may promote L1 activation and contribute to oncogenesis. This relation was confirmed by our experiments in tissue culture, where we found that silencing endogenous TP53 with artificial microRNAs (amiRs) increased the number of L1 retrotransposition events. The findings suggest that L1-ORF1p is a novel biomarker for both cancer diagnosis and prognosis. Its expression patterns could help distinguish between low and high-grade lesions, potentially improving diagnostic accuracy and patient management. Additionally, targeting L1-ORF1p could offer new therapeutic options in cancer treatment. The study highlights the importance of further research to elucidate the mechanisms through which L1 activity contributes to tumorigenesis and to explore its potential clinical applications
Inhalable ketoprofen nanocrystal-based dry powders for local and systemic pulmonary delivery: A patient-centric approach
Full text linkAlgoritmikus gondolkodás vizsgálata és fejlesztése
Full text linkThis dissertation focuses on developing a test to assess the algorithmic thinking skills of first-year students in an English-language BSc program, analysing its effectiveness, and designing and evaluating a related development course. The study is particularly relevant as some students entering the English-language BSc Programming program at the Faculty of Informatics, Eötvös Loránd University, demonstrate lower levels of algorithmic thinking than required for successful academic performance. Implementing a diagnostic assessment tool and targeted intervention strategies can help bridge the initial knowledge gap among students, enhancing their chances of academic success.
The dissertation first reviews key terminologies related to developments in information and communication technologies (ICT) and examines the evolution and theoretical framework of algorithmic thinking. It introduces a comprehensive model of algorithmic thinking, serving as the theoretical foundation for empirical research. Additionally, the study presents preliminary investigations into young students’ (grades 1–4) use of digital tools and the algorithmic thinking skills of students in grades 5–6, with findings that influenced the test design.
The initial version of the algorithmic thinking test was piloted alongside a self-report questionnaire measuring potential background variables (e.g., prior knowledge, language proficiency). Based on the pilot results, the test was refined and revised. To further support students’ skill development, a problem-solving and algorithmic thinking course was also created.
The study was conducted over three semesters, involving 137 BSc students. The reliability of the algorithmic thinking test improved compared to the pilot study results; however, the discrimination index of certain items suggested that some tasks were less effective in distinguishing between different skill levels. As expected, the most challenging tasks required students to develop their own algorithms, demonstrating a high level of cognitive demand. While a weak correlation was found between the English proficiency test and the algorithmic thinking test, further analysis of students' programming course results and first-semester academic performance suggests that the remedial course contributes to student progress.
Finally, the research examines the impact of the COVID-19 period and presents the improved online version of the test, along with its potential adaptation for other student groups
A theoretical and corpus linguistics study of the light verb constructions: Empirical data from Indonesian
Full text linkThis dissertation investigates Indonesian Light Verb Constructions (LVCs) through the integrated lenses of theoretical linguistics and corpus-based analysis. The primary part of the study provides an empirical foundation, drawing on four corpora to assess the frequency and distribution of LVCs. K-means clustering reveals three natural groupings (low-, medium-, and high-frequency), with Hypothesis 1 supported by significant cross-corpus rank consistency (Spearman’s rs = 0.891, p <.001), validating the temporal and genre-stable nature of LVC frequency patterns. Hypothesis 2 confirms a significant deviation from Zipfian expectations, aligning more closely with Zipf–Mandelbrot law. Additional modeling supports the Menzerath–Altmann law (morpheme-based fit), while vocabulary dynamics are elaborated through Heaps’ Law and Baayen’s productivity metrics. Lexical drift is capture diachronically via Yule’s K and KL Divergence, while entropy measures underscore shifting lexical concentration. A contrastive analysis with Altmann’s (1967) seminal dictionary-based lexical model of Indonesian reveals substantial structural divergence in morpheme density, type distribution, and correlation behavior. Hypothesis 3 is confirmed through morpho-semantic-syntactic stratification across clusters, identifying a structurally asymmetric, gradiently layered LVC system. The secondary part classifies verb elements into True Light Verbs and Vague Action Verbs using aktionsart diagnostics. Machine learning (Naïve Bayes and Random Forest) highlights frequency and semantic parameters as strong predictors of verb productivity. The final part analyzes noun components, distinguishing stative and eventive types based on temporal features. Findings indicate that stative interpretations are largely noun-driven, while eventive reading emerge from verb-noun interaction and distributional patterns. This research offers a theoretically informed, data-driven typology of Indonesian LVCs, contributing to corpus linguistics and the broader modeling of LVC systems in underdescribed languages
