The Quality Control of Puerariae Lobatae Radix and Puerariae Thomsonii Radix

Puerariae Lobatae Radix (PLR) and Puerariae Thomsonii Radix (PTR) are traditional Chinese medicines used interchangeably in clinical practice, even though they possess significantly different chemical profiles. The aim of this thesis was to differentiate PLR from PTR using various analytical instruments coupled with chemometrics. Morphological results illustrate PLR possessed distinct macroscopic and microscopic features as compared to PTR. UPLC results reveal isoflavonoids were the major chemical constituents in both species, with the content of puerarin in PLR significantly greater than in PTR. PLS-DA models demonstrate both UPLC and HPTLC chromatographic fingerprints were effective in differentiating PLR from PTR. PLSR coupled with Raman spectra was able to predict the TPC and antioxidant capacities of PLR and PTR. The pharmacological results illustrate PLR possessed significantly greater anti-diabetic, cytoprotective and anti-cancer activities as compared to PTR. In summary, the results reveal the chemical fingerprints coupled with chemometrics was effective in differentiating PLR from PTR, and PLR was morphologically, chemically and pharmacologically different from PTR. This thesis provided further insight into the comprehensive nature of the quality control of two similar species and recommends changes to their descriptions in the pharmacopoeias. This will ultimately improve the quality, safety and efficacy of herbal products

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Painolliset äärellistilaiset menetelmät oikaisulukuun

This dissertation is a large-scale study of spell-checking and correction using finite-state technology. Finite-state spell-checking is a key method for handling morphologically complex languages in a computationally efficient manner. This dissertation discusses the technological and practical considerations that are required for finite-state spell-checkers to be at the same level as state-of-the-art non-finite-state spell-checkers. Three aspects of spell-checking are considered in the thesis: modelling of correctly written words and word-forms with finite-state language models, applying statistical information to finite-state language models with a specific focus on morphologically complex languages, and modelling misspellings and typing errors using finite-state automata-based error models. The usability of finite-state spell-checkers as a viable alternative to traditional non-finite-state solutions is demonstrated in a large-scale evaluation of spell-checking speed and the quality using languages with morphologically different natures. The selected languages display a full range of typological complexity, from isolating English to polysynthetic Greenlandic with agglutinative Finnish and the Saami languages somewhere in between.Tässä väitöskirjassa tutkin äärellistilaisten menetelmien käyttöä oikaisuluvussa. Äärellistilaiset menetelmät mahdollistavat sananmuodostukseltaan monimutkaisempien kielten, kuten suomen tai grönlannin, sanaston sujuvan käsittelyn oikaisulukusovelluksissa. Käsittelen tutkielmassani tieteellisiä ja käytännöllisiä toteutuksia, jotka ovat tarpeen, jotta tällaisia sananmuodostukseltaan monimutkallisempia kieliä voisi käsitellä oikaisuluvussa yhtä tehokkaasti kuin yksinkertaisempia kieliä, kuten englantia tai muita indo-eurooppalaisia kieliä nyt käsitellään. Tutkielmassa esitellään kolme keskeistä tutkimusongelmaa, jotka koskevat oikaisuluvun toteuttamista sanarakenteeltaan monimutkaisemmille kielille: miten mallintaa oikeinkirjoitetut sanamuodot äärellistilaisin mallein, miten soveltaa tilastollista mallinnusta monimutkaisiin sanarakenteisiin kuten yhdyssanoihin, ja miten mallintaa kirjoitusvirheitä äärellistilaisin mentelmin. Tutkielman tuloksena esitän äärellistilaisia oikaisulukumenetelmiä soveltuvana vaihtoehtona nykyisille oikaisulukimille, tämän todisteena esitän mittaustuloksia, jotka näyttävät, että käyttämäni menetelmät toimivat niin rakenteellisesti yksinkertaisille kielille kuten englannille yhtä hyvin kuin nykyiset menetelmät että rakenteellisesti monimutkaisemmille kielille kuten suomelle, saamelle ja jopa grönlannille riittävän hyvin tullakseen käytetyksi tyypillisissä oikaisulukimissa

TA-COS 2016 : First workshop on text analytics for cybersecurity and online safety : Proceedings

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Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Lexical innovation on the web and social media

This dissertation investigates the emergence and diffusion of English neologisms on the web and social media, employing a data-driven methodology to identify a substantial sample of 851 neologisms. Neologisms are examined from their coining to successful dissemination within the community, with the study revealing a wide spectrum of degrees of diffusion. The exploration extends to studying the usage and diffusion of selected neologisms on the web and on Twitter, with a particular focus on social dynamics and variation among different speaker groups. Moreover, the dissertation probes into semantic innovation, demonstrating substantial socio-semantic variation and polarized public discourse surrounding certain neologisms. The research conducts an extensive analysis of semantic innovation and socio-semantic variation, elucidating significant socio-semantic discrepancies between various communities. The dissertation sheds light on the social and semantic dynamics underpinning the life cycle of neologisms within a linguistically diverse community

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

Study of Machine Learning Methods in Intelligent Transportation Systems

Machine learning and data mining are currently hot topics of research and are applied in database, artificial intelligence, statistics, and so on to discover valuable knowledge and the patterns in big data available to users. Data mining is predominantly about processing unstructured data and extracting meaningful information from them for end users to help take business decisions. Machine learning techniques use mathematical algorithms to find a pattern or extract meaning out from big data. The popularity of such techniques in analyzing business problems has been enhanced by the arrival of big data. The main objective of this thesis is to study the importance of big data and machine learning and their impact on transportation industry. This thesis is primarily a review of the important machine learning algorithms and their applications in the field of big data. The author has tried to showcase the need to extract meaningful information from the vast amount of big data in the form of traffic data available in today’s world and also listed different machine learning techniques that can be used to extract this knowledge required in order to facilitate better decision making for transportation applications. The analysis is done by using five different multivariate analysis and machine learning techniques in data mining namely cluster analysis, multivariate linear regression, hierarchical multiple regression, factor analysis and discriminant analysis in two different software packages namely SPSS and R. As part of the analysis, the author has tried to explain how knowledge extracted from random traffic data containing variables such as age of the driver, sex of the driver, the day of the week, atmospheric condition and blood alcohol content of the driver can play an important role in predicting the traffic crash. The data taken into account is accident data, which was obtained from Fatality Analysis Reporting System (FARS) ranging from the year 1999 to 2009. It is concluded that traffic accidents were mostly impacted by the atmospheric conditions, blood alcohol content followed by the day of the week

Development of On-Tissue Mass Spectrometric Strategies for Protein Identification, Quantification and Mapping

Résumé : L’imagerie par spectrométrie de masse est une technique sans marquage permettant la détection et la localisation de protéines à partir de coupes de tissus. Afin de répondre à des problématiques biologiques, le nombre de protéines identifiées doit être amélioré. Une stratégie consiste à réaliser une micro-jonction liquide sur des régions particulières des coupes de tissus afin d’extraire les peptides issus de la digestion in situ des protéines. Plus de 1500 protéines ont identifié sur une zone de 650µm, correspondant à environ 1900 cellules. Une corrélation entre ces données avec celles générées par MSI a augmenté le nombre de protéines localisées. Afin d’obtenir dans le même temps, la localisation et l’identification de protéines, une méthode consiste à réaliser la microdissection de l’ensemble de la coupe après l’avoir déposée sur une lame recouverte de prafilm. Parafilm-Assisted Microdissection (PAM) a également été appliquée à l’étude de l'expression différentielle de protéines dans des tumeurs de prostate. Les résultats identifiés glutamate oxaloacétate transférase 2 (GOT2) en tant que biomarqueur de protéine candidate impliquée dans le métabolisme du glucose, en plus de celles qui ont déjà été indiqué précédemment. Réunis ensemble, ces méthodes MS d'analyses directes fournissent un moyen robuste d’étude de protéines dans leur état natif afin de fournir des indications sur leur rôle dans des systèmes biologiques. // Abstract : Mass spectrometry-based methods for direct tissue analysis, such as MS imaging, are label-free techniques that permit the detection and localization of proteins on tissue sections. There is a need to improve the number of protein identifications in these techniques for them to comprehensively address biological questions. One strategy to obtain high protein IDs is to realize liquid microjunction on localized regions of tissue sections to extract peptides from the in situ digestion of proteins. More than 1500 proteins were identified in a 650μm spot, corresponding to about 1900 cells. Matching these IDs with those from MSI increased the number of localized proteins. In order to achieve simultaneous identification and localization of proteins, a method consisting of microdissecting entire tissue sections mounted on parafilmcovered slides was developed. Spectral counting was then used to quantify identified proteins, and the values were used to generate images. Parafilm-Assisted Microdissection (PAM) was also used to examine the differential expression of proteins on prostate tumors. Results identified glutamate oxaloacetate transferase 2 (GOT2) as a candidate protein biomarker involved in glucose metabolism, in addition to those that have already been reported previously. Taken together, these direct MS analysis methods provide a robust means of analyzing proteins in their native state and are expected to provide insights to their role in biological systems