Analysis of Impairements in T Cells Subsets in First Degree Relatives of Patients with Type 1 Diabetes as Risk Markers for Disease Development

Prethodna istraživanja su ukazala na značajnu ulogu subpopulacija T limfocita, T helper 1 (Th1) i T helper 2 (Th2), fenotipski definisanih ekspresijom odgovarajućih hemokinskih receptora CXCR3 i CCR4, kao i T regulatorne (T reg) subpopulacije, u inicijalnoj fazi tipa 1 dijabetesa (T1D). Meñutim, povezanost promena u nivou CXCR3+ (Th1 asociranih), CCR4+ (Th2 asociranih) i CD25high (T reg asociranih) subpopulacija T memorijskih limfocita kao i citokina, njihovih liganda/medijatora funkcije, interferon-γ inducibilnog hemokina 10 (interferon-γ inducibile chemokine-IP-10) (Th1 asociranog), timusom i aktivacijom regulisanog hemokina (thymus and activation-regulated chemokine-TARC) (Th2 asociranog) i transformišućeg faktora rasta β (transforming growth factor β-TGFβ) (Treg asociranog), i rizika za ispoljavanje T1D, nije još uvek razjašnjena. Istovremeno, povezanost promena ovih imunoloških parametara i metaboličkih parametara u smislu nivoa insulinske sekrecije i insulinske senzitivnosti u ranim fazama razvoja T1D, nije do sada detaljnije analizirana. U tom smislu, cilj istraživanja je poreñenje nivoa (a) CXCR3+, CCR4+ i CD25high subpopulacija memorijskih T limfocita (b) hemokina/citokina IP-10, TARC, TGFβ i (c) insulinske sekrecije i insulinske senzitivnosti, izmeñu grupa zdravih prvih roñaka pacijenata sa T1D (PR), sa visokim i niskim rizikom za ispoljavanje bolesti (gde je rizik za T1D definisan prisustvom/odsustvom antitela na glutamat dekarboksilazu (GAD) i tirozin fosfatazu (IA-2), pacijenata sa novootkrivenim T1D (NT1D) u insulin zavisnom stanju na početku bolesti i u stanju kliničke remisije (KR), i kontrolnih ispitanika. Uključili smo PR sa visokim rizikom (GADA+, IA-2+) (N=17), PR sa niskim rizikom (GADA-, IA-2-) (N=34), pacijente sa NT1D (N=24), pacijente sa T1D u KR (N=10) i kontrolne ispitanike (N=18). Nivo CXCR3+, CCR4+ i CD25high T memorijskih limfocita je analiziran četvorobojnom imunofluoroscencijom i protočnom citometrijom. Nivo hemokina/citokina i antitela u serumu odreñeni su ELISA metodom. Prva faza insulinske sekrecije odreñivana je zbirom nivoa insulina u 1. i 3. minutu nakon IVGTT-a. Nivo insulinske senzitivnosti evaluiran je hiperinsulinemijskim euglikemijskim klampom. Rezultati ukazuju da je u PR sa visokim rizikom utvrñen povišen nivo CXCR3+ Th1 subpopulacije i IP-10 hemokina, i snižen nivo CCR4+ Th2 i CD25high T reg subpopulacije limfocita, što bi moglo sugerisati da je u PR rizik za ispoljavanje T1D povezan sa pojačanom aktivnošću Th1 i smanjenom aktivnošću Th2 i T regulatornog imunskog odgovora. Sa druge strane, početak T1D je povezan sa značajnim smanjenjem nivoa CXCR3+ i CCR4+ subsetova T memorijskih limfocita i TGFβ, i porastom nivoa IP-10 i TARC, reflektujući njihovu akumulaciju u pankreasna ostrvca i funkcionalno iscrpljivanje regulatorne subpopulacije T limfocita, što bi moglo ukazati da je nastanak bolesti moduliran na nivou ovih subsetova T memorijskih limfocita i hemokina...Previous studies have reported an important role of T cells subpopulations, T helper 1 (Th1) and T helper 2 (Th2), characterized with expression of chemokine receptors CXCR3 and CCR4 on their surface, respectively, as well as T regulatory (T reg) subpopulations, in the initial phase of type 1 diabetes (T1D). However, the relationship among the impairments in the level of CXCR3+ (Th1 associated), CCR4+ (Th2 associated) and CD25high (T reg associated) T memory cells subpopulations, as well as cytokines, their ligands/mediators of function, interferon-γ inducibile chemokine-IP-10 (Th1 associated), thymus and activation-regulated chemokine-TARC (Th2 associated) and transforming growth factor β-TGFβ (Treg associated), and risk for T1D developing, has not yet been clarified. Additionally, relationship between the immunological and metabolic changes, regarding the insulin secretion and insulin sensitivity level, early in T1D development, is still controversial. Therefore, the aim of this study was to analyse the changes in (a) percentage of CXCR3+, CCR4+ T memory cell and CD25high T cells subsets (b) IP-10, TARC and TGFβ (c) insulin secretion and insulin sensitivity levels and in peripheral blood in the following groups of subjects: (1) 17 high-risk nondiabetic first degree relatives (hrFDRs) of patients with T1D (glutamate decarboxylase antibodies-GADA+, tyrosine phosphatase insulinoma antigen-2 antibodies-IA-2+); (2) 34 low-risk nondiabetic first degree relatives (lrFDRs) of patients with T1D (GADA-, IA-2-); (3) 24 recent-onset T1D patients and (4) 10 patients in clinical remission (5) 18 healthy, unrelated control subjects. The percentages of CXCR3+, CCR4+ and CD25high T memory cell subsets were analyzed in peripheral blood by using four-color immunofluorescence staining and flowcytometry. IP-10, TARC, TGFβ, GADA and IA-2 levels were determined by ELISA. Insulin secretion was evaluated by firstphase insulin response (FPIR) as insulin levels 1+3 min after IVGTT. Insulin sensitivity was tested by using euglycemic hyperinsulinemic clamp method (M value). Our results have demonstrated that hr FDRs, defined by the presence of the autoantibodies, showed higher levels of CXCR3+ T cell subset and IP-10 chemokine, both associated with Th1 response, together with lower level of CCR4+ Th2 and CD25high T reg cell subsets. In this study, complementary investigations imply that in FDRs, the risk of progression to T1D might be strongly influenced by enhanced activity of Th1 and diminished activity of Th2 and T reg autoimmune response. In addition, we demonstrated that the onset of T1D is characterized by the decreases in CXCR3+ Th1 and CCR4+ Th2 memory T subsets and TGFβ levels, and increases in IP-10 and TARC, presumably reflecting possible recruitment of those cells in pancreatic tissue and functionally exhaustion of T reg subpopulation..

Combined GSTM1 and GSTT1 null genotypes are strong risk factors for atherogenesis in a Serbian population

Oxidative stress (OS) plays an important role in atherogenesis and since glutathione S-transferases (GSTs) provide protection against OS, we have tested the hypothesis that deletion polymorphisms in two GSTs (GSTM1 and GSTT1) may affect the risk of developing atherosclerosis. A total of 382 individuals (200 patients with atherosclerosis and 182 healthy controls) were included in this association study. Genomic DNA was isolated from peripheral blood cells or from buccal epithelial cells and genotyping was performed using multiplex-PCR or real-time PCR methods. GSTM1 null genotype was significantly more frequent in atherosclerotic patients than in controls (52.0% vs 34.1%) and individuals with the GSTM1 null genotype had an approximately 2-fold increase in atherosclerosis risk (OR: 2.1, 95%CI=1.39-3.17, P=0.0004). GSTT1 null genotype alone did not show a statistically significant effect on atherosclerosis risk modulation, but the association approached significance (OR: 1.57, 95%CI=0.94-2.64, P=0.08). The combined analysis showed that the presence of both genes had a protective effect against atherosclerosis (OR=0.55, 95%CI=0.37-0.83, P=0.005) while double null genotypes led to a robust atherosclerosis risk increase (OR: 8.14, 95%CI=2.41-27.51, P lt 0.0001). This study demonstrated that the GSTM1 null and combined GSTM1/GSTT1 null genotypes are susceptibility factors for development of atherosclerosis in a Serbian population

DIFFERENT TEXT REPRESENTATION MODELS

Mnoge aplikacije koje koriste automatsku klasifikaciju dokumenata, izvlačenje informacija, prepoznavanje govora ovise o statističkim jezičnim modelima. Ovaj rad usmjeren je na zadatak automatske klasifikacije dokumenata ili preciznije na istraživanje različitih statističkih jezičnih modela koji se mogu koristiti za izlucivanje znacajki iz dokumenata. Tradicionalne metode za izradu značajki baziraju se na modelima vreće riječi te se u velikoj mjeri koriste unatoč njihovim poznatim slabostima. Modeli vreće riječi popularni su zbog njihove jednostavnosti te zbog toga što vrlo cesto daju dobre rezultate. Razvoj tehnologije i algoritama za strojno učenje omogućio nam je istraživanje kompleksnijih metoda reprezentacije dokumenata. Cilj ovoga rada jest predstaviti različite modele za reprezentaciju dokumenata koji su se nedavno pojavili te istražiti da li se računalna kompleksnost tih modela može opravdati s poboljšanim performansama. Konkretno, tradicionalni modeli-vreće-riječi korišteni su kao baza za usporedbu word2vec/doc2vec modela i modela baziranih na kompleksnim mrežama. Modeli vreće riječi već su opsežno istraživani u kontekstu klasifikacije dokumenata. Međutim ostala dva modela nisu dovoljno temeljito istražena unutar istog problemskog konteksta. Rad mjeri performanse klasifikatora učenih algoritmom nasumičnih šuma na značajkama generiranima s navedenim modelima. Rezultati pokazuju da su doc2vec modeli s vektorima malih dimenzija usporedivi s tradicionalnim modelima vreće riječi. Također, modeli bazirani na grafovima koji koriste mjeru selektivnosti za značajke pokazuju poboljšanje nad modelima vreće riječi kod skupa podataka s većim brojem klasa.Many successful applications depend on statistical language models such as automatic document classification, information retrieval, speech recognition any many more. This thesis is focused on the task of automatic document classification, more specifically on exploring different statistical language models that can be used to extract features from documents. State-of-the-art methods for feature construction are based on bag-of-words models and are largely used despite their known weaknesses. Their popularity rests on their simplicity and often very high accuracy. With the development of technology and machine learning algorithms, we are now able to explore more complex methods for document representations. The goal of this thesis is to present different document representation models that emerged in recent years and to explore whether computational complexity of these models can be justified by the improvement in performance. Namely, state-of-the art bag-of-word models are used as a base for comparison of word2vec/doc2vec models and models based on complex networks. While the bag-of-word models have been extensively studied in the context of document classification, the other two models have not been well understood on the same task. The study measures the performance of classifiers trained with random forest algorithm on features generated by the specified models tuned with different parameters. Results show that low dimensional doc2vec model is comparable with the traditional bag-of-words model. Also, graph based models that use selectivity measure as a feature show improvements over the bag-of-words model on a dataset with higher number of classes

DIFFERENT TEXT REPRESENTATION MODELS

Mnoge aplikacije koje koriste automatsku klasifikaciju dokumenata, izvlačenje informacija, prepoznavanje govora ovise o statističkim jezičnim modelima. Ovaj rad usmjeren je na zadatak automatske klasifikacije dokumenata ili preciznije na istraživanje različitih statističkih jezičnih modela koji se mogu koristiti za izlucivanje znacajki iz dokumenata. Tradicionalne metode za izradu značajki baziraju se na modelima vreće riječi te se u velikoj mjeri koriste unatoč njihovim poznatim slabostima. Modeli vreće riječi popularni su zbog njihove jednostavnosti te zbog toga što vrlo cesto daju dobre rezultate. Razvoj tehnologije i algoritama za strojno učenje omogućio nam je istraživanje kompleksnijih metoda reprezentacije dokumenata. Cilj ovoga rada jest predstaviti različite modele za reprezentaciju dokumenata koji su se nedavno pojavili te istražiti da li se računalna kompleksnost tih modela može opravdati s poboljšanim performansama. Konkretno, tradicionalni modeli-vreće-riječi korišteni su kao baza za usporedbu word2vec/doc2vec modela i modela baziranih na kompleksnim mrežama. Modeli vreće riječi već su opsežno istraživani u kontekstu klasifikacije dokumenata. Međutim ostala dva modela nisu dovoljno temeljito istražena unutar istog problemskog konteksta. Rad mjeri performanse klasifikatora učenih algoritmom nasumičnih šuma na značajkama generiranima s navedenim modelima. Rezultati pokazuju da su doc2vec modeli s vektorima malih dimenzija usporedivi s tradicionalnim modelima vreće riječi. Također, modeli bazirani na grafovima koji koriste mjeru selektivnosti za značajke pokazuju poboljšanje nad modelima vreće riječi kod skupa podataka s većim brojem klasa.Many successful applications depend on statistical language models such as automatic document classification, information retrieval, speech recognition any many more. This thesis is focused on the task of automatic document classification, more specifically on exploring different statistical language models that can be used to extract features from documents. State-of-the-art methods for feature construction are based on bag-of-words models and are largely used despite their known weaknesses. Their popularity rests on their simplicity and often very high accuracy. With the development of technology and machine learning algorithms, we are now able to explore more complex methods for document representations. The goal of this thesis is to present different document representation models that emerged in recent years and to explore whether computational complexity of these models can be justified by the improvement in performance. Namely, state-of-the art bag-of-word models are used as a base for comparison of word2vec/doc2vec models and models based on complex networks. While the bag-of-word models have been extensively studied in the context of document classification, the other two models have not been well understood on the same task. The study measures the performance of classifiers trained with random forest algorithm on features generated by the specified models tuned with different parameters. Results show that low dimensional doc2vec model is comparable with the traditional bag-of-words model. Also, graph based models that use selectivity measure as a feature show improvements over the bag-of-words model on a dataset with higher number of classes

Enforcement in tax procedure and cross-border aspects

Slovenija je članica Evropske unije in pravni red Evropske unije je del slovenske zakonodaje. To pomeni, da se mora Slovenija prilagajati tudi na področju davčne zakonodaje in spremembam, ki to področje spremljajo. Pobiranje davkov je ena najstarejših pravic posamezne države. Države s pobiranjem davkov zadovoljujejo javne interese in uresničujejo svojo suverenost. Daleč v zgodovino pa sega tudi neizpolnjevanje davčnih obveznosti. Problem nastane, ko država zaradi neplačila ne more pobrati odmerjenega davka. Konflikti med davčnimi zavezanci in davčnimi organi se pojavljajo že znotraj domicilne države, še več težav pa je zaznati izven meja posamezne države. V magistrski nalogi sem se opredelila do izvršbe v davčnem postopku, najprej v slovenskem davčnem prostoru, nato pa analizirala tudi čezmejni vidik davčne izvršbe in medsebojno pomoč ter vzajemno sodelovanje pri izmenjavi informacij, ki omogočata lažjo poravnavo neplačanih davčnih obveznosti. Države članice si prizadevajo za zajezitev davčne nediscipline, pri tem pa se poslužujejo različnih ukrepov. Mednarodno sodelovanje in upravna pomoč pri izmenjavi informacij med državami članicami EU sta izjemnega pomena za uspešno davčno poslovanje in na področju pobiranja davščin. Vsaka država lahko izbira sebi prilagojen davčni sistem. Vendar države z oblikovanjem direktiv, sklepov in uredb skrbijo za to, da se davčni sistemi držav razvijajo v isto smer, se harmonizirajo in omogočajo boljše sodelovanje med davčnimi upravami pri nadzoru in pobiranju davkov. V nalogi sem se posebej opredelila še do hrvaškega davčnega sistema in primerjala zakonodajo davčne izvršbe v Sloveniji in v sosednji Republiki Hrvaški.Tax collection is one of the oldest rights of a country. Tax collection serves the public interest and fulfilling the country\u27s sovereignty. Tax evasion also has existed since time immemorial. The problem arises when a country cannot collect its due taxes because of non-payments. The conflicts between taxable persons and tax authorities are present in home countries already and even more tax conflicts are registered outside of the home country. This thesis handles the enforcement in a tax procedure. It first analyses the Slovenian tax enforcement procedure and then focuses on the enforcement of taxes abroad and the mutual help and cooperation of member states for the collection of unpaid taxes. The EU states are actively trying to curb the tax evasion and in their effort they employ a variety of measures. Both the international cooperation and the administrative help in exchanging information between member states are vital for successful taxation. Although each member state is free to choose its own tax system, EU directives and regulations ensure that the tax systems of individual countries are being developed in the same direction, are synchronized and enable a better cooperation of tax authorities with regards to tax control and tax collection. This thesis also focuses on the Croatian tax system and compares the tax enforcement procedure in the Republic of Slovenia with the one in the Republic of Croatia

Comparison of Language Network Measures for Legal Texts and Literature

Predmet istraživanja u ovom radu je usporedba jezičnih mreža pravnih i književnih tekstova pisanih na engleskom jeziku. Mreže su generirane kao usmjereni grafovi s vezama koje sadrže težine. Unutar mreže riječi predstavljaju čvorove koji su povezani ukoliko su riječi susjedne u rečenici. Dok težine njihovih veza označavaju koliko se puta pojedini par riječi ponavlja. Ispitane su mjere mreža na globalnoj, lokalnoj i središnjoj razini. Na globalnoj i središnjoj razini promatrane su mjere poput prosječne snage, prosječnog stupnja, gustoće, asortativnost, broja zajednica i dr., dok su na lokalnoj razini uzeti u obzir snaga, stupanj, koeficijent grupiranja i prosječna snaga pojedinog čvora. Cilj rada bio je ispitati i prikazati kako se dvije različite kategorije teksta odražavaju na vrijednosti mjera kompleksnih mreža. Dobiveni rezultati pokazuju kako se gledajući mreže na globalnoj razini ne mogu vidjeti značajne razlike. No usporedbom mreža na lokalnoj razini, konkretno na prosječnoj snazi čvora (selektivnosti), primijećene su razlike između dvije kategorije

Comparison of Language Network Measures for Legal Texts and Literature

Predmet istraživanja u ovom radu je usporedba jezičnih mreža pravnih i književnih tekstova pisanih na engleskom jeziku. Mreže su generirane kao usmjereni grafovi s vezama koje sadrže težine. Unutar mreže riječi predstavljaju čvorove koji su povezani ukoliko su riječi susjedne u rečenici. Dok težine njihovih veza označavaju koliko se puta pojedini par riječi ponavlja. Ispitane su mjere mreža na globalnoj, lokalnoj i središnjoj razini. Na globalnoj i središnjoj razini promatrane su mjere poput prosječne snage, prosječnog stupnja, gustoće, asortativnost, broja zajednica i dr., dok su na lokalnoj razini uzeti u obzir snaga, stupanj, koeficijent grupiranja i prosječna snaga pojedinog čvora. Cilj rada bio je ispitati i prikazati kako se dvije različite kategorije teksta odražavaju na vrijednosti mjera kompleksnih mreža. Dobiveni rezultati pokazuju kako se gledajući mreže na globalnoj razini ne mogu vidjeti značajne razlike. No usporedbom mreža na lokalnoj razini, konkretno na prosječnoj snazi čvora (selektivnosti), primijećene su razlike između dvije kategorije

Analysis of Impairements in T Cells Subsets in First Degree Relatives of Patients with Type 1 Diabetes as Risk Markers for Disease Development

Prethodna istraživanja su ukazala na značajnu ulogu subpopulacija T limfocita, T helper 1 (Th1) i T helper 2 (Th2), fenotipski definisanih ekspresijom odgovarajućih hemokinskih receptora CXCR3 i CCR4, kao i T regulatorne (T reg) subpopulacije, u inicijalnoj fazi tipa 1 dijabetesa (T1D). Meñutim, povezanost promena u nivou CXCR3+ (Th1 asociranih), CCR4+ (Th2 asociranih) i CD25high (T reg asociranih) subpopulacija T memorijskih limfocita kao i citokina, njihovih liganda/medijatora funkcije, interferon-γ inducibilnog hemokina 10 (interferon-γ inducibile chemokine-IP-10) (Th1 asociranog), timusom i aktivacijom regulisanog hemokina (thymus and activation-regulated chemokine-TARC) (Th2 asociranog) i transformišućeg faktora rasta β (transforming growth factor β-TGFβ) (Treg asociranog), i rizika za ispoljavanje T1D, nije još uvek razjašnjena. Istovremeno, povezanost promena ovih imunoloških parametara i metaboličkih parametara u smislu nivoa insulinske sekrecije i insulinske senzitivnosti u ranim fazama razvoja T1D, nije do sada detaljnije analizirana. U tom smislu, cilj istraživanja je poreñenje nivoa (a) CXCR3+, CCR4+ i CD25high subpopulacija memorijskih T limfocita (b) hemokina/citokina IP-10, TARC, TGFβ i (c) insulinske sekrecije i insulinske senzitivnosti, izmeñu grupa zdravih prvih roñaka pacijenata sa T1D (PR), sa visokim i niskim rizikom za ispoljavanje bolesti (gde je rizik za T1D definisan prisustvom/odsustvom antitela na glutamat dekarboksilazu (GAD) i tirozin fosfatazu (IA-2), pacijenata sa novootkrivenim T1D (NT1D) u insulin zavisnom stanju na početku bolesti i u stanju kliničke remisije (KR), i kontrolnih ispitanika. Uključili smo PR sa visokim rizikom (GADA+, IA-2+) (N=17), PR sa niskim rizikom (GADA-, IA-2-) (N=34), pacijente sa NT1D (N=24), pacijente sa T1D u KR (N=10) i kontrolne ispitanike (N=18). Nivo CXCR3+, CCR4+ i CD25high T memorijskih limfocita je analiziran četvorobojnom imunofluoroscencijom i protočnom citometrijom. Nivo hemokina/citokina i antitela u serumu odreñeni su ELISA metodom. Prva faza insulinske sekrecije odreñivana je zbirom nivoa insulina u 1. i 3. minutu nakon IVGTT-a. Nivo insulinske senzitivnosti evaluiran je hiperinsulinemijskim euglikemijskim klampom. Rezultati ukazuju da je u PR sa visokim rizikom utvrñen povišen nivo CXCR3+ Th1 subpopulacije i IP-10 hemokina, i snižen nivo CCR4+ Th2 i CD25high T reg subpopulacije limfocita, što bi moglo sugerisati da je u PR rizik za ispoljavanje T1D povezan sa pojačanom aktivnošću Th1 i smanjenom aktivnošću Th2 i T regulatornog imunskog odgovora. Sa druge strane, početak T1D je povezan sa značajnim smanjenjem nivoa CXCR3+ i CCR4+ subsetova T memorijskih limfocita i TGFβ, i porastom nivoa IP-10 i TARC, reflektujući njihovu akumulaciju u pankreasna ostrvca i funkcionalno iscrpljivanje regulatorne subpopulacije T limfocita, što bi moglo ukazati da je nastanak bolesti moduliran na nivou ovih subsetova T memorijskih limfocita i hemokina...Previous studies have reported an important role of T cells subpopulations, T helper 1 (Th1) and T helper 2 (Th2), characterized with expression of chemokine receptors CXCR3 and CCR4 on their surface, respectively, as well as T regulatory (T reg) subpopulations, in the initial phase of type 1 diabetes (T1D). However, the relationship among the impairments in the level of CXCR3+ (Th1 associated), CCR4+ (Th2 associated) and CD25high (T reg associated) T memory cells subpopulations, as well as cytokines, their ligands/mediators of function, interferon-γ inducibile chemokine-IP-10 (Th1 associated), thymus and activation-regulated chemokine-TARC (Th2 associated) and transforming growth factor β-TGFβ (Treg associated), and risk for T1D developing, has not yet been clarified. Additionally, relationship between the immunological and metabolic changes, regarding the insulin secretion and insulin sensitivity level, early in T1D development, is still controversial. Therefore, the aim of this study was to analyse the changes in (a) percentage of CXCR3+, CCR4+ T memory cell and CD25high T cells subsets (b) IP-10, TARC and TGFβ (c) insulin secretion and insulin sensitivity levels and in peripheral blood in the following groups of subjects: (1) 17 high-risk nondiabetic first degree relatives (hrFDRs) of patients with T1D (glutamate decarboxylase antibodies-GADA+, tyrosine phosphatase insulinoma antigen-2 antibodies-IA-2+); (2) 34 low-risk nondiabetic first degree relatives (lrFDRs) of patients with T1D (GADA-, IA-2-); (3) 24 recent-onset T1D patients and (4) 10 patients in clinical remission (5) 18 healthy, unrelated control subjects. The percentages of CXCR3+, CCR4+ and CD25high T memory cell subsets were analyzed in peripheral blood by using four-color immunofluorescence staining and flowcytometry. IP-10, TARC, TGFβ, GADA and IA-2 levels were determined by ELISA. Insulin secretion was evaluated by firstphase insulin response (FPIR) as insulin levels 1+3 min after IVGTT. Insulin sensitivity was tested by using euglycemic hyperinsulinemic clamp method (M value). Our results have demonstrated that hr FDRs, defined by the presence of the autoantibodies, showed higher levels of CXCR3+ T cell subset and IP-10 chemokine, both associated with Th1 response, together with lower level of CCR4+ Th2 and CD25high T reg cell subsets. In this study, complementary investigations imply that in FDRs, the risk of progression to T1D might be strongly influenced by enhanced activity of Th1 and diminished activity of Th2 and T reg autoimmune response. In addition, we demonstrated that the onset of T1D is characterized by the decreases in CXCR3+ Th1 and CCR4+ Th2 memory T subsets and TGFβ levels, and increases in IP-10 and TARC, presumably reflecting possible recruitment of those cells in pancreatic tissue and functionally exhaustion of T reg subpopulation..

The Influence of Feature Representation of Text on the Performance of Document Classification

In this paper we perform a comparative analysis of three models for a feature representation of text documents in the context of document classification. In particular, we consider the most often used family of bag-of-words models, the recently proposed continuous space models word2vec and doc2vec, and the model based on the representation of text documents as language networks. While the bag-of-word models have been extensively used for the document classification task, the performance of the other two models for the same task have not been well understood. This is especially true for the network-based models that have been rarely considered for the representation of text documents for classification. In this study, we measure the performance of the document classifiers trained using the method of random forests for features generated with the three models and their variants. Multi-objective rankings are proposed as the framework for multi-criteria comparative analysis of the results. Finally, the results of the empirical comparison show that the commonly used bag-of-words model has a performance comparable to the one obtained by the emerging continuous-space model of doc2vec. In particular, the low-dimensional variants of doc2vec generating up to 75 features are among the top-performing document representation models. The results finally point out that doc2vec shows a superior performance in the tasks of classifying large documents