QR codes

In this thesis QR codes are considered. A QR code is a two-dimensional bar code for machine-readable data. Data are first encoded using Reed-Solomon error correction codes to ensure readability even if the QR code is damaged. In the first part of the thesis we first review finite fiels that are needed in the sequel. Next we introduce error correction codes and define Reed-Solomon codes. Reed-Solomon codes attain the Singleton bound which means that they are capable of correcting the maximum possible number of errors with respect to to the number of symbols that are added to the message. We also present algorithms for coding and decoding Reed-Solomon codes, which are used for QR codes. In the second part of the thesis we describe the structure of the QR code symbol in detail. We explain how the message is encoded in the QR code and how a QR code is decoded. We illustrate these procedures with examples. Finally, we discuss the security of QR codes and possibilities of their abuse

QR codes

In this thesis QR codes are considered. A QR code is a two-dimensional bar code for machine-readable data. Data are first encoded using Reed-Solomon error correction codes to ensure readability even if the QR code is damaged. In the first part of the thesis we first review finite fiels that are needed in the sequel. Next we introduce error correction codes and define Reed-Solomon codes. Reed-Solomon codes attain the Singleton bound which means that they are capable of correcting the maximum possible number of errors with respect to to the number of symbols that are added to the message. We also present algorithms for coding and decoding Reed-Solomon codes, which are used for QR codes. In the second part of the thesis we describe the structure of the QR code symbol in detail. We explain how the message is encoded in the QR code and how a QR code is decoded. We illustrate these procedures with examples. Finally, we discuss the security of QR codes and possibilities of their abuse

Predicting tumor response in metastatic melanoma based on gene expression signatures

Uvod: Zaviralci imunskih kontrolnih točk so v zadnjem desetletju naredili izjemen napredek pri zdravljenju metastatskega melanoma, vendar je odzivnost na zdravljenje relativno nizka. Prepoznavanje biologije odziva in odpornosti na zdravljenje sta prednostni nalogi za optimizacijo izbire zdravil in izboljšanje rezultatov bolnikov. V okviru naše študije smo ocenili genski podpis IPRES za napovedovanje odziva na imunoterapijo. Metode: Izvedli smo statistično analizo kliničnih podatkov bolnikov z metastatskim melanomom in temeljne korake razvoja napovednega modela na transkiptomskih podatkih. Napovedne modele smo zgradili z metodo multiple logistične regresije, naključnega gozda in nevronsko mrežo. Modele smo ocenili z 20-kratno ponovitvijo vgnezdenega 5-kratnega sorazmernega prečnega preverjanja. Rezultati: Z uporabo podpisa IPRES kot vhodne spremenljivke napovednih modelov se je za najboljšega izkazal naključni gozd z rezultatom pri vrednosti AUC 0,65 (95 % IZ: 0. 65– 0.66). Z integracijo statistično značilnih genomskih podatkov smo vrednost metrike AUC povišali na 0,72 (95 % IZ: 0,71–0,72). Razprava in zaključek: Geni IPRES so bili izbrani kot diferencialni geni. Izkazalo se je, da diferenčnost izražanja genov med neodvisnima bazama podatkov iste vrste raka ni ponovljiva in da diferencialni geni kot predstavniki signalnih poti nimajo nujno zadostne napovedne moči. Potrdili smo pomembnost združevanja – omik in uporabo modelov strojnega učenja za doseganje natančnejših napovedi.Introduction: Immune checkpoint inhibitors have made significant progress in metastatic melanoma treatment over the past decade, but the response rate to this treatment is relatively low. Understanding the biology of response and resistance to treatment are high priority tasks for drug selection optimisation and improved patient outcomes. In this study, we evaluated the IPRES gene signature for predictiveness of immunotherapy response. Methods: We carried out a statistical analysis of metastatic melanoma patient clinical data and the fundamental steps of predictive model development on transcriptomic data. Prediction models were built using multiple logistic regression, random forest and neural networks. They were evaluated by nested 5-fold cross-validation repeated 20 times. Results: Using IPRES signature as input, the random forest model showed best performance with AUC 0.65 (95% CI: 0.65–0.66). We were able to improve the AUC metric to 0.72 (95 % CI: 0.71–0.72) by integrating statistically significant genome data. Discussion and Conclusion: IPRES genes were selected as differential ones. It turned out that differential expression of genes between independent databases of the same cancer type data is not replicable and that differential genes as representatives of signalling pathways do not necessarily have sufficient predictive power. We have excludes the IPRES signature from potential biomarkers. We have, however, confirmed the importance of – omic data integration and the use of machine learning models for achieving more accurate predictions

Žuželke- delavnice za otroke

Prispevek je rezultat projekta z naslovom Promocija biotske pestrosti v Krajinskem parku Kolpa (PRO-BIO-KOLPA), pri katerem so sodelovale institucije: Univerza v Ljubljani, Pedagoška fakulteta in Fakulteta za računalništvo in informatiko, KOLPA\u27S podjetje za proizvodnjo, trgovino, gostinstvo in servis, d. o. o., in Javni zavod Krajinski park Kolpa. Projekt je potekal od marca do julija 2017. S projektom smo želeli izboljšati ozaveščenost mladih o metuljih in žuželkah na splošno. Na osnovni šoli in v Krajinskem parku Kolpa smo izvedli različne delavnice o žuželkah. Otroci in mladostniki so se učili rokovanja z žuželkami ter raznolikost metuljev spoznavali med lovljenjem metuljev na terenu. Izvedli smo delavnice o obustnih aparatih žuželk, o razvojnem krogu metuljev, o zgradbi metuljev, simetriji kril ter o raznolikosti žuželk

Žuželke- delavnice za otroke

Prispevek je rezultat projekta z naslovom Promocija biotske pestrosti v Krajinskem parku Kolpa (PRO-BIO-KOLPA), pri katerem so sodelovale institucije: Univerza v Ljubljani, Pedagoška fakulteta in Fakulteta za računalništvo in informatiko, KOLPA'S podjetje za proizvodnjo, trgovino, gostinstvo in servis, d. o. o., in Javni zavod Krajinski park Kolpa. Projekt je potekal od marca do julija 2017. S projektom smo želeli izboljšati ozaveščenost mladih o metuljih in žuželkah na splošno. Na osnovni šoli in v Krajinskem parku Kolpa smo izvedli različne delavnice o žuželkah. Otroci in mladostniki so se učili rokovanja z žuželkami ter raznolikost metuljev spoznavali med lovljenjem metuljev na terenu. Izvedli smo delavnice o obustnih aparatih žuželk, o razvojnem krogu metuljev, o zgradbi metuljev, simetriji kril ter o raznolikosti žuželk