91 research outputs found
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No full text157 pagesMFA theses in English Language and Literature are not available for direct download. Users wishing to access an MFA thesis in this collection may request access by clicking the link to the restricted file(s) and completing the request form. If we have contact information for the author, we will contact them and request permission to provide access. If we do not have contact information or the author denies or does not respond to our inquiry, we will not be able to provide access.A novel that follows four women from Karachi, Pakistan, estranged friends who come back together in the wake of a tragedy in one of their lives.10000-01-0
V kolì Andriâ Šeptic'kogo : Ukraïns'ke kul'turne vìdrodžennâ za časìv mecenatstva mitropolita
No full textTekst jest pierwszym w języku polskim studium o mecenacie Andrzeja Szeptyckiego (1865-1944), greckokatolickiego metropolity lwowskiego (1900-1944). Autor opisuje dzieła Szeptyckiego: szkolę ikon, Narodowe Muzeum Ukraińskie we Lwowie, mecenat nad ukraińską sztuką nowoczesną, ukraińskie organizacje artystyczne (SPUOM, GDUM, ANUM) i wystawy sztuki ukraińskiej w czasie II wojny światowej we Lwowie. Tekst zawiera unikatowy wykaz ok. 150 artystów ukraińskich z kręgu Andrzeja Szeptyckiego.The text is the first study in Polish on art patronage by Andrej Sheptyckyj (1865-1944), the Greek Catholic metropolite of Lwow (1900-1944). The author describes Sheptyckyj's works: the school of icons, the National Ukrainian Museum in Lwow, patronage for Ukrainian modern art, Ukrainian artistic organizations (SPUOM, GDUM, ANUM) and Ukrainian art exhibitions in Lwow in the time of the World War II. The text contains the unique register (the list) of circa 150 Ukrainian artists from the Andrej Sheptyckyj's circle
Alienation and the Dilemma of Man in Eugene O’Neill’s The Hairy Ape
No full textEugene O\u27 Neill, an American playwright was born into a troubled and an upset family on October 16, 1888. Eugene O’ Neill had a quite precarious, wobbly and uneven adolescence as his elder brother was an affirmed alcoholic whereas his mom was a drug addict. This research paper analyzes the alienation, dilemma and the futile struggle of man in the quest of his identity. O\u27Neill followed the course of a superior and advanced writer looking for a profound focus in the entirety of his significant works. His perspective on humankind in his dramatizations is basically sad and heartbreaking. The author needed to cause man to feel free from all worries and inhale outside fresh air and build up a feeling of having a place in the general public in which he lived. However, it was impractical
Automated Segmentation of Metastatic Lymph Nodes in Lymphoma Patients
No full textVed å bruke det kunstige nevrale nettverket 2D U-Net, tester denne masteroppgaven nøyaktigheten til 2D U-Net med formålet om å automatisk segmentere ondartede svultser i PET/MR-bilder av pasienter med metastatisk lymfom.
For Hodgkin- og Non-Hodgkin-lymfom er FDG PET/MR-segmenteringene viktige for prognose, stadieinndeling (staging) og responsvurdering av lymfompasienter. Manuelle segmenteringer er imidlertid tidkrevende og vanskelige i komplekse pasienttilfeller der en har høy sykdomsbyrde. Målet med dette prosjektet er å utvikle en automatisert metode for segmentering av kreftrammede lymfeknuter i PET/MR ved bruk av dyp læring (deep learning), nærmere bestemt et dypt kunstig nevralt nettverk. FDG PET/MR-baseline, interim- og behandlingsavslutningsbilder (EOT) av Hodgkin- og Non-Hodgkin-lymfompasienter ble analysert. To grupper radiologer og nukleærmedisinere har bidratt med klinisk lesing av PET/MR-bildene etter standardiserte protokoller. Imidlertid manglet de faktiske segmenteringene, m.a.o segmenterings fasiten, fra lymfomdatasettet, og disse var avgjørende for å implementere dyplæringsnettverket for en automatisert segmenteringsprosess. De manuelle segmenteringene som krevdes ble utført av forfatteren og validert av en nukleærmedisiner fra St.Olavs Hospital.
Den nevrale nettverksmodellen ble lært hvordan man utfører klassifiseringsoppgaver direkte fra bilder, dvs. nettverket ble opplært til å gjenkjenne mønstre fra et datasett bestående av 64 PET/MR-undersøkelser. Et 3-kanals multimodalt bilde, et RGB-bilde, bestående av PET, T2-HASTE og DWI med b = 800 s/mm^{2} ble brukt som input for algoritmen, og modellen ble lært til å gjenskape segmenteringene i grunnsannheten (segmenterings fasiten) ved å bruke en 2D U-Net-arkitektur.
Videre ble lymfomdatasettet delt inn i et 85/15-forhold for trening og testing som bestod av henholdsvis 53 og 11 PET/MR-undersøkelser. Både en 4-fold og 13-fold kryssvalidering ble utført for opplæringen av modellen. Valideringen resulterte i gjennomsnittlige Dice-score (overlappingsmål) på henholdsvis 0,61 og 0,63 for 4-fold og 13-fold modellene. Flere andre evaluaringer som tap, nøyaktighet, presisjon, tilbakekalling, negativ prediktiv verdi (NPV) og spesifisitet ble inkludert for voxel-nivåanalysen. Resultatene var 0,011, 0,97, 0,83, 0,11, 0,97, 0,99, henholdsvis for 4-fold valideringen og 0,065, 0,97, 0,90, 0.10, 0,97, 0,99, henholdsvis for 13-fold valideringen. Den gjennomsnittlige dice scoren til testpasientene var henholdsvis 0,29 og 0,32 for 4-fold og 13-fold kryssvalidering, noe som antyder en dårligere ytelse på nye og usette pasienter sammenlignet med PET/MR-undersøkelsene brukt i valideringen. Til tross for de generelt høye verdiene for evalueringsmetodene, ga den voxelbaserte analysen ingen god indikasjon på hvor nøyaktig modellen klarte å segmentere kreftlesjoner ettersom flertallet av vokslene i pasientene ble klassifisert som ekte negative (TN). Derfor ble det utført en lesjonsbasert analyse, og den avslørte at modellen ofte segmenterte færre lesjoner enn det som var tilstede i grunnsannheten. Dette indikerte at modellens hovedbegrensning var antallet falske negative predikerte kreftsvultser. Som en konsekvens, presterer modellen bedre på valideringsdataene enn for testdatasettet som ble ekskludert fra opplæringen.
For å konkludere, så segmenterer den trente 2D U-Net-modellen automatisk ondartede lymfeknutesvultser i 3-kanals multimodale bilder. Fremtidig arbeid bør fokusere på å forbedre overlappingsmålet dice score, co-registreringen, redusere antall uoppdagede tumorlesjoner, samt øke datasettet for å sikre en større variasjon i kohorten. Dette kan forbedre både treningen og resultatene.Using the deep learning artificial neural network 2D U-Net, this project tests the accuracy of the 2D U-Net for the purpose of automatically segmenting malignant lesions in PET/MR images of patients with metastatic lymphoma.
For Hodgkin and Non-Hodgkin lymphoma, the FDG PET/MRI segmentations are important for prognosis, staging, and response assessment of lymphoma patients. However, manually segmentations are time-consuming and difficult in complex patient cases and for high disease burden. The aim of this project is to develop an automated method for segmentation of cancer-affected lymph-nodes in PET/MRI using a deep neural network.
FDG PET/MRI baseline, interim, and End-Of-Treatment (EOT) images of Hodgkin and Non-Hodgkin lymphoma patients were analyzed. Two groups of radiologist and nuclear medicine physicians have contributed with clinical reading of the PET/MR images following standardized protocols. However, the segmentation ground truth was missing from the lymphoma dataset, and it was crucial for implementing the deep learning network for an automated segmentation process. The manual segmentation required has therefore been performed by the author and validated by a nuclear medicine physician from St. Olavs Hospital.
The neural network model was taught how to perform classification tasks directly from images, i.e., the network was trained to recognize patterns from a dataset consisting of 64 PET/MRI examinations. A 3-channel multi-modal image, i.e., an RGB image, consisting of a PET, a T2-HASTE, and a DWI with b = 800 s/mm^{2} was used as input for the algorithm. The model was trained to replicate the segmentations of the ground truth by using a 2D U-Net architecture.
Furthermore, the lymphoma dataset was divided in a 85/15 ratio for training and testing consisting of 53 and 11 PET/MRI examinations, respectively. Both a 4-fold and 13-fold cross-validation were performed for the training of the model. The validation resulted in average dice scores of 0.61 and 0.63 respectively for the 4-fold and 13-fold trained models. Several other metrics such as loss, accuracy, precision, recall, Negative Predictive Value (NPV), and specificity were included for the voxel level analysis. The scores were 0.011, 0.97, 0.83, 0.11, 0.97, 0.99, respectively for the 4-fold validation and 0.065, 0.97, 0.90, 0.10, 0.97, 0.99, respectively for the 13-fold validation. The average dice score of the testing patient were 0.29 and 0.32 respectively for the 4-fold and 13-fold cross-validation which suggested an inferior performance on unseen patients compared to the PET/MRI examinations used in the validation. Despite the overall high scores for the evaluation metrics, the voxel based analysis did not give a great indication of how well the model managed to segment cancer lesions due to the majority of the voxels in a patient being classified as true negative. Therefore, a lesion-based analysis were conducted and it revealed that the model often segmented fewer lesions than in the ground truth. This indicated that the model's main limitation was the number of false negative predicted lesions. As a consequence, the model performs better on the validation data than for the testing dataset which was excluded from the training.
In conclusion, the trained 2D U-Net model automatically segments malignant lymph node lesions in the 3-channel multi-modal images. However, future research should focus on improving the dice score, co-registration, decrease the number of undetected tumor lesions, and increase the dataset to ensure a larger variation in the cohort. This will benefit the training and yield better results
Novel and Ultra-Rare Heterozygous Mis-Sense LMNA Variants Causing Familial Partial Lipodystrophy
No full textThe attached file includes case descriptions and supplementary tables. This submission meets the Extended Data Sets and Supplemental Materials requirements that are included in author guidelines for the Journal of Clinical Endocrinology & Metabolism (Print ISSN 0021-972X, Online ISSN 1945-7197).Figure references for two patients needed to be corrected. A revised version of the file was uploaded on 2025-05-14 at the author's request
Digital Credibility and Social Gratification: Understanding How Generation Z in Pakistan Engages with Misinformation and Algorithmic Influence in the Contemporary Social Media Landscape
No full textIn the contemporary digital landscape, Generation Z increasingly relies on social media as a primary source of information, communication, and self-expression. While these platforms foster connectivity, learning, and creativity, they also amplify the circulation of misinformation due to limited regulation and inadequate fact-checking practices. This study investigates the motivations and behavioral patterns of Generation Z in Pakistan concerning online information engagement, focusing on the balance between social gratification and information credibility. Employing a qualitative exploratory design, data were collected through five focus group discussions (FGDs) comprising 25 participants across diverse academic disciplines, including Media Studies, Art & Design, Computer Science, Business Administration, and Allied Health Sciences. Thematic analysis revealed that social validation and entertainment are dominant motivators for content sharing, whereas critical evaluation and fact-checking remain secondary concerns. Instagram and WhatsApp emerged as the most frequently used platforms, followed by X (formerly Twitter), TikTok, and Facebook. Although participants acknowledged the prevalence of misinformation, only 52% consistently verified content prior to sharing. The study highlights how algorithmic reinforcement and emotional engagement contribute to selective exposure and echo chambers, intensifying the challenge of discerning credible information. Findings underscore the need for comprehensive digital literacy initiatives that integrate fact-checking, ethical sharing, and critical thinking into educational frameworks. The research contributes to the broader discourse on media ethics, algorithmic influence, and the sociocognitive dimensions of digital engagement among youth in developing contexts
Digital Credibility and Social Gratification: Understanding How Generation Z in Pakistan Engages with Misinformation and Algorithmic Influence in the Contemporary Social Media Landscape
No full textIn the contemporary digital landscape, Generation Z increasingly relies on social media as a primary source of information, communication, and self-expression. While these platforms foster connectivity, learning, and creativity, they also amplify the circulation of misinformation due to limited regulation and inadequate fact-checking practices. This study investigates the motivations and behavioral patterns of Generation Z in Pakistan concerning online information engagement, focusing on the balance between social gratification and information credibility. Employing a qualitative exploratory design, data were collected through five focus group discussions (FGDs) comprising 25 participants across diverse academic disciplines, including Media Studies, Art & Design, Computer Science, Business Administration, and Allied Health Sciences. Thematic analysis revealed that social validation and entertainment are dominant motivators for content sharing, whereas critical evaluation and fact-checking remain secondary concerns. Instagram and WhatsApp emerged as the most frequently used platforms, followed by X (formerly Twitter), TikTok, and Facebook. Although participants acknowledged the prevalence of misinformation, only 52% consistently verified content prior to sharing. The study highlights how algorithmic reinforcement and emotional engagement contribute to selective exposure and echo chambers, intensifying the challenge of discerning credible information. Findings underscore the need for comprehensive digital literacy initiatives that integrate fact-checking, ethical sharing, and critical thinking into educational frameworks. The research contributes to the broader discourse on media ethics, algorithmic influence, and the sociocognitive dimensions of digital engagement among youth in developing contexts
Figure 1 from: Rais M, Ahmed W, Sajjad A, Akram A, Saeed M, Hamid HN, Abid A (2021) Amphibian fauna of Pakistan with notes on future prospects of research and conservation. ZooKeys 1062: 157-175. https://doi.org/10.3897/zookeys.1062.66913
No full textFigure 1 A Iranian Toad (Bufotes surdus) B Batura Toad (Bufotes baturae) C Himalayan Toad (Duttaphrynus himalayanus) D Ladakh Toad (Bufotes latastii) E Baloch Green Toad (Bufotes zugmayeri) F Swat Green Toad (Bufotes pseudoraddei). Photographers: Dr Spartak Litvinchuk (A–D, F); Muhammad Sharif Khan (E)
Figure 3 from: Rais M, Ahmed W, Sajjad A, Akram A, Saeed M, Hamid HN, Abid A (2021) Amphibian fauna of Pakistan with notes on future prospects of research and conservation. ZooKeys 1062: 157-175. https://doi.org/10.3897/zookeys.1062.66913
No full textFigure 3 A Skittering Frog (Euphlyctis cyanophlyctis) B Skittering Frog (Euphlyctis kalasgramensis) C Pierrei's Cricket Frog (Minervarya pierrei) D Murree Hills Frog (Nanorana vicina) E, F Burrowing Frog (Sphaerotheca maskeyi). Photographers: Dr Muhammad Rais (A, C–F); Waqas Ali (B)
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