MUN Chamber Choir, conductor: D.F. Cook (April 29-May 3, 1979)

MUN Chamber Choir, conductor D.F. Cook (April 29-May 3, 1979

A comparison of flexural strengths of polymer (SBR and PVA) modified, roller compacted concrete

This brief article aims to reveal the flexural performance, including the equivalent flexural strength of PVA (Polyvinyl Alcohol) modified concrete by comparing it primarily with that of SBR (Styrene Butadiene Rubber) concrete. This data article is directly related to Karadelis and Lin [6]

Predicting Oral Cancer-related mortality among adults in the United States

Objectives: To predict oral cancer-related mortality among adults in the United States and identify the predictors of oral cancer-related mortality using the Machine Learning Approach. Methods: We extracted data for 8,176 participants from the SEER database (1975 to 2022). A series of 38 demographic, clinicopathological, and lifestyle factors were extracted from the SEER database along with the outcome variable Oral Cancer-Related Mortality (OCRM) coded as “Died from Oral Cancer” and “Alive/Died from Other Causes.” The data were pre-processed using recipe packages in R. Machine Learning (ML) models-extreme gradient boosting (xgboost), Lassso Regression, and K-nearest neighbor were used to perform prediction of oral cancer prognosis under five-fold cross-validation to prevent overfitting or underfitting of the data. Model performance was evaluated using the Brier score, area under the curve (AUC), specificity, sensitivity, and accuracy. ML model was performed using MachineShop Package in R. Results: The study participants were 63% male and predominantly non-Hispanic white (71%). 7444 participants were alive or dead of other causes and 732 were dead due to cancer. Across all models, XGBoost ML model performed the best with a Brier Score of 0.0677, an accuracy of 91%, a 13% kappa statistic, an ROC AUC of 84%, a sensitivity of 99%, and less than 1% specificity. Out of 38 variables assessed, 17 were found to be the most important predictors of OCRM. The most important predictors of OCRM (in descending order) were cancer stage group, age, T stage, Lymph node surgery, cancer site, tumor rarity, N stage, marital status, radiation, income, grade, lymph node size, surgery radiation sequence, race, histology, the sequence number of multiple primary cancers, side of a paired organ which tumor originated from. Conclusion: Our Machine-Learning model was effective in predicting oral cancer mortality using clinicopathological variables from the national cancer registry. Keywords: machine learning, metastasis, oral cancer, prediction, squamous cell carcinoma, SEER database.Oral Biolog

Sensory characteristics and nutritional quality of food products made with a biofortified and lectin free common bean (Phaseolus vulgaris l.) flour

Common beans (Phaseolus vulgaris L.) are an important source of nutrients with beneficial effects on human health. However, they contain lectins, that limit the direct use of flour in food preparations without thermal treatment, and phytic acid, that reduces mineral cation bioavailability. The objectives of this research were: to obtain biofortified snacks and a cream using an untreated common bean flour devoid of active lectins (lec− ) and with reduced content of phytic acid (lpa) and to evaluate the sensorial appreciation for these products. The main results of the present work were: the products with the lpa lec− flour did not retain residual hemagglutinating activity due to lectins; they showed higher residual α-amylase inhibitor activity (from 2.2 to 135 times), reduced in vitro predicted glycemic index (about 5 units reduction) and increased iron bioavailability compared to the products with wild type flour; products with common bean flour were less appreciated than the reference ones without this flour, but the presence of an intense umami taste can be a positive attribute. Results confirmed that the use of the lpa lec− flour has important advantages in the preparation of safe and nutritionally improved products, and provide useful information to identify target consumers, such as children and elderly people

High monocyte CD300e expression in patients with acute onset type 1 diabetes

大阪医科薬科大学博士（医学）2022thesi

Sykepleiers erfaringer med identifisering og kartlegging av delirium hos eldre pasienter på sykehus

Bakgrunn Delirium er en alvorlig forvirrelses tilstand som rammer opptil 50% av eldre sykehuspasienter. Sykepleiere har de beste forutsetningene for å identifisere og kartlegge pasientens kognitive status og om den fluktuerer, da de tilbringer mye av tiden sammen med pasienten. Flere eldre pasienter kan ende opp med udiagnostisert delirium, dersom tilstanden ikke blir identifisert eller kartlagt på et tidlig stadium. Dette kan føre til alvorlige komplikasjoner hos pasientene. Hensikt Oppgavens hensikt er å beskrive sykepleieres erfaringer med identifisering og kartlegging av delirium hos eldre pasienter på sykehus. På bakgrunn av dette vil vi diskutere aktuelle sykepleiefaglige og organisatoriske tiltak for anvendelse i praksis. Metode Metoden benyttet i oppgaven er en litteraturstudie, hvor fire kvalitative studier ble analysert. Resultatene fra forskningsartiklene, relevant teori, bakgrunnslitteratur og egne refleksjoner legger til grunnlaget for besvarelsen av oppgaven. Resultat Resultatene viser at sykepleiere har manglende kunnskaper og har ulike forståelser om sykepleiers rolle i identifisering og kartlegging av delirium. Videre viser resultatene at organisatoriske faktorer har en stor innvirkning på hvordan sykepleiere identifiserer og kartlegger delirium. Nøkkelord: Delirium, sykepleier, erfaringer, kunnskap, identifisering, kartlegging, kartleggingsverktøy

Mechanical Reliability of Sintered Nano Silver for Power Device Packaging

京都先端科学大学博士（工学）2022年度doctoral thesi

政治と行政の中の「学問の自由」-ポポロ最高裁判所を機縁として-

Articledepartmental bulletin pape

データ駆動型ネットワークプロセッサDDNPのパケットフィルタリング機構

2006-07-06 WEB公開高知工科大学博士(工学) 平成18年3月20日授与 (甲第88号)高知工科大学, 博士論文.thesi