Savage的問題,透過圖書和論文來找解法和答案更準確安心。 我們找到下列各種有用的問答集和懶人包

Savage的問題,我們搜遍了碩博士論文和台灣出版的書籍,推薦Murdock, Tricia,Savage, Johanna寫的 Atlas of Gynecologic Pathology: A Pattern Based Approach 和Pollack, Deborah C.的 Florida Sculptors and Their Work: 1880-2020都 可以從中找到所需的評價。

另外網站savage - Yahoo奇摩字典搜尋結果也說明:savage · adj. 殘暴的;猛烈的; 凶狠的; 惡毒的 · n. 未開化的人;凶殘的人 · vt. 凶猛地攻擊;猛烈抨擊 ...

這兩本書分別來自 和所出版 。

國立臺北科技大學 電資學院外國學生專班(iEECS) 白敦文所指導 VAIBHAV KUMAR SUNKARIA的 An Integrated Approach For Uncovering Novel DNA Methylation Biomarkers For Non-small Cell Lung Carcinoma (2022),提出Savage關鍵因素是什麼,來自於Lung Cancer、LUAD、LUSC、NSCLC、DNA methylation、Comorbidity Disease、Biomarkers、SCT、FOXD3、TRIM58、TAC1。

而第二篇論文臺北醫學大學 醫務管理學系碩士在職專班 簡文山所指導 邱彥蓁的 以人工神經網路(ANN)分析心臟衰竭再住院的危險因子 (2021),提出因為有 心臟衰竭、再住院、人工神經網路、模型預測的重點而找出了 Savage的解答。

最後網站Savage Pizza | Great Food for the Savage in you!則補充:Savage Pizza restaurants of Atlanta, Georgia. Choose one of our 2 Dine-in locations. Home. Savage ...

接下來讓我們看這些論文和書籍都說些什麼吧:

除了Savage,大家也想知道這些:

Atlas of Gynecologic Pathology: A Pattern Based Approach

為了解決Savage的問題,作者Murdock, Tricia,Savage, Johanna 這樣論述:

Savage進入發燒排行的影片

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An Integrated Approach For Uncovering Novel DNA Methylation Biomarkers For Non-small Cell Lung Carcinoma

為了解決Savage的問題,作者VAIBHAV KUMAR SUNKARIA 這樣論述:

Introduction - Lung cancer is one of primal and ubiquitous cause of cancer related fatalities in the world. Leading cause of these fatalities is non-small cell lung cancer (NSCLC) with a proportion of 85%. The major subtypes of NSCLC are Lung Adenocarcinoma (LUAD) and Lung Small Cell Carcinoma (LUS

C). Early-stage surgical detection and removal of tumor offers a favorable prognosis and better survival rates. However, a major portion of 75% subjects have stage III/IV at the time of diagnosis and despite advanced major developments in oncology survival rates remain poor. Carcinogens produce wide

spread DNA methylation changes within cells. These changes are characterized by globally hyper or hypo methylated regions around CpG islands, many of these changes occur early in tumorigenesis and are highly prevalent across a tumor type.Structure - This research work took advantage of publicly avai

lable methylation profiling resources and relevant comorbidities for lung cancer patients extracted from meta-analysis of scientific review and journal available at PubMed and CNKI search which were combined systematically to explore effective DNA methylation markers for NSCLC. We also tried to iden

tify common CpG loci between Caucasian, Black and Asian racial groups for identifying ubiquitous candidate genes thoroughly. Statistical analysis and GO ontology were also conducted to explore associated novel biomarkers. These novel findings could facilitate design of accurate diagnostic panel for

practical clinical relevance.Methodology - DNA methylation profiles were extracted from TCGA for 418 LUAD and 370 LUSC tissue samples from patients compared with 32 and 42 non-malignant ones respectively. Standard pipeline was conducted to discover significant differentially methylated sites as prim

ary biomarkers. Secondary biomarkers were extracted by incorporating genes associated with comorbidities from meta-analysis of research articles. Concordant candidates were utilized for NSCLC relevant biomarker candidates. Gene ontology annotations were used to calculate gene-pair distance matrix fo

r all candidate biomarkers. Clustering algorithms were utilized to categorize candidate genes into different functional groups using the gene distance matrix. There were 35 CpG loci identified by comparing TCGA training cohort with GEO testing cohort from these functional groups, and 4 gene-based pa

nel was devised after finding highly discriminatory diagnostic panel through combinatorial validation of each functional cluster.Results – To evaluate the gene panel for NSCLC, the methylation levels of SCT(Secritin), FOXD3(Forkhead Box D3), TRIM58(Tripartite Motif Containing 58) and TAC1(Tachikinin

1) were tested. Individually each gene showed significant methylation difference between LUAD and LUSC training cohort. Combined 4-gene panel AUC, sensitivity/specificity were evaluated with 0.9596, 90.43%/100% in LUAD; 0.949, 86.95%/98.21% in LUSC TCGA training cohort; 0.94, 85.92%/97.37 in GEO 66

836; 0.91,89.17%/100% in GEO 83842 smokers; 0.948, 91.67%/100% in GEO83842 non-smokers independent testing cohort. Our study validates SCT, FOXD3, TRIM58 and TAC1 based gene panel has great potential in early recognition of NSCLC undetermined lung nodules. The findings can yield universally accurate

and robust markers facilitating early diagnosis and rapid severity examination.

Florida Sculptors and Their Work: 1880-2020

為了解決Savage的問題,作者Pollack, Deborah C. 這樣論述:

The first study of its kind, featuring over 80 important artists who worked in Florida. With its natural beauty, distinctiveness, and warmth, Florida has attracted an abundance of artists for centuries. While fine painters have been featured in art books about the state, little has been written a

bout important sculptors who have adopted it. To remedy the dearth of literature on the subject, Florida Sculptors and Their Work: 1880-2020 is a tribute to these diverse artists who have enchanted, amused, saddened, or outraged us. Capturing the Sunshine State’s essence, this well-researched and ge

nerously illustrated volume tells the fascinating stories of these creative people and reveals secrets behind their three-dimensional art--from realistic to abstract to folk art. Discover how Florida has inspired such world-renowned artists as Augusta Savage, Duane Hanson, Richard Anuszkiewicz, John

Chamberlain, and Robert Rauschenberg, as well as lesser-known yet highly praised sculptors who have enhanced collections throughout the world and changed the state’s profile with their iconic public art. This indispensable resource is a must-have for those interested in Florida’s art, history, and

culture. Select list of museums whose work is featured: TK

以人工神經網路(ANN)分析心臟衰竭再住院的危險因子

為了解決Savage的問題,作者邱彥蓁 這樣論述:

研究目的:以人工神經網路及統計運算方法預測人口學特徵與疾病因子對於心臟衰竭再住院的影響程度。研究方法:本研究以次級資料進行分析,運用北部某醫學大學臨床研究資料庫資料,採人工神經網路(Artificial Neural Network, ANN)演算法來預測心臟衰竭住院病患再住院的危險因子,本研究個案之基本人口學特徵為年齡、性別、BMI;疾病因子為高血壓、高血脂、冠狀動脈疾病、心肌梗塞、糖尿病、慢性阻塞性肺病、慢性腎臟病。研究資料區間自2010年01月01日至2020年12月31日,總樣本數為3,256筆,以R軟體進行隨機分組,分為75%訓練組(N=2,442)及25%測試組(N=814),透

過輸入變項之不同,進行各模組間比較。每項模組訓練以十折交叉驗證進行試驗,取其準確度最佳之結果作為評估心臟衰竭再住院模型之標準。最後針對選擇出的最佳模組,呈現各變項在神經網路模型中的相對重要程度。研究結果:經各項模組比較後發現,納入所有變項之模組表現最佳,測試組之敏感度為94.49%、準確度為80.96%,以及ROC曲線下面積為85.96%,其表示各項危險因子納入模型中對於預測結果皆有幫助。最後,依據此結果進行變項重要性評估,結果發現,慢性腎臟病為影響心臟衰竭再住院最重要的危險因子,比例為19.86%,糖尿病則次之(11.78%),冠狀動脈疾病位居第三(10.82%)。影響較小則為BMI(6.0

3%)及高血壓(6.27%)。結論:依據本研究結果,納入所有危險因子之模組表現最佳,亦表示各項危險因子對於心臟衰竭再住院患者皆有其影響性。目前國內多數醫療器材廠商較難取得疾病患者原始資料,來輔助產品之優化,期望可透過本研究實際的預測結果,將各項危險因子之影響程度提供醫療器材廠商增強儀器訓練及模型校正,達到產品最佳化之精準預測能力。