FogLearn: Leveraging Fog-based Machine Learning for Smart System Big Data Analytics

Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This paper discussed the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This paper proposed and developed fog computing based framework i.e. FogLearn for application of K-means clustering in Ganga River Basin Management and realworld feature data for detecting diabetes patients suffering from diabetes mellitus. Proposed architecture employed machine learning on deep learning framework for analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results showed that fog computing hold an immense promise for analysis of medical and geospatial big data

Risk of high-risk human papillomavirus infection and cervical precancerous lesions with past or current trichomonas infection: a pooled analysis of 25,054 women in rural China

Background: Trichomonas vaginitis (TV) infection has obviously been implicated in gynecological morbidity but still unclear in cervical lesions. Objective: To evaluate the risk of hr-HPV infection and cervical intraepithelial neoplasia grade 2 or worse (CIN2 +) by TV infection. Study design: The pooled study was conducted among 12 population-based, cervical cancer screening studies throughout China (N = 24,054). HPV was detected by Hybrid Capture®2 (HC2) test. Past TV infection was measured by self-reporting, current TV infection was diagnosed by liquid-based cytology (LBC), cervical lesions was diagnosed by histopathology. Results: Respective prevalence of hr-HPV and CIN2+ were 17.4% and 3.3%. Out of 24,054 women, 14.6% reported past TV infection, and out of 11,853 women, 9.9% had current TV infection. Current TV-positive women had an increased risk for hr-HPV (OR 1.31, 95%CI: 1.11-1.56). The risk of CIN2+ decreased for hr-HPV positive women with current TV infection (adjusted OR 0.50, 95% CI: 0.30-0.84) and past TV infection (adjusted OR 0.68, 95% CI: 0.54-0.86). Among hr-HPV negative women, no significant associations were observed between past or current TV infection and risk of CIN2+. Conclusions: Women infected with HPV are more likely to be infected by other types of sexually transmitted diseases. Current TV-positive women had an increased risk for hr-HPV infection compared to currently TV-negative women. Both past and current TV-positive women had a decreased risk for CIN2+, especially among high-risk HPV positive women. More direct investigation into the interaction between TV, HPV, inflammatory signals, and risk of carcinogenesis are further needed

Clustering and 5G-enabled smart cities: a survey of clustering schemes in VANETs

This chapter highlights the importance of Vehicular Ad-hoc Networks (VANETs) in the context of the 5Genabled smarter cities and roads, a topic that attracts significant interest. In order for VANETs and its associated applications to become a reality, a very promising avenue is to bring together multiple wireless technologies in the architectural design. 5G is envisioned to have a heterogeneous network architecture. Clustering is employed in designing optimal VANET architectures that successfully use different technologies, therefore clustering has the potential to play an important role in the 5G-VANET enabled solutions. This chapter presents a survey of clustering approaches in the VANET research area. The survey provides a general classification of the clustering algorithms, presents some of the most advanced and latest algorithms in VANETs, and it is among the fewest works in the literature that reviews the performance assessment of clustering algorithms

Proteomic and systematic analysis of sodium azide induced blast disease resistant mutants of Tainung 67 rice variety

稻熱病是水稻主要病害，廣布全球水稻栽培地區，由真菌Magnaporthe oryzae所引起，危害時，會造成產量嚴重損失及稻穀品質降低。目前的稻熱病抗性研究主要是尋找及定位Pi抗性基因座，已成功選殖的Pi基因座大都編碼NBS-LRR蛋白，能辨識稻熱病菌致病蛋白，啟動局部過敏反應，傳遞抗病訊息，誘導防禦反應基因；此種植物先天免疫系統的訊息傳遞是透過蛋白質轉譯後修飾；因此，利用蛋白質體分析水稻稻熱病抗性是最直接的研究方法。由於已知Pi基因完全符合gene-for-gene理論，導致現有Pi基因座的研究及應用無法跳脫R基因抗性理論架構，使得目前稻熱病抗性育種的成效不佳。從稻熱病極感品種臺農67號疊氮化鈉突變庫中，篩選出廣幅抗性突變品系SA0009及SA0169，這二個穩定突變品系與誘變親非常相似，可視為臺農67號的近同源品系。由於突變品系SA0009及SA0169對稻熱病菌呈現極抗反應，而且與極感臺農67號的基因組非常相近，因此，本研究使用極抗突變品系SA0009及SA0169與極感誘變親臺農67號進行全蛋白質體差異性分析時，不會有太多背景因素的干擾，較能獲得與稻熱病抗性直接或相關蛋白。為了能獲得符合抗性反應的二維電泳蛋白質點，不但使用中抗及中感反應品種(系)做為實驗正控制組，更將蛋白質點的表現量常規化，同時使用表現群組分析篩選523個二維電泳蛋白質點，獲得17個抗性與12個感性反應蛋白質點。另選定53個非預期表現群組蛋白質點，一共四種稻熱病抗性反應材料的82個不同位置蛋白質點進行質譜身份鑑定，獲得源自1967個獨特編碼蛋白質(SwissProt unique protein)的5338個鑑定蛋白質(identifier)。將每個獨特編碼蛋白質的鑑定次數視為表現量，分析蛋白質常規化表現量的表現群組，並選出具差異性表現蛋白質，獲得14個抗性與22個感性蛋白，其中抗性蛋白F-box/LRR-repeat protein At4g14103、Auxin response factor 2、Ethylene-responsive transcription factor ESR1及E3 ubiquitin-protein ligase ATL6與逆境反應及防禦系統訊號傳遞有關，感性蛋白MLP-like protein 423及Cysteine-rich repeat secretory protein 39與感病有關，而感性蛋白Serine/threonine-protein phosphatase PP2A-5 catalytic subunit及Protein SPA1-RELATED 4則與逆境反應及防禦系統訊號傳遞有關。經註解1967個獨特編碼蛋白質，共發現199種生物功能，可分為細胞殺傷、細胞死亡、逆境反應、刺激反應、訊息傳遞、基因表現代謝作用、細胞作用、細胞結構組織及未知功能等10大類。預期表現群組蛋白質點具有較多自發性程式細胞死亡及逆境反應等抗病作用相關蛋白，所以，由不同稻熱病抗性反應材料的二維電泳蛋白質點表現量可以直接鏈結抗性反應特徵，甚至找到抗、感病蛋白，亦可做為稻熱病抗性篩選生物標誌。由生物功能常規化表現程度的特徵分析及差異性分析與蛋白質常規化表現程度特徵分析，可建立預期表現群組抗感性蛋白質清單，有145個抗性與171個感性蛋白，分別有132個抗性與163個感性反應對應水稻基因座，未來可做為突變品系SA0009及SA0169的稻熱病抗性育種選拔之分子標誌。由蛋白質體-基因體整合圖譜得知，這295個稻熱病抗感反應水稻基因座的分布位置與已知Pi基因座不同，證實突變品系SA0009及SA0169的抗性非已知抗性基因座。由上述各項分析得知，極抗突變品系SA0009及SA0169的抗性為非典型NBS-LRR或LRR kinase蛋白的防禦素(defensin)蛋白，能主動殺死微生物，具有廣幅抗性，不會啟動下游防禦PR基因；本研究是第一個在水稻發現水稻防禦素具稻熱病抗性。Rice blast, caused by the fungus Magnaporthe oryzae, is one of the devastating epidemics in all rice-growing regions around the world. This fungus can attack rice plant at any growth stage and cause severe yield loss and grain quality reduction which becomes a serious economic and humanitarian issue. The studies of rice blast resistance are focused on screening and mapping resistant Pi locus. Most of cloned Pi loci are encoded NBS-LRR; indeed, that can recognize M. oryzae Avr proteins, introduce local hypersensitive response, and then transmit signal to induce defense response genes. The signal transduction of plant innate immune system is performed by post-translational modification, hence proteomic analysis is the best method and strategy to study rice blast resistance. The study and application of Pi loci is required to conform R gene’s behavior to follow gene-for-gene theory, and consequently it is difficult to improve rice blast resistance. Sodium azide mutant lines SA0009 and SA0169 carried broad-spectrum resistance to rice blast fungus are generated from high susceptible rice variety Tainung 67 (TNG67) and can be considered rice blast resistant near-isogenic lines (NILs) of TNG67 due to they are very similar from phenotype to genotype. In this study, the differentially expressed proteomic analysis between high resistant mutants and high susceptible wild type can easily discover rice blast resistant protein or involved protein under less background interference. In order to obtain disease responsive 2-DE protein spot, the positive control of moderate resistant and susceptible materials, the normalization of protein spot volume, and the expression profile analysis are used to study 523 2-DE protein spots. Besides 17 resistant and 12 susceptible protein spots are obtained, 53 protein spots with undesirable expression profile are chosen. Total 82 protein spots on different position collected from 4 different rice blast reactions are executed by protein mass spectrometric identification. Total 5338 identifier derived from 1967 SwissProt unique proteins are successfully indentified, and the amount of unique proteins is assigned as protein signal volume. The expression profile of normalized protein signal volume is estimated to find out differentially expressed protein including 14 resistant and 22 susceptible proteins. Resistant proteins F-box/LRR-repeat protein At4g14103, Auxin response factor 2, Ethylene-responsive transcription factor ESR1 and E3 ubiquitin-protein ligase ATL6 are involved in stress response and defense signal transduction. Susceptible proteins MLP-like protein 423 and Cysteine-rich repeat secretory protein 39 participate with disease susceptible and Serine/threonine-protein phosphatase PP2A-5 catalytic subunit及Protein SPA1-RELATED 4 are also involved in stress response and defense signal transduction. After annotated 1967 unique proteins, 199 biological functions of 10 catalogues are discovered including cell killing, cell death, response to stress, response to stimulus, signal transduction, gene expression, metabolic process, cellular process, cellular component organization, and unknown. Because protein spots with desirable expression profile contain more programmed cell death and stress response proteins involved in disease resistance, the expression profile of protein spot volume can directly indicate rice blast reaction, and furthermore rice blast resistant and/or susceptible proteins are discovered to be biomarker. According to the biological function and protein’s normalized expression level analysis, the list of resistant and susceptible proteins carried desirable expression profile is established and contains 145 resistant and 171 susceptible proteins which’s corresponding rice loci are 132 resistant and 63 susceptible. These 295 rice blast resistant and susceptible corresponding rice loci can be designed as resistance selection molecular marker of mutants SA0009 and SA0169 in breeding program and demonstrates that the resistance of mutants SA0009 and SA0169 is different to known Pi loci in accordance with proteomic-genomics integrative map. Based on all evidences, the resistance of mutants SA0009 and SA0169 is defensin, which is untypical R protein encoded NBS-LRR or LRR kinase, actively kills microorganism, has broad-spectrum resistance to different isolates and even pathogens, but does not trigger downstream defense gene, like PR gene. Finally, this study is first report about rice defensin carried rice blast resistance.中文摘要··············································································································· i 英文摘要··············································································································· iii 目次······················································································································· v 表目次··················································································································· vi 圖目次··················································································································· vii 第一章 前言········································································································· 1 第二章 前人研究································································································· 3 一、水稻稻熱病菌Magnaporthe oryzae之研究················································ 4 二、水稻稻熱病抗性之遺傳研究····································································· 9 三、水稻稻熱病抗性之轉錄體研究································································· 12 四、水稻稻熱病抗性之蛋白質體研究····························································· 16 第三章 水稻臺農67號疊氮化鈉稻熱病抗性突變體之蛋白質體研究············· 24 一、前言············································································································· 25 二、材料與方法································································································· 26 三、結果············································································································· 32 四、討論············································································································· 37 第四章 水稻臺農67號疊氮化鈉稻熱病抗性突變體之系統性分析················· 67 一、前言············································································································· 68 二、材料與方法································································································· 69 三、結果············································································································· 72 四、討論············································································································· 78 第五章 結論········································································································· 101 第六章 參考文獻································································································· 104 附錄······················································································································· 13