Search results for: survival data analysis

Authors: Chaimaa Messaoudi, Rachida Fissoune, Hassan Badir

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Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community.

Keywords: biological ontology, linked data, semantic data integration, semantic web

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Authors: Sahar Nasr, Lin Li, Edwin Wang

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Bladder cancer (BLCA) is known as the 13th cause of death among cancer patients worldwide, and ~575,000 new BLCA cases are diagnosed each year. Urothelial carcinoma (UC) is the most prevalent subtype among BLCA patients, which can be categorized into muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC). Currently, various therapeutic options are available for UC patients, including (1) transurethral resection followed by intravesical instillation of chemotherapeutics or Bacillus Calmette-Guérin for NMIBC patients, (2) neoadjuvant platinum-based chemotherapy (NAC) plus radical cystectomy is the standard of care for localized MIBC patients, and (3) systematic chemotherapy for metastatic UC. However, conventional treatments may lead to several challenges for treating patients. As an illustration, some patients may suffer from recurrence of the disease after the first line of treatment. Recently, immune checkpoint therapy (ICT) has been introduced as an alternative treatment strategy for the first or second line of treatment in advanced or metastatic BLCA patients. Although ICT showed lucrative results for a fraction of BLCA patients, ~80% of patients were not responsive to it. Therefore, novel treatment methods are required to augment the ICI response rate within BLCA patients. It has been shown that the infiltration of T-cells into the tumor microenvironment (TME) is positively correlated with the response to ICT within cancerous patients. Therefore, the goal of this study is to enhance the infiltration of cytotoxic T-cells into TME through the identification of target genes within the tumor that are responsible for the non-T-cell inflamed TME and their inhibition. BLCA bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA) and immune score for TCGA samples were used to determine the Pearson correlation score between the expression of different genes and immune score for each sample. The genes with strong negative correlations were selected (r < -0.2). Thereafter, the correlation between the expression of each gene and survival in BLCA patients was calculated using the TCGA data and Cox regression method. The genes that are common in both selected gene lists were chosen for further analysis. Afterward, BLCA bulk and single-cell RNA-sequencing data were ranked based on the expression of each selected gene and the top and bottom 25% samples were used for pathway enrichment analysis. If the pathways related to the T-cell infiltration (e.g., antigen presentation, interferon, or chemokine pathways) were enriched within the low-expression group, the gene was included for downstream analysis. Finally, the selected genes will be used to calculate the correlation between their expression and the infiltration rate of the activated CD+8 T-cells, natural killer cells and the activated dendric cells. A list of potential target genes has been identified and ranked based on the above-mentioned analysis and criteria. SUN-1 got the highest score within the gene list and other identified genes in the literature as benchmarks. In conclusion, inhibition of SUN1 may increase the tumor-infiltrating lymphocytes and the efficacy of ICI in BLCA patients. BLCA tumor cells with and without SUN-1 CRISPR/Cas9 knockout will be injected into the syngeneic mouse model to validate the predicted SUN-1 effect on increasing tumor-infiltrating lymphocytes.

Keywords: data analysis, gene expression analysis, gene identification, immunoinformatic, functional genomics, transcriptomics

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Authors: Deniz Karagöz

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This study is attempt to comprehend the relationship between the spatial characteristics of cultural structure, activities and the tourism demand in Turkey. The analysis divided into four parts. The first part consisted of a cultural structure and cultural activity (CSCA) index provided by principal component analysis. The analysis determined four distinct dimensions, namely, cultural activity/structure, accessing culture, consumption, and cultural management. The exploratory spatial data analysis employed to determine the spatial models of cultural structure and cultural activities in 81 provinces in Turkey. Global Moran I indices is used to ascertain the cultural activities and the structural clusters. Finally, the relationship between the cultural activities/cultural structure and tourism demand was analyzed. The raw/original data of the study official databases. The data on the cultural structure and activities gathered from the Turkish Statistical Institute and the data related to the tourism demand was provided by the Republic of Turkey Ministry of Culture and Tourism.

Keywords: cultural activities, cultural structure, spatial characteristics, tourism demand, Turkey

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Authors: Christopher R. Triggle, Hong Ding, Khaled Machaca, Gnanapragasam Arunachalam

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The antidiabetic drug, metformin, has been in clinical use for over 50 years and remains the first choice drug for the treatment of type two diabetes. In addition to its effectiveness as an oral anti-hyperglycaemic drug metformin also possesses vasculoprotective effects that are assumed to be secondary to its ability to reduce insulin resistance and control glycated hemoglobin levels; however, recent data from our laboratory indicate that metformin also has direct vasoprotective effects that are mediated, at least in part, via the anti-ageing gene, SIRT1. Diabetes is a major risk factor for the development of cardiovascular disease (CVD) and it is also well established that tobacco use further enhances the risk of CVD; however, it is not known whether treatment with metformin can offset the negative effects of diabetes and tobacco use on cardiac function. The current study was therefore designed to investigate 1: the effects of hyperglycaemia (HG) either alone or in the presence of elevated fatty acids (palmitate) and nicotine on the protein expression levels of the deacetylase sirtuin 1 (the protein product of SIRT1), anti-apoptotic Bcl-2, pro-apoptotic BIM and the pro-apoptotic, tumour suppressor protein, acetylated p53 in cardiomyocytes. 2: the ability of metformin to prevent the detrimental effects of HG, palmitate and nicotine on cardiomyocyte survival. Cell culture protocols were designed using a rat cardiomyocyte cell line, H9c2, either under normal glycaemic (NG) conditions of 5.5mM glucose, or hyperglycaemic conditions (HG) of 25mM glucose with, or without, added palmitate (250μM) or nicotine (1.0mM) for 24h. Immuno-blotting was used to detect the expression of sirtuin 1, Bcl-2, BIM, acetylated (Ac)-p53, p53 with β-actin used as the reference protein. Exposure to HG, palmitate, or nicotine alone significantly reduced expression of sirtuin1, Bcl-2 and raised the expression levels of acetylated p53 and BIM; however, the combination of HG, palmitate and nicotine had a synergistic effect to significantly suppress the expression levels of sirtuin 1 and Bcl-2, but further enhanced the expression of Ac-p53, and BIM. The inclusion of 1000μM, but not 50μM, metformin in the H9c2 cell culture protocol prevented the effects of HG, palmitate and nicotine on the pro-apoptotic pathways. Collectively these data indicate that metformin, in addition to its anti-hyperglycaemic and vasculoprotective properties, also has direct cardioprotective actions that offset the negative effects of hyerglycaemia, elevated free fatty acids and nicotine on cardiac cell survival. These data are of particular significance for the treatment of patients with diabetes who are also smokers as the inclusion of metformin in their therapeutic treatment plan should help reduce cardiac-related morbidity and mortality.

Keywords: apoptosis, cardiac muscle, diabetes, metformin, nicotine

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Authors: Phan Nghiem Vu, Le Tuan Vu, Ta Quang Tai

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Knowledge Management effectively is critical to the survival and advance of a company, especially in company-based industries such as construction. Knowledge management practice is crucial to the survival and progress of a company, especially company-based knowledge such as construction consultancy. Effective knowledge management practices are very significant to the competitive and development of a consulting organization. Hence, the success of knowledge management implementation depends on knowledge capturing and reusing effectively. In this paper, a survey was carried out of engineers and managers with experience in seven construction consulting organizations that provide services on the north-central coast of Vietnam. The main objectives of the survey to finding out how these organizations capture and reuse knowledge and significant barriers to the implementation of knowledge management. A conceptual framework based-on Trello application is proposed to formalize the knowledge-capturing and reusing process within construction consulting companies. It is showed that the conceptual framework could be used to manage both implicit and explicit knowledge effectively in construction consultant organizations.

Keywords: knowledge management, construction consultant organization, knowledge capturing, reusing knowledge, application-based technology

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Authors: Mouataz Zreika, Maria Estela Varua

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Stock investment decisions are often made based on current events of the global economy and the analysis of historical data. Conversely, visual representation could assist investors’ gain deeper understanding and better insight on stock market trends more efficiently. The trend analysis is based on long-term data collection. The study adopts a hybrid method that combines the Clustering algorithm and Force-directed algorithm to overcome the scalability problem when visualizing large data. This method exemplifies the potential relationships between each stock, as well as determining the degree of strength and connectivity, which will provide investors another understanding of the stock relationship for reference. Information derived from visualization will also help them make an informed decision. The results of the experiments show that the proposed method is able to produced visualized data aesthetically by providing clearer views for connectivity and edge weights.

Keywords: clustering, force-directed, graph drawing, stock investment analysis

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Authors: Hongmeng Su, Luyu Zhao, Yanyan Qian, Hong Fan

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Aim: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors that endanger human health seriously. RNA methylation, especially N6-methyladenosine (m⁶A) and 5-methylcytosine (m⁵C), a crucial epigenetic transcriptional regulatory mechanism, plays an important role in tumorigenesis, progression and prognosis. This research aims to systematically evaluate the prognostic value of m⁶A and m⁵C modulators in HCC patients. Methods: Twenty-four modulators of m⁶A and m⁵C were candidates to analyze their expression level and their contribution to predict the prognosis of HCC. Consensus clustering analysis was applied to classify HCC patients. Cox and LASSO regression were used to construct the risk model. According to the risk score, HCC patients were divided into high-risk and low/medium-risk groups. The clinical pathology factors of HCC patients were analyzed by univariate and multivariate Cox regression analysis. Results: The HCC patients were classified into 2 clusters with significant differences in overall survival and clinical characteristics. Nine-gene risk model was constructed including METTL3, VIRMA, YTHDF1, YTHDF2, NOP2, NSUN4, NSUN5, DNMT3A and ALYREF. It was indicated that the risk score could serve as an independent prognostic factor for patients with HCC. Conclusion: This study constructed a Nine-gene risk model by modulators of m⁶A and m⁵C and investigated its effect on the clinical prognosis of HCC. This model may provide important consideration for the therapeutic strategy and prognosis evaluation analysis of patients with HCC.

Keywords: hepatocellular carcinoma, m⁶A, m⁵C, prognosis, RNA methylation

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Authors: Md. M. Hossain, Regan Roat, Jenica Christopherson, Colette Free, Zhiguang Guo

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Long noncoding RNA (lncRNA) mediated post-transcriptional gene regulation, and their epigenetic landscapes have been shown to be involved in many human diseases. However, their regulation in diabetes through governing islet’s β-cell function and survival needs to be elucidated. Due to the technical and ethical constraints, it is difficult to study their role in β-cell function and survival in human under in vivo condition. In this study, humanized mice have been developed through transplanting human pancreatic islet under the kidney capsule of NOD.SCID mice and induced β-cell death leading to diabetes condition to study lncRNA mediated regulation. For this, human islets from 3 donors (3000 IEQ, purity > 80%) were transplanted under the kidney capsule of STZ induced diabetic NOD.scid mice. After at least 2 weeks of normoglycecemia, lymphocytes from diabetic NOD mice were adoptively transferred and islet grafts were collected once blood glucose reached > 200 mg/dl. RNA from human donor islets, islet grafts from humanized mice with either adoptive lymphocyte transfer (ALT) or PBS control (CTL) were ribodepleted; barcoded fragment libraries were constructed and sequenced on the Ion Proton sequencer. lncRNA expression in isolated human islets, islet grafts from humanized mice with and without induced β-cell death and their regulation in human islets function in vitro under glucose challenge, cytokine mediated inflammation and induced apoptotic condition were investigated. Out of 3155 detected lncRNAs, 299 that highly expressed in islets were found to be significantly downregulated and 224 upregulated in ALT compared to CTL. Most of these are found to be collocated within 5 kb upstream and 1 kb downstream of 788 up- and 624 down-regulated mRNAs. Genomic Regions Enrichment of Annotations Analysis revealed deregulated and collocated genes are related to pancreas endocrine development; insulin synthesis, processing, and secretion; pancreatitis and diabetes. Many of them, that found to be located within enhancer domains for islet specific gene activity, are associated to the deregulation of known islet/βcell specific transcription factors and genes that are important for β-cell differentiation, identity, and function. RNA sequencing analysis revealed aberrant lncRNA expression which is associated to the deregulated mRNAs in β-cell function as well as in molecular pathways related to diabetes. A distinct set of candidate lncRNA isoforms were identified as highly enriched and specific to human islets, which are deregulated in human islets from donors with different BMIs and with type 2 diabetes. These RNAs show an interesting regulation in cultured human islets under glucose stimulation and with induced β-cell death by cytokines. Aberrant expression of these lncRNAs was detected in the exosomes from the media of islets cultured with cytokines. Results of this study suggest that the islet specific lncRNAs are deregulated in human islet with β-cell death, hence important in diabetes. These lncRNAs might be important for human β-cell function and survival thus could be used as biomarkers and novel therapeutic targets for diabetes.

Keywords: β-cell, humanized mouse, pancreatic islet, LncRNAs

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Authors: Zohra Mekranfar, Ahmed Saidi, Abdellah Mebrek

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This work aims to develop multi-criteria decision making (MCDM) and spatial data warehouse (SDW) methods, which will be integrated into a GIS according to a ‘GIS dominant’ approach. The GIS operating tools will be operational to operate the SDW. The MCDM methods can provide many solutions to a set of problems with various and multiple criteria. When the problem is so complex, integrating spatial dimension, it makes sense to combine the MCDM process with other approaches like data mining, ascending analyses, we present in this paper an experiment showing a geo-decisional methodology of SWD construction, On-line analytical processing (OLAP) technology which combines both basic multidimensional analysis and the concepts of data mining provides powerful tools to highlight inductions and information not obvious by traditional tools. However, these OLAP tools become more complex in the presence of the spatial dimension. The integration of OLAP with a GIS is the future geographic and spatial information solution. GIS offers advanced functions for the acquisition, storage, analysis, and display of geographic information. However, their effectiveness for complex spatial analysis is questionable due to their determinism and their decisional rigor. A prerequisite for the implementation of any analysis or exploration of spatial data requires the construction and structuring of a spatial data warehouse (SDW). This SDW must be easily usable by the GIS and by the tools offered by an OLAP system.

Keywords: data warehouse, GIS, MCDM, SOLAP

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Authors: M. I. Oshimagye, V. O. Ayuba, P. A. Annune

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The effect of Sublethal Concentrations of Parkia biglobosa pod extract on the growth and survival of Clarias gariepinus juveniles (mean weight 32.73g ± 0.0) were investigated under laboratory conditions for 8 weeks using the static renewal and continuous aeration system. Statistical analysis showed that fish exposed to various concentrations had significantly lower (P<0.05) growth rate than the control groups. The reduction in growth was observed to be directly proportional to increase in concentration. However, at 50 mg/L no significant depression in weight was observed.

Keywords: Clarias gariepinus, Parkia biglobosa, pod, weight

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Authors: Adrian Hang Yue Siu, Matthew Holyland, Sharon Carey, Daniel Steffens, Nabila Ansari, Cherry E. Koh

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Introduction: Peritoneal malignancies are challenging cancers to manage. While cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS and HIPEC) may offer a cure, it’s considered radical and morbid. Pre-emptive identification of deconditioned patients for optimization may mitigate the risks of surgery. However, the difficulty lies in the scarcity of validated predictive tools to identify high-risk patients. In recent times, there has been growing interest in sarcopenia, which can occur as a result of malnutrition and malignancies. Therefore, the purpose of this study was to assess the utility of sarcopenia in predicting post-operative outcomes. Methods: A single quaternary-center retrospective study of CRS and HIPEC patients between 2017-2020 was conducted to determine the association between pre-operative sarcopenia and post-operative outcomes. Lumbar CT images were analyzed using Slice-o-matic® to measure sarcopenia. Results : Cohort (n=94) analysis found that 40% had sarcopenia, with a majority being female (53.2%) and a mean age of 55 years. Sarcopenia was statistically associated with decreased weight compared to non-sarcopenia patients, 72.7kg vs. 82.2kg (p=0.014) and shorter overall survival, 1.4 years vs. 2.1 years (p=0.032). Post-operatively, patients with sarcopenia experienced more post-operative complications (p=0.001). Conclusion: Complex procedures often require optimization to prevent complications and improve survival. While patient biomarkers – BMI and weight – are used for optimization, this research advocates for the identification of sarcopenia status for pre-operative planning. Sarcopenia may be an indicator of advanced disease requiring further treatment and is an emerging area of research. Larger studies are required to confirm these findings and to assess the reversibility of sarcopenia after surgery.

Keywords: sarcopaenia, cytoreductive surgery, hyperthermic intraperitoneal chemotherapy, surgical oncology

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Authors: Raphael Udeh, Luis García De Guadiana Romualdo, Xenia Dolje-Gore

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Background: Quite disturbing is the huge public health impact of COVID-19: As at today [25th March 2021, the COVID-19 global burden shows over 123 million cases and over 2.7 million deaths worldwide. Rationale: Recent evidence shows calprotectin’s potential as a therapeutic target, stating that tasquinimod, from the Quinoline-3-Carboxamide family is capable of blocking the interaction between calprotectin and TLR4. Hence preventing the cytokine release syndrome, that heralds the functional exhaustion in COVID-19. Early preclinical studies showed that tasquinimod inhibit tumor growth and prevent angiogenesis/cytokine storm. Phase I – III clinical studies in prostate cancer showed it has a good safety profile with good radiologic progression free survival but no effect on overall survival. Rationale/hypothesis: Strategic endeavors have been amplified globally to assess new therapeutic interventions for COVID-19 management – thus the clinical and antiviral efficacy of tasquinimod in COVID-19 remains to be explored. Hence the primary objective of this trial will be to evaluate the efficacy of tasquinimod in the treatment of adult patients with severe COVID-19 infections. Therefore, I hypothesise that among adults with COVID19 infection, tasquinimod will reduce the severe respiratory distress associated with COVID-19 compared to placebo, over a 28-day study period. Method: The setting is in Europe. Design – a randomized, placebo-controlled, phase II double-blinded trial. Trial lasts for 28 days from randomization, Tasquinimod capsule given as 0.5mg daily 1st fortnight, then 1mg daily 2nd fortnight. I0 outcome - assessed using six-point ordinal scale alongside eight 20 outcomes. 125 participants to be enrolled, data collection at baseline and subsequent data points, and safety reporting monitored via serological profile. Significance: This work could potentially establish tasquinimod as an effective and safe therapeutic agent for COVID-19 by reducing the severe respiratory distress, related time to recovery, time on oxygen/admission. It will also drive future research – as in larger multi-centre RCT.

Keywords: Calprotectin, COVID-19, Phase II Trial, Tasquinimod

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Authors: Stepan Papacek, Jiri Jablonsky, Radek Kana, Ctirad Matonoha, Stefan Kindermann

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FRAP (Fluorescence Recovery After Photobleaching) is a widely used measurement technique to determine the mobility of fluorescent molecules within living cells. While the experimental setup and protocol for FRAP experiments are usually fixed, data processing part is still under development. In this paper, we formulate and solve the problem of data selection which enhances the processing of FRAP images. We introduce the concept of the irrelevant data set, i.e., the data which are almost not reducing the confidence interval of the estimated parameters and thus could be neglected. Based on sensitivity analysis, we both solve the problem of the optimal data space selection and we find specific conditions for optimizing an important experimental design factor, e.g., the radius of bleach spot. Finally, a theorem announcing less precision of the integrated data approach compared to the full data case is proven; i.e., we claim that the data set represented by the FRAP recovery curve lead to a larger confidence interval compared to the spatio-temporal (full) data.

Keywords: FRAP, inverse problem, parameter identification, sensitivity analysis, optimal experimental design

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Authors: Hamideh Nouri, Sattar Chavoshi Borujeni

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Grey infrastructure is an unavoidable part of urbanisation that is threatening the local microclimates. Sustainable urbanisation requires more green infrastructure in cities such as green roofs to minimise urbanisation impacts. The environmental, social and economic benefits of green roofs are widely deliberated. However, there is still a lack of assessment of the water management for green roofs. This paper aimed to assess the irrigation management of green roofs in a semi-arid region where blue water scarcity is one of the primary challenges in urban water management. To determine the appropriate water source and growing media for green roofs, an experiment was established at the University of South Australia, Australia. This study compared the performance of two growing media and three water sources on the drainage quality, medium weight and survival rate of potted Tussock grass (Poa labillardieral), an endemic plant to Australia and recommended for green roofs. Three irrigation sources were tap water, mixed of wastewater-stormwater, and rainwater. The growing media were natural sandy loam soil and Scoria - one of the most used commercial growing media for green roofs. The drainage quality of these media was tested by analysing leachate samples. Medium weight was measured before and after watering, and all pots were monitored for their survival rates. Results showed that although plant growing development was significantly higher in Scoria, the survival rate was lower. For all three water sources, EC and pH of the leachate were significantly lower from Scoria than the sandy loam soil. However, the mixed of wastewater-stormwater had the highest EC, and rainwater had the lowest EC. Results did not present a significant difference between pH of different water resources in the same media. Our experimental results found the scoria and rainwater as the best sources of medium and water for green roofs.

Keywords: green smart cities, urban water, green roofs, green walls, wastewater, stormwater

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Authors: Chernet Mulugeta, Girma Seyoum, Yeshineh Demrew, Kehabtimer Shiferaw

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Background: Obstetric fistula (OF) is a serious medical condition that includes an abnormal opening between the vagina and bladder (vesico-vaginal fistula) or the vagina and rectum (recto-vaginal fistula). It is usually caused by prolonged obstructed labour. Despite its serious health and psychosocial consequences, there is a paucity of evidence regarding the time it takes to heal from OF. Objective: The aim of this study was to assess the time to cure from obstetric fistula and its predictors among women admitted to Addis Ababa Hamlin Fistula Hospital, Addis Ababa, Ethiopia. Methodology: An institution-based retrospective cohort study was conducted from January 2015 to December 2020 among a randomly selected 434 women with OF in Addis Ababa Hamlin Fistula Hospital. Data was collected using a structured checklist adapted from a similar study. The open data kit (ODK) collected data was exported and analyzed by using STATA (14.2). Kaplan Meir was used to compare the recovery time from OF. To identify the predictors of OF, a Cox regression model was fitted, and an adjusted hazard ratio with a 95% confidence interval was used to estimate the strength of the associations. Results: The average time to recover from obstetric fistula was 3.95 (95% CI: 3.0-4.6) weeks. About ¾ of the women [72.8% (95% CI - 0.65-1.2)] were physically cured of obstetric fistula. Having secondary education and above [AHR=3.52; 95% CI (1.98, 6.25)] compared to no formal education, having a live birth [AHR=1.64; 95% CI (1.22, 2.21)], having an intact bladder [AHR=2.47; 95% CI (1.1, 5.54)] compared to totally destructed, and having a grade 1 fistula [AHR=1.98; 95% CI (1.19, 3.31)] compared to grade 3 were the significant predictors of shorter time to cure from an obstetric fistula. Conclusion and recommendation: Overall, the proportion of women with OF who were not being cured was unacceptably high. The time it takes for them to recover from the fistula was also extended. It connotes us to work on the identified predictors to improve the time to recovery from OF.

Keywords: time to recovery, obstetric fistula, predictors, Ethiopia

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Authors: Cheng-Cao Sun, Shu-Jun Li, De-Jia Li

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Long non-coding RNA (lncRNA) X inactivate-specific transcript (XIST) has been verified as an oncogenic gene in several human malignant tumors, and its dysregulation was closed associated with tumor initiation, development and progression. Nevertheless, whether the aberrant expression of XIST in human nasopharyngeal carcinoma (NPC) is corrected with malignancy, metastasis or prognosis has not been elaborated. Here, we discovered that XIST was up-regulated in NPC tissues and higher expression of XIST contributed to a markedly poorer survival time. In addition, multivariate analysis demonstrated XIST was an independent risk factor for prognosis. XIST over-expression enhanced, while XIST silencing hampered the cell growth in NPC. Additionally, mechanistic analysis revealed that XIST up-regulated the expression of miR-34a-5p targeted gene E2F3 through acting as a competitive ‘sponge’ of miR-34a-5p. Taking all into account, we concluded that XIST functioned as an oncogene in NPC through up-regulating E2F3 in part through ‘spongeing’ miR-34a-5p.

Keywords: X inactivate-specific transcript; hsa-miRNA-34a-5p, miR-34a-5p; E2F3, nasopharyngeal carcinoma, tumorigenesis

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Authors: Jatin Kumar Choudhary, Naren Rayala, Abbas Eslami Kiasari, Fahimeh Jafari

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The Great Britain has one of the safest road networks in the world. However, the consequences of any death or serious injury are devastating for loved ones, as well as for those who help the severely injured. This paper aims to analyse the Great Britain's road safety situation and show the response measures for areas where the total damage caused by accidents can be significantly and quickly reduced. In this paper, we do an exploratory data analysis using STATS19 data. For the past 30 years, the UK has had a good record in reducing fatalities. The UK ranked third based on the number of road deaths per million inhabitants. There were around 165,000 accidents reported in the Great Britain in 2009 and it has been decreasing every year until 2019 which is under 120,000. The government continues to scale back road deaths empowering responsible road users by identifying and prosecuting the parameters that make the roads less safe.

Keywords: road safety, data analysis, openstreetmap, feature expanding.

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Authors: Taner Ersoz, Filiz Ersoz

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This study was designed enable application of multivariate technique in the interpretation of categorical data for measuring health care services satisfaction in Turkey. The data was collected from a total of 17726 respondents. The establishment of the sample group and collection of the data were carried out by a joint team from The Ministry of Health and Turkish Statistical Institute (Turk Stat) of Turkey. The multiple correspondence analysis (MCA) was used on the data of 2882 respondents who answered the questionnaire in full. The multiple correspondence analysis indicated that, in the evaluation of health services females, public employees, younger and more highly educated individuals were more concerned and complainant than males, private sector employees, older and less educated individuals. Overall 53 % of the respondents were pleased with the improvements in health care services in the past three years. This study demonstrates the public consciousness in health services and health care satisfaction in Turkey. It was found that most the respondents were pleased with the improvements in health care services over the past three years. Awareness of health service quality increases with education levels. Older individuals and males would appear to have lower expectancies in health services.

Keywords: multiple correspondence analysis, multivariate categorical data, health care services, health satisfaction survey

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Authors: Mahdi Bazarganigilani

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Computer-aided diagnosis (CAD) systems can play crucial roles in diagnosing crucial diseases such as breast cancer at the earliest. In this paper, a CAD system for the diagnosis of breast cancer was introduced and evaluated. This CAD system was developed by using spatio-temporal analysis of data on a set of consecutive thermographic images by employing wavelet transformation. By using this analysis, a very accurate machine learning model using random forest was obtained. The final results showed a promising accuracy of 91% in terms of the F1 measure indicator among 200 patients' sample data. The CAD system was further extended to obtain a detailed analysis of the effect of smaller sub-areas of each breast on the occurrence of cancer.

Keywords: computer-aided diagnosis systems, thermographic analysis, spatio-temporal analysis, image processing, machine learning

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Authors: M. A. Wanzala, L. A. Ogallo, F. J. Opijah, J. N. Mutemi

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The usefulness and limitations in climate information are due to uncertainty inherent in the climate system. For any given region to have sustainable development it is important to apply climate information into its socio-economic strategic plans. The overall objective of the study was to assess the performance of the Coupled Model Inter-comparison Project (CMIP5) over the Lake Victoria Basin. The datasets used included the observed point station data, gridded rainfall data from Climate Research Unit (CRU) and hindcast data from eight CMIP5. The methodology included trend analysis, spatial analysis, correlation analysis, Principal Component Analysis (PCA) regression analysis, and categorical statistical skill score. Analysis of the trends in the observed rainfall records indicated an increase in rainfall variability both in space and time for all the seasons. The spatial patterns of the individual models output from the models of MPI, MIROC, EC-EARTH and CNRM were closest to the observed rainfall patterns.

Keywords: categorical statistics, coupled model inter-comparison project, principal component analysis, statistical downscaling

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Authors: K. Hardy, A. Maurushat

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Policy makers are increasingly looking to make evidence-based decisions. Evidence-based decisions have historically used rigorous methodologies of empirical studies by research institutes, as well as less reliable immediate survey/polls often with limited sample sizes. As we move into the era of Big Data analytics, policy makers are looking to different methodologies to deliver reliable empirics in real-time. The question is not why did these people do this for the last 10 years, but why are these people doing this now, and if the this is undesirable, and how can we have an impact to promote change immediately. Big data analytics rely heavily on government data that has been released in to the public domain. The open data movement promises greater productivity and more efficient delivery of services; however, Australian government agencies remain reluctant to release their data to the general public. This paper considers the barriers to releasing government data as open data, and how these barriers might be overcome.

Keywords: big data, open data, productivity, data governance

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Authors: Isha Saxena, Behzad Kazemtabrizi, Matthias C. M. Troffaes, Christopher Crabtree

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The calculations done at the beginning of the life of a wind farm are rarely reliable, which makes it important to conduct research and study the failure and repair rates of the wind turbines under various conditions. This miscalculation happens because the current models make a simplifying assumption that the failure/repair rate remains constant over time. This means that the reliability function is exponential in nature. This research aims to create a more accurate model using sensory data and a data-driven approach. The data cleaning and data processing is done by comparing the Power Curve data of the wind turbines with SCADA data. This is then converted to times to repair and times to failure timeseries data. Several different mathematical functions are fitted to the times to failure and times to repair data of the wind turbine components using Maximum Likelihood Estimation and the Posterior expectation method for Bayesian Parameter Estimation. Initial results indicate that two parameter Weibull function and exponential function produce almost identical results. Further analysis is being done using the complex system analysis considering the failures of each electrical and mechanical component of the wind turbine. The aim of this project is to perform a more accurate reliability analysis that can be helpful for the engineers to schedule maintenance and repairs to decrease the downtime of the turbine.

Keywords: reliability, bayesian parameter inference, maximum likelihood estimation, weibull function, SCADA data

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Authors: Hamidreza Namazi

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Fractal analysis is assessing fractal characteristics of data. It consists of several methods to assign fractal characteristics to a dataset which may be a theoretical dataset or a pattern or signal extracted from phenomena including natural geometric objects, sound, market fluctuations, heart rates, digital images, molecular motion, networks, etc. Fractal analysis is now widely used in all areas of science. An important limitation of fractal analysis is that arriving at an empirically determined fractal dimension does not necessarily prove that a pattern is fractal; rather, other essential characteristics have to be considered. For this purpose a Visual C++ based software called FRATSAN (FRActal Time Series ANalyser) was developed which extract information from signals through three measures. These measures are Fractal Dimensions, Jeffrey’s Measure and Hurst Exponent. After computing these measures, the software plots the graphs for each measure. Besides computing three measures the software can classify whether the signal is fractal or no. In fact, the software uses a dynamic method of analysis for all the measures. A sliding window is selected with a value equal to 10% of the total number of data entries. This sliding window is moved one data entry at a time to obtain all the measures. This makes the computation very sensitive to slight changes in data, thereby giving the user an acute analysis of the data. In order to test the performance of this software a set of EEG signals was given as input and the results were computed and plotted. This software is useful not only for fundamental fractal analysis of signals but can be used for other purposes. For instance by analyzing the Hurst exponent plot of a given EEG signal in patients with epilepsy the onset of seizure can be predicted by noticing the sudden changes in the plot.

Keywords: EEG signals, fractal analysis, fractal dimension, hurst exponent, Jeffrey’s measure

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Authors: R. Shamsi, F. Sharifi

Abstract:

In this paper, we obtain the projection of inefficient units in data envelopment analysis (DEA) in the case of stochastic inputs and outputs using the multi-objective programming (MOP) structure. In some problems, the inputs might be stochastic while the outputs are deterministic, and vice versa. In such cases, we propose a multi-objective DEA-R model because in some cases (e.g., when unnecessary and irrational weights by the BCC model reduce the efficiency score), an efficient decision-making unit (DMU) is introduced as inefficient by the BCC model, whereas the DMU is considered efficient by the DEA-R model. In some other cases, only the ratio of stochastic data may be available (e.g., the ratio of stochastic inputs to stochastic outputs). Thus, we provide a multi-objective DEA model without explicit outputs and prove that the input-oriented MOP DEA-R model in the invariable return to scale case can be replaced by the MOP-DEA model without explicit outputs in the variable return to scale and vice versa. Using the interactive methods for solving the proposed model yields a projection corresponding to the viewpoint of the DM and the analyst, which is nearer to reality and more practical. Finally, an application is provided.

Keywords: DEA-R, multi-objective programming, stochastic data, data envelopment analysis

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Authors: Iruka C. Anugwo, Winston M. Shakantu

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The objective of this study is to uncover the basic business-forces that necessitated the survival and sustainable performance of the medium scale contractors in the South African construction market. This study is essential as it set to contribute towards long-term strategic solutions for combating the incessant failure of start-ups construction organizations within South African. The study used a qualitative research methodology; as the most appropriate approach to elicit and understand, and uncover the phenomena that are basic business-forces for the active contractors in the market. The study also adopted a phenomenological study approach; and in-depth interviews were conducted with 20 medium scale contractors in Port Elizabeth, South Africa, between months of August to October 2015. This allowed for an in-depth understanding of the critical and basic business-forces that influenced their survival and performance beyond the first five years of business operation. Findings of the study showed that for potential contractors (startups), to survival in the competitive business environment such as construction industry, they must possess the basic business-forces. These forces are educational knowledge in construction and business management related disciplines, adequate industrial experiences, competencies and capabilities to delivery excellent services and products as well as embracing the spirit of entrepreneurship. Convincingly, it can be concluded that the strategic approach to minimize the endless failure of startups construction businesses; the potential construction contractors must endeavoring to access and acquire the basic educationally knowledge, training and qualification; need to acquire industrial experiences in collaboration with required competencies, capabilities and entrepreneurship acumen. Without these basic business-forces as been discovered in this study, the majority of the contractors gaining entrance in the market will find it difficult to develop and grow a competitive and sustainable construction organization in South Africa.

Keywords: basic business-forces, medium scale contractors, South Africa, sustainable organisations

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Authors: Gizem Kodak

Abstract:

The Strait of Çanakkale, together with the Strait of Istanbul and the Sea of Marmara, form the Turkish Straits System. In other words, the Strait of Çanakkale is the southern gate of the system that connects the Black Sea countries with the other countries of the world. Due to the heavy maritime traffic, it is important to scientifically examine the accident characteristics in the region. In particular, the results indicated by the descriptive statistics are of critical importance in order to strengthen the safety of navigation. At this point, exploratory data analysis offers strategic outputs in terms of defining the problem and knowing the strengths and weaknesses against possible accident risk. The study aims to determine the accident characteristics in the Strait of Çanakkale with temporal and spatial analysis of historical data, using Exploratory Data Analysis (EDA) as the research method. The study's results will reveal the general characteristics of maritime accidents in the region and form the infrastructure for future studies. Therefore, the text provides a clear description of the research goals and methodology, and the study's contributions are well-defined.

Keywords: maritime accidents, EDA, Strait of Çanakkale, navigational safety

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Authors: Éva Pintér, Péter Bagó, Nikolett Deutsch, Miklós Hetényi

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The process of digital transformation has a profound influence on all sectors of the worldwide economy and the business environment. The influence of blockchain technology can be observed in the digital economy and e-government, rendering it an essential element of a nation's growth strategy. The banking industry is experiencing significant expansion and development of financial technology firms. Utilizing developing technologies such as artificial intelligence (AI), machine learning (ML), and big data (BD), these entrants are offering more streamlined financial solutions, promptly addressing client demands, and presenting a challenge to incumbent institutions. The advantages of digital transformation are evident in the corporate realm, and firms that resist its adoption put their survival at risk. The advent of digital technologies has revolutionized the business environment, streamlining processes and creating opportunities for enhanced communication and collaboration. Thanks to the aid of digital technologies, businesses can now swiftly and effortlessly retrieve vast quantities of information, all the while accelerating the process of creating new and improved products and services. Big data analytics is generally recognized as a transformative force in business, considered the fourth paradigm of science, and seen as the next frontier for innovation, competition, and productivity. Big data, an emerging technology that is shaping the future of the banking sector, offers numerous advantages to banks. It enables them to effectively track consumer behavior and make informed decisions, thereby enhancing their operational efficiency. Banks may embrace big data technologies to promptly and efficiently identify fraud, as well as gain insights into client preferences, which can then be leveraged to create better-tailored products and services. Moreover, the utilization of big data technology empowers banks to develop more intelligent and streamlined models for accurately recognizing and focusing on the suitable clientele with pertinent offers. There is a scarcity of research on big data analytics in the banking industry, with the majority of existing studies only examining the advantages and prospects associated with big data. Although big data technologies are crucial, there is a dearth of empirical evidence about the role of big data analytics (BDA) capabilities in bank performance. This research addresses a gap in the existing literature by introducing a model that combines the resource-based view (RBV), the technical organization environment framework (TOE), and dynamic capability theory (DC). This study investigates the influence of Big Data Analytics (BDA) utilization on the performance of market and risk management. This is supported by a comparative examination of Hungarian mobile banking services.

Keywords: big data, digital transformation, dynamic capabilities, mobile banking

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Authors: Anung Style Bukhori, Ani Dijah Rahajoe

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Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below.

Keywords: poverty, classification, naïve bayes, Indonesia

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Authors: Dale Dzemydiene, Aurelija Burinskiene, Arunas Miliauskas, Kristina Ciziuniene

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Data flow and the purpose of reporting the data are different and dependent on business needs. Different parameters are reported and transferred regularly during freight delivery. This business practices form the dataset constructed for each time point and contain all required information for freight moving decisions. As a significant amount of these data is used for various purposes, an integrating methodological approach must be developed to respond to the indicated problem. The proposed methodology contains several steps: (1) collecting context data sets and data validation; (2) multi-objective analysis for optimizing freight transfer services. For data validation, the study involves Grubbs outliers analysis, particularly for data cleaning and the identification of statistical significance of data reporting event cases. The Grubbs test is often used as it measures one external value at a time exceeding the boundaries of standard normal distribution. In the study area, the test was not widely applied by authors, except when the Grubbs test for outlier detection was used to identify outsiders in fuel consumption data. In the study, the authors applied the method with a confidence level of 99%. For the multi-objective analysis, the authors would like to select the forms of construction of the genetic algorithms, which have more possibilities to extract the best solution. For freight delivery management, the schemas of genetic algorithms' structure are used as a more effective technique. Due to that, the adaptable genetic algorithm is applied for the description of choosing process of the effective transportation corridor. In this study, the multi-objective genetic algorithm methods are used to optimize the data evaluation and select the appropriate transport corridor. The authors suggest a methodology for the multi-objective analysis, which evaluates collected context data sets and uses this evaluation to determine a delivery corridor for freight transfer service in the multi-modal transportation network. In the multi-objective analysis, authors include safety components, the number of accidents a year, and freight delivery time in the multi-modal transportation network. The proposed methodology has practical value in the management of multi-modal transportation processes.

Keywords: multi-objective, analysis, data flow, freight delivery, methodology

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Authors: Seyong Jeong, Jinmo Park, Jinhyoun Park, Boram Kim, Kyoungsoo Ahn

Abstract:

Energy management in the maritime industry is being required by economics and in conformity with new legislative actions taken by the International Maritime Organization (IMO) and the European Union (EU). In response, the various performance monitoring methodologies and data collection practices have been examined by different stakeholders. While many assorted advancements in operation and technology are applicable, their adoption in the shipping industry stays small. This slow uptake can be considered due to many different barriers such as data analysis problems, misreported data, and feedback problems, etc. This study presents a conceptual design of an energy management system (EMS) and proposes the methodology to resolve the limitations (e.g., data normalization using naval architectural evaluation, management of misrepresented data, and feedback from shore to ship through management of performance analysis history). We expect this system to make even short-term charterers assess the ship performance properly and implement sustainable fleet control.

Keywords: data normalization, energy management system, naval architectural evaluation, ship performance analysis

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