scholarly journals Estimation of the Oil-in-Place Resources in the Liquid-Rich Shale Formations Exploiting Geochemical and Petrophysical Data in a 3D High-Resolution Geological Model Domain: Baltic Basin Case Study

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Krzysztof Sowiżdżał ◽  
Tomasz Słoczyński ◽  
Weronika Kaczmarczyk
Keyword(s):  
Early Stage ◽  
Rock Properties ◽  
Geological Model ◽  
Baltic Basin ◽  
Data Set ◽  
Shale Formations ◽  
Exploration Process ◽  
Geochemical Properties ◽  
Rock Eval ◽  
Geochemical Method

The paper discusses the issue of oil-in-place estimation for liquid-saturated shales in Lower Paleozoic (Silurian and Ordovician) organic-rich formations of the Baltic Basin in North Poland. The authors adopted a geochemical method based on Rock Eval results which directly measure hydrocarbon content present in rock samples. Its application on a real data set required the implementation of correction procedures to consider also those oil compounds which were lost before Rock Eval measurements were taken or are not recorded in S1 parameter. It was accomplished through the introduction of two correction coefficients: c1—for evaporation loss and c2—for heavier compounds underestimation. The first one was approximated on the basis of published results and known properties of crude oil, while the second one was addressed with laboratory experimental procedure which combines Rock Eval pyrolysis and rock sample extraction with organic solvents. The calculation formulas were implemented in the 3D geological model of shale formations reproducing their geometry as well as the spatial variability of the petrophysical and geochemical properties. Consequently, the results of oil-in-place estimation were also available as 3D models, ready for visualization and interpretation in terms of delineation of most favorable zones or well placement. The adopted geochemical method and the results of oil-in-place estimation it produced were confronted with standard volumetric method. Although both of them are volumetric methods, the results depend on different sets of rock properties, which is an advantage for result comparison reasons. The study revealed that the geochemical method of oil-in-place estimation in liquid-rich shales after appropriate adjustment, considering shale formation and reservoir fluid dependent conditions, could provide reliable results and be implemented on the early stage of shale exploration process in a condition of production data inaccessibility.

Identification of Coronary Artery Disease using Artificial Neural Network and Case Based Reasoning

2020 ◽  
Vol 13 ◽  
Author(s):  
Varun Sapra ◽  
M.L Saini ◽  
Luxmi Verma
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Early Stage ◽  
Second Phase ◽  
Case Based Reasoning ◽  
Data Set ◽  
Artery Disease ◽  
Severity Of The Disease ◽  
Case Based

Background: Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention of Coronary Artery Disease is possible only if it is diagnosed, at early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multi-layer perceptron) with Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented on reduced dataset and with time-based learning for stochastic gradient descent respectively. Results: The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners as a supporting decision system and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.

scholarly journals Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit

Geosciences ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 150
Author(s):  
Nilgün Güdük ◽  
Miguel de la Varga ◽  
Janne Kaukolinna ◽  
Florian Wellmann
Keyword(s):  
Structural Parameters ◽  
Rock Properties ◽  
Magnetic Data ◽  
Geological Model ◽  
Borehole Data ◽  
Likelihood Functions ◽  
Different Types ◽  
Geophysical Information ◽  
Different Sources

Structural geological models are widely used to represent relevant geological interfaces and property distributions in the subsurface. Considering the inherent uncertainty of these models, the non-uniqueness of geophysical inverse problems, and the growing availability of data, there is a need for methods that integrate different types of data consistently and consider the uncertainties quantitatively. Probabilistic inference provides a suitable tool for this purpose. Using a Bayesian framework, geological modeling can be considered as an integral part of the inversion and thereby naturally constrain geophysical inversion procedures. This integration prevents geologically unrealistic results and provides the opportunity to include geological and geophysical information in the inversion. This information can be from different sources and is added to the framework through likelihood functions. We applied this methodology to the structurally complex Kevitsa deposit in Finland. We started with an interpretation-based 3D geological model and defined the uncertainties in our geological model through probability density functions. Airborne magnetic data and geological interpretations of borehole data were used to define geophysical and geological likelihoods, respectively. The geophysical data were linked to the uncertain structural parameters through the rock properties. The result of the inverse problem was an ensemble of realized models. These structural models and their uncertainties are visualized using information entropy, which allows for quantitative analysis. Our results show that with our methodology, we can use well-defined likelihood functions to add meaningful information to our initial model without requiring a computationally-heavy full grid inversion, discrepancies between model and data are spotted more easily, and the complementary strength of different types of data can be integrated into one framework.

scholarly journals Separation of Fe from Mn in the Cryogenian Sedimentary Mn Deposit, South China: Insights from Ore Mineral Chemistry and S Isotopes from the Dawu Deposit

Minerals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 446
Author(s):  
Zhiming Xu ◽  
Chengquan Wu ◽  
Zhengwei Zhang ◽  
Jinhong Xu ◽  
Xiyao Li ◽  
...  
Keyword(s):  
South China ◽  
Large Scale ◽  
Early Stage ◽  
Sulfur Isotopes ◽  
Mineral Chemistry ◽  
Manganese Carbonate ◽  
Separation Mechanism ◽  
Geochemical Properties ◽  
Stage Separation ◽  
Fe Content

Manganese and Fe have similar geochemical properties in the supergene environment. Separation of Mn and Fe is an important process for the formation of high-grade sedimentary manganese deposits. Large-scale manganese carbonate deposits (total reserves of approximately 700 Mt) were formed during the interglacial of the Sturtian and Marinoan in South China. The orebodies are hosted in the black rock series at the basal Datangpo Formation of the Cryogenian period. The Fe contents in ores range from 1.15 to 7.18 wt.%, with an average of 2.80 wt.%, and the average Mn/Fe ratio is 8.9, indicating a complete separation of Mn and Fe during the formation of manganese ores. Here, we present element data of manganese carbonates and sulfur isotopes of pyrite from the Dawu deposit, Guizhou, China, aiming to investigate the separation mechanism of Mn and Fe and the ore genesis. The Fe in ores mainly occurs as carbonate (FeCO3) and pyrite (FeS2). The Mn, Ca, Mg and Fe exist in the form of isomorphic substitutions in manganese carbonate. The contents of FeCO3 in manganese carbonates are similar in different deposits, with averages of 2.6–2.8 wt.%. The whole-rock Fe and S contents have an obvious positive correlation (R = 0.69), indicating that the difference of whole-rock Fe content mainly comes from the pyrite content. The δ34SV-CDT of pyrite varies from 40.0 to 48.3‰, indicating that the pyrite formed in a restricted basin where sulfate supply was insufficient and the sulfate concentrations were extremely low. Additionally, the whole-rock Fe content is negatively correlated with the δ34S values of the whole-rock and pyrite, with correlation coefficients of −0.78 and −0.83, respectively. Two stages of separations of Mn and Fe might have occurred during the mineralization processes. The reduced seawater became oxidized gradually after the Sturtian glaciation, and Fe2+ was oxidized and precipitated before Mn2+, which resulted in the first-stage separation of Mn and Fe. The residual Mn-rich and Fe-poor seawater flowed into the restricted rift basin. Mn and Fe were then precipitated in sediments as oxyhydroxide as the seawater was oxidized. At the early stage of diagenesis, organic matter was oxidized, and manganese oxyhydroxide was reduced, forming the manganese carbonate. H2S was insufficient in the restricted basin due to the extremely low sulfate concentration. The Fe2+ was re-released due to the lack of H2S, resulting in the second-stage separation of Mn and Fe. Finally, the manganese carbonate deposit with low Fe and very high δ34S was formed in the restricted basin after the Sturtian glaciation.

Racial disparities in stage-specific gastric cancer: analysis of results from the Surveillance Epidemiology and End Results (SEER) program database

2017 ◽  
Vol 65 (6) ◽  
pp. 991-998 ◽  
Author(s):  
Gang Zhang ◽  
Xing Zhao ◽  
Jie Li ◽  
Yu Yuan ◽  
Ming Wen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Radiation Treatment ◽  
Early Stage ◽  
Ethnic Disparities ◽  
Incidence Rates ◽  
Pacific Islanders ◽  
Seer Program ◽  
Second Primary ◽  
Data Set ◽  
End Results

The incidence of gastric cancer is declining in western countries but continues to represent a serious health problem worldwide, especially in Asia and among Asian Americans. This study aimed to investigate ethnic disparities in stage-specific gastric cancer, including differences in incidence, treatment and survival. The cohort study was analyzed using the data set of patients with gastric cancer registered in the Surveillance, Epidemiology, and End Results (SEER) program from 2004 to 2013. Among 54,165 patients with gastric cancer, 38,308 were whites (70.7%), 7546 were blacks (13.9%), 494 were American Indian/Alaskan Natives (0.9%) and 7817 were Asians/Pacific Islanders (14.4%). Variables were patient demographics, disease characteristics, surgery/radiation treatment, overall survival (OS) and cause specific survival (CSS). Asians/Pacific Islanders demonstrated the highest incidence rates for gastric cancer compared with other groups and had the greatest decline in incidence during the study period (13.03 to 9.28 per 100,000/year), as well as the highest percentage of patients with American Joint Committee on Cancer (AJCC) early stage gastric cancer. There were significant differences between groups in treatment across stages I–IV (all p<0.001); Asians/Pacific Islanders had the highest rate of surgery plus radiation (45.1%). Significant differences were found in OS and CSS between groups (p<0.001); OS was highest among Asians/Pacific Islanders. Multivariate analysis revealed that age, race, grade, stage, location, and second primary cancer were valid prognostic factors for survival. Marked ethnic disparities exist in age-adjusted incidence of primary gastric cancer, with significant differences between races in age, gender, histological type, grade, AJCC stage, location, second cancer, treatment and survival.

Computer-assisted Diagnosis for Early Stage Pleural Mesothelioma

2007 ◽  
Vol 46 (03) ◽  
pp. 324-331 ◽  
Author(s):  
P. Jäger ◽  
S. Vogel ◽  
A. Knepper ◽  
T. Kraus ◽  
T. Aach ◽  
...  
Keyword(s):  
Early Stage ◽  
Computation Time ◽  
Hounsfield Unit ◽  
Computer Assisted ◽  
Clinical Test ◽  
Pleural Mesothelioma ◽  
Data Sets ◽  
Data Set ◽  
Average Computation Time ◽  
Ct Data

Summary Objectives: Pleural thickenings as biomarker of exposure to asbestos may evolve into malignant pleural mesothelioma. Foritsearly stage, pleurectomy with perioperative treatment can reduce morbidity and mortality. The diagnosis is based on a visual investigation of CT images, which is a time-consuming and subjective procedure. Our aim is to develop an automatic image processing approach to detect and quantitatively assess pleural thickenings. Methods: We first segment the lung areas, and identify the pleural contours. A convexity model is then used together with a Hounsfield unit threshold to detect pleural thickenings. The assessment of the detected pleural thickenings is based on a spline-based model of the healthy pleura. Results: Tests were carried out on 14 data sets from three patients. In all cases, pleural contours were reliably identified, and pleural thickenings detected. PC-based Computation times were 85 min for a data set of 716 slices, 35 min for 401 slices, and 4 min for 75 slices, resulting in an average computation time of about 5.2 s per slice. Visualizations of pleurae and detected thickeningswere provided. Conclusion: Results obtained so far indicate that our approach is able to assist physicians in the tedious task of finding and quantifying pleural thickenings in CT data. In the next step, our system will undergo an evaluation in a clinical test setting using routine CT data to quantifyits performance.

scholarly journals An Immune Signature Robustly Predicts Clinical Deterioration for Hepatitis C Virus-Related Early-Stage Cirrhosis Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Cheng Guo ◽  
Chenglai Dong ◽  
Junjie Zhang ◽  
Rui Wang ◽  
Zhe Wang ◽  
...  
Keyword(s):  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Early Stage ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Calibration Plot ◽  
Prognostic Signature ◽  
Data Set ◽  
Immune Infiltration ◽  
Immune Related Genes

Hepatitis C virus (HCV)-related cirrhosis leads to a heavy global burden of disease. Clinical risk stratification in HCV-related compensated cirrhosis remains a major challenge. Here, we aim to develop a signature comprised of immune-related genes to identify patients at high risk of progression and systematically analyze immune infiltration in HCV-related early-stage cirrhosis patients. Bioinformatics analysis was applied to identify immune-related genes and construct a prognostic signature in microarray data set. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted with the “clusterProfiler” R package. Besides, the single sample gene set enrichment analysis (ssGSEA) was used to quantify immune-related risk term abundance. The nomogram and calibrate were set up via the integration of the risk score and clinicopathological characteristics to assess the effectiveness of the prognostic signature. Finally, three genes were identified and were adopted to build an immune-related prognostic signature for HCV-related cirrhosis patients. The signature was proved to be an independent risk element for HCV-related cirrhosis patients. In addition, according to the time-dependent receiver operating characteristic (ROC) curves, nomogram, and calibration plot, the prognostic model could precisely forecast the survival rate at the first, fifth, and tenth year. Notably, functional enrichment analyses indicated that cytokine activity, chemokine activity, leukocyte migration and chemotaxis, chemokine signaling pathway and viral protein interaction with cytokine and cytokine receptor were involved in HCV-related cirrhosis progression. Moreover, ssGSEA analyses revealed fierce immune-inflammatory response mechanisms in HCV progress. Generally, our work developed a robust prognostic signature that can accurately predict the overall survival, Child-Pugh class progression, hepatic decompensation, and hepatocellular carcinoma (HCC) for HCV-related early-stage cirrhosis patients. Functional enrichment and further immune infiltration analyses systematically elucidated potential immune response mechanisms.

scholarly journals CYSTM1: A Novel Biomarker for Hepatocellular Carcinoma Prognosis

2021 ◽  
Author(s):  
Jun Du ◽  
Jinguo Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Early Stage ◽  
Human Tumor ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Kaplan Meier ◽  
Cancer Genome Atlas ◽  
Cox Proportional Hazard Regression ◽  
The Relationship

Abstract Background: The expression and molecular mechanism of cysteine rich transmembrane module containing 1 (CYSTM1) in human tumor cells remains unclear. The aim of this study was to determine whether CYSTM1 could be used as a potential prognostic biomarker for hepatocellular carcinoma (HCC).Methods: We first demonstrated the relationship between CYSTM1 expression and HCC in various public databases. Secondly, Kaplan–Meier analysis and Cox proportional hazard regression model were performed to evaluate the relationship between the expression of CYSTM1 and the survival of HCC patients which data was downloaded in the cancer genome atlas (TCGA) database. Finally, we used the expression data of CYSTM1 in TCGA database to predict CYSTM1-related signaling pathways through bioinformatics analysis.Results: The expression level of CYSTM1 in HCC tissues was significantly correlated with T stage (p = 0.039). In addition, Kaplan–Meier analysis showed that the expression of CYSTM1 was significantly associated with poor prognosis in patients with early-stage HCC (p = 0.003). Multivariate analysis indicated that CYSTM1 is a potential predictor of poor prognosis in HCC patients (p = 0.036). The results of biosynthesis analysis demonstrated that the data set of CYSTM1 high expression was mainly enriched in neurodegeneration and oxidative phosphorylation pathways.Conclusion: CYSTM1 is an effective biomarker for the prognosis of patients with early-stage HCC and may play a key role in the occurrence and progression of HCC.

scholarly journals Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7821 ◽  
Author(s):  
Xiaoming Zhang ◽  
Jing Zhuang ◽  
Lijuan Liu ◽  
Zhengguo He ◽  
Cun Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Prognostic Value ◽  
Early Stage ◽  
Expression Profiles ◽  
Diagnostic Value ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

scholarly journals NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION

2019 ◽  
Vol 2019 (02) ◽  
pp. 89-98
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Early Stage ◽  
Original Data ◽  
Data Set ◽  
Cancer Prediction ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
The Neural Networks ◽  
Human Nervous System

Predicting the category of tumors and the types of the cancer in its early stage remains as a very essential process to identify depth of the disease and treatment available for it. The neural network that functions similar to the human nervous system is widely utilized in the tumor investigation and the cancer prediction. The paper presents the analysis of the performance of the neural networks such as the, FNN (Feed Forward Neural Networks), RNN (Recurrent Neural Networks) and the CNN (Convolutional Neural Network) investigating the tumors and predicting the cancer. The results obtained by evaluating the neural networks on the breast cancer Wisconsin original data set shows that the CNN provides 43 % better prediction than the FNN and 25% better prediction than the RNN.

Validation of a high performing blood test for multiple major cancer screenings.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10561-10561
Author(s):  
Linhao Xu ◽  
Jun Wang ◽  
Weifeng Ma ◽  
Xin Liu ◽  
Sihui Li ◽  
...  
Keyword(s):  
Cancer Screening ◽  
Blood Test ◽  
Retrospective Cohort ◽  
Early Stage ◽  
Stage I ◽  
Cancer Type ◽  
Data Set ◽  
Specificity And Sensitivity ◽  
Cancer Types ◽  
Stage Stage

10561 Background: Early detection at the localized stage is pivotal for the successful treatment of various cancer types. Although several cancers already have routine screening approaches, the comprehensive utilities are impeded for various reasons, e.g., low accuracy, high cost, limited availability of required facilities, especially in the developing countries. Therefore, an accurate, cost-effective, and non-invasive test for multiple major cancer screening is in high demand. We previously reported a cfDNA methylation test, which can detect five major cancer types with high specificity and sensitivity, especially at the early stage (stage I). These five major cancers, including lung cancer (LC), breast cancer (BC), colorectal cancer (CRC), gastric cancer (GC), and esophageal cancer (EC), account for 56% of new cancer cases and 60% of cancer-related deaths yearly in China. Here, we report the result in an independent cohort as a further validation of this multi-cancer screening test. Methods: The high-throughput targeted methylation profiling platform, Aurora, was used to analyze the plasma samples from an independent retrospective cohort containing 505 healthy controls and ̃200 cases for each cancer type. A locked model based on our previous pilot study (reported in AACR 2020 and 2021) was applied to this data set to assess the overall performance. Results: The Area Under Curves (AUC) of the classifier for LC, BC, CRC, GC and EC are 97.3%, 96.2%, 92.0%, 94.0% and 93.5%, respectively. At a fixed specificity of 99%, the sensitivities for LC, BC, CRC, GC and EC are 84%, 75%, 82%, 85% and 78%, respectively. Conclusions: A methylation blood test for five major cancer screening has been validated in a large retrospective cohort. Its high sensitivity for each cancer type, especially at the early stage (stage I), and easy to use suggests it can be implemented in real clinical world. A large prospective clinical trial is undergoing to further validate this test in asymptomatic populations.

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