Deep learning algorithm-enabled sediment characterization techniques to determination of water saturation for tight gas carbonate reservoirs in Bohai Bay Basin, China.

Understanding water saturation levels in tight gas carbonate reservoirs is vital for optimizing hydrocarbon production and mitigating challenges such as reduced permeability due to water saturation (Sw) and pore throat blockages, given its critical role in managing capillary pressure in water drive mechanisms reservoirs. Traditional sediment characterization methods such as core analysis, are often costly, invasive, and lack comprehensive spatial information. In recent years, several classical machine learning models have been developed to address these shortcomings. Traditional machine learning methods utilized in reservoir characterization encounter various challenges, including the ability to capture intricate relationships, potential overfitting, and handling extensive, multi-dimensional datasets. Moreover, these methods often face difficulties in dealing with temporal dependencies and subtle patterns within geological formations, particularly evident in heterogeneous carbonate reservoirs. Consequently, despite technological advancements, enhancing the reliability, interpretability, and applicability of predictive models remains imperative for effectively characterizing tight gas carbonate reservoirs. This study employs a novel data-driven strategy to prediction of water saturation in tight gas reservoir powered by three recurrent neural network type deep/shallow learning algorithms-Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), K-nearest neighbor (KNN) and Decision tree (DT)-customized to accurately forecast sequential sedimentary structure data. These models, optimized using Adam's optimizer algorithm, demonstrated impressive performance in predicting water saturation levels using conventional petrophysical data. Particularly, the GRU model stood out, achieving remarkable accuracy (an R-squared value of 0.9973) with minimal errors (RMSE of 0.0198) compared to LSTM, RNN, SVM, KNN and, DT algorithms, thus showcasing its proficiency in processing extensive datasets and effectively identifying patterns. By achieving unprecedented accuracy levels, this study not only enhances the understanding of sediment properties and fluid saturation dynamics but also offers practical implications for reservoir management and hydrocarbon exploration in complex geological settings. These insights pave the way for more reliable and efficient decision-making processes, thereby advancing the forefront of reservoir engineering and petroleum geoscience.

Seismic sedimentology response characteristics of nearshore terminal fan in the suning area of Raoyang Sag, Bohai Bay Basin, NE China.

A nearshore terminal fan is a special water system formed in arid environments. The characterisation of its thin-channel sand bodies has long been a challenge restricting oil and gas exploration. This study takes the Suning area of the Raoyang Sag as an example and uses the principles of seismic sedimentology to conduct seismic sedimentary research on the nearshore terminal fan of the first member of the Palaeogene Shahejie Formation (Es1) based on three-dimensional seismic, logging, and core analysis. Seven fourth-order sequences (SQV7) were identified within Es1, deposited by a fluvial river system terminating at the contracting bank of a lake. Prograding terminal fan sedimentary facies on a gentle slope zone were observed in the root mean square seismic attributes after spectral decomposition. We have successfully resolved the sandstone within the studied terminal fan system using a 90° phase conversion of the seismic data and red-green-blue (RGB) fusion of the various seismic attributes. The upper subsegment of the Shahejie Formation developed extensive nearshore terminal fan sedimentation, and the seismic sedimentological response characteristics were mainly channel-like and strip-shaped geomorphic systems deposited on gentle slope zones, indicating distributary channels and distal basin sedimentation. This study enriches our understanding of nearshore fans and provides ideas for predicting favourable sand bodies in this type of sedimentary facies.

Risk aggregation considering probabilistic and consequential interactions: A general formulation with computational cost handling.

Complicated interaction between risk events is the critical obstacle preventing accurate risk aggregation, which is an important issue in risk management. Recent research integrates interaction into risk aggregation with different perspectives and lacks a comprehensive discussion of this issue, making the risk aggregation process not universal for diverse cases, especially in subjective risk assessment contexts. Therefore, this article proposes a theoretically convincing risk aggregation method embedding different types of interaction to support decision analysis more effectively. The main contributions of this article are as follows: (a) more in-depth and stricter definitions, measures, and graphical descriptions of different types of interaction are developed to ensure the accuracy of risk aggregation; (b) a formal risk aggregation approach that could apply in both objective and subjective risk assessment contexts while elegantly embedding risk interactions is proposed; (c) the additivity of risks and risk sets in the risk aggregation process is discussed in detail and the conditions for additivity are clarified; (d) the quasi-two/three-additive measures, which approximately obtain the aggregate risk value within sufficient reliability, are proposed to greatly reduce the computational cost. To examine the applicability of the proposed general risk aggregation method, a case study is finally presented to show the complete risk aggregation process and its application in the decision-making stage.

Treating SARS-CoV-2 Omicron variant infection by molnupiravir for pandemic mitigation and living with the virus: a mathematical modeling study.

Treating severe COVID-19 patients and controlling the spread of SARS-CoV-2 are concurrently important in mitigating the pandemic. Classically, antiviral drugs are primarily developed for treating hospitalized COVID-19 patients with severe diseases to reduce morbidity and/or mortality, which have limited effects on limiting pandemic spread. In this study, we simulated the expanded applications of oral antiviral drugs such as molnupiravir to mitigate the pandemic by treating nonhospitalized COVID-19 cases. We developed a compartmental mathematical model to simulate the effects of molnupiravir treatment assuming various scenarios in the Omicron variant dominated settings in Denmark, the United Kingdom and Germany. We found that treating nonhospitalized cases can limit Omicron spread. This indirectly reduces the burden of hospitalization and patient death. The effectiveness of this approach depends on the intrinsic nature of the antiviral drug and the strategies of implementation. Hypothetically, if resuming pre-pandemic social contact pattern, extensive application of molnupiravir treatment would dramatically (but not completely) mitigate the COVID-19 burden, and thus there remains lifetime cost of living with the virus.

Prognostic value of a microRNA-pair signature in laryngeal squamous cell carcinoma patients.

PURPOSE: Predicting the prognosis in laryngeal squamous cell carcinoma (LSCC) patients will improve clinical decision-making. Here, we aimed to identify a qualitative signature based on the within-sample relative expression orderings (REOs) of microRNA (miRNA) pairs to predict the overall survival (OS) of LSCC patients. METHODS: First, we constructed non-repeating miRNA pairs based on differentially expressed miRNAs (DEmiRNAs) between LSCC and normal tissues. Then, we applied a bootstrap-based feature selection method to identify a robust miRNA-pair signature. The bootstrap-based feature selection improved the stability of feature selection by an ensemble based on the data perturbation. Furthermore, a series of bioinformatics analyses were carried out to explore the potential mechanisms of the signature and potential drug targets for LSCC. RESULTS: Based on the REOs of miRNA pairs, we identified a qualitative signature that consisted of 12 miRNA pairs. The constructed signature has good performance in predicting the OS of LSCC patients. It is robust against batch effects and more suitable for individual clinical applications. Furthermore, we identified several hub genes that may be potential drug targets for LSCC. CONCLUSION: Overall, our findings provided a promising signature for predicting the OS of LSCC patients.

A multi-regional, hierarchical-tier mathematical model of the spread and control of COVID-19 epidemics from epicentre to adjacent regions.

Epicentres are the focus of COVID-19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi-regional, hierarchical-tier mathematical model for better understanding the complexity and heterogeneity of COVID-19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi-regional, hierarchical-tier SLIHR model. With this model, we revealed insight into how COVID-19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi-regional, hierarchical-tier SLIHR model revealed insight into how COVID-19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID-19 pandemic.

Identification of a Qualitative Signature for the Diagnosis of Dementia With Lewy Bodies.

Background and purpose: Diagnosis of dementia with Lewy bodies (DLB) is highly challenging, primarily due to a lack of valid and reliable diagnostic tools. To date, there is no report of qualitative signature for the diagnosis of DLB. We aimed to develop a blood-based qualitative signature for differentiating DLB patients from healthy controls. Methods: The GSE120584 dataset was downloaded from the public database Gene Expression Omnibus (GEO). We combined multiple methods to select features based on the within-sample relative expression orderings (REOs) of microRNA (miRNA) pairs. Specifically, we first quickly selected miRNA pairs related to DLB by identifying reversal stable miRNA pairs. Then, an optimal miRNA pair subset was extracted by random forest (RF) and support vector machine-recursive feature elimination (SVM-RFE) methods. Furthermore, we applied logistic regression (LR) and SVM to build several prediction models. The model performance was assessed using the receiver operating characteristic curve (ROC) analysis. Lastly, we conducted bioinformatics analyses to explore the molecular mechanisms of the discovered miRNAs. Results: A qualitative signature consisted of 17 miRNA pairs and two clinical factors was identified for discriminating DLB patients from healthy controls. The signature is robust against experimental batch effects and applicable at the individual levels. The accuracies of the-signature-based models on the test set are 82.61 and 79.35%, respectively, indicating that the signature has acceptable discrimination performance. Moreover, bioinformatics analyses revealed that predicted target genes were enriched in 11 Go terms and 2 KEGG pathways. Moreover, five potential hub genes were found for DLB, including SRF, MAPK1, YWHAE, RPS6KA3, and KDM7A. Conclusion: This study provided a blood-based qualitative signature with the potential to be used as an effective tool to improve the accuracy of DLB diagnosis.

Recent advances in peptide nucleic acid for cancer bionanotechnology.

Peptide nucleic acid (PNA) is an oligomer, in which the phosphate backbone has been replaced by a pseudopeptide backbone that is meant to mimic DNA. Peptide nucleic acids are of the utmost importance in the biomedical field because of their ability to hybridize with neutral nucleic acids and their special chemical and biological properties. In recent years, PNAs have emerged in nanobiotechnology for cancer diagnosis and therapy due to their high affinity and sequence selectivity toward corresponding DNA and RNA. In this review, we summarize the recent progresses that have been made in cancer detection and therapy with PNA biotechnology. In addition, we emphasize nanoparticle PNA-based strategies for the efficient delivery of drugs in anticancer therapies.

Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.

CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.

Upregulated KLK10 inhibits esophageal cancer proliferation and enhances cisplatin sensitivity in vitro.

The kallikrein-related peptidase 10 (KLK10) gene has tumor-suppressive function in various types of human cancer. However, previous studies showed that KLK10 also acts as an oncogene and is upregulated in gastrointestinal tumors. The role of KLK10 in human esophageal cancer (EC) remains unclear. In the present study, the expression of KLK10 in human esophageal and non-esophageal cancer tissues was investigated by immunohistochemistry. Quantitative RT-PCR and western blot analysis were utilized to detect KLK10 mRNA and protein expression in human esophageal cancer cell lines (TE-1 and Eca-109). Small interference RNA was utilized to specifically knockdown KLK10 expression in Eca-109 and TE-1 cells. Cell proliferation, cell cycle analysis as well as CDDP-dependent apoptosis were determined using a CCK-8 assay and flow cytometry. The results showed that, KLK10 was positive in 67 out of 83 (80.72%) human EC and positive in 3 out of 11 (27.27%) normal tissues (P=0.001). The present study indicated that KLK10 potentially plays a crucial role in Eca-109 cell growth. Additionally, the downregulation of KLK10 induced S-phase arrest and promoted cisplatin-induced apoptosis. The resutls of the present study suggested that KLK10 is a promising novel marker for the diagnostic and therapeutic target of esophageal cancer.

A robust ordering strategy for retailers facing a free shipping option.

Free shipping with conditions has become one of the most effective marketing tools available. An increasing number of companies, especially e-businesses, prefer to offer free shipping with some predetermined condition, such as a minimum purchase amount by the customer. However, in practice, the demands of buyers are uncertain; they are often affected by many factors, such as the weather and season. We begin by modeling the centralized ordering problem in which the supplier offers a free shipping service and retailers face stochastic demands. As these random data are considered, only partial information such as the known mean, support, and deviation is needed. The model is then analyzed via a robust optimization method, and the two types of equivalent sets of uncertainty constraints that are obtained provide good mathematical properties with consideration of the robustness of solutions. Subsequently, a numerical example is used to compare the results achieved from a robust optimization method and the linear decision rules. Additionally, the robustness of the optimal solution is discussed, as it is affected by the minimum quantity parameters. The increasing cost-threshold relationship is divided into three periods. In addition, the case study shows that the proposed method achieves better stability as well as computational complexity.

Characteristics of polycyclic aromatic hydrocarbons in agricultural soils at a typical coke production base in Shanxi, China.

There is wide concern about polycyclic aromatic hydrocarbons (PAHs) because of their carcinogenic and mutagenic potential. The coking industry is an important source of PAHs. In this study, 36 arable soil samples, a sensitive medium from the perspective of food safety and health, were collected from one of the largest coke production bases in China. The concentration of total 21 PAHs ranged from 294 to 1665 ng g(-1), with a mean of 822±355 ng g(-1). Approximately 60% of the soil samples were heavily polluted with the level higher than 600 ng g(-1). Particularly high abundances of high molecular weight PAHs were found, and the calculated BaPeq was as high as 54.3 ng g(-1). Soil PAH levels were positively correlated with soil organic matter content. The soil PAHs were from complex mixture sources, and high-temperature pyrogenic sources were most likely responsible for the heavy PAH contamination. Effective control strategies and probable remediation approaches should be proposed to improve soil quality.

Metformin enhances radiation response of ECa109 cells through activation of ATM and AMPK.

Metformin is a first-line used agent for type II diabetes with few side effects. The antineoplastic effect of metformin was widely explored recently. Metformin may also be a prospective chemosensitizer or radiosensitizer in cancer treatment. In the present study, we firstly showed that metformin could effectively enhance the anti-proliferation effect of ionizing radiation (IR) on esophageal cancer (EC) cells ECa109. More potent DNA damage was observed by detection of γH2AX foci. Metformin synergistically induce apoptosis and cell cycle arrest in ECa109 cells with IR. Furthermore, the mechanisms how metformin sensitized ECa109 cells to IR may be targeting the ATM and AMPK/mTOR/HIF-1α pathways. Metformin may be a valuable agent in comprehensive treatment of EC.

Local fuzzy fractal dimension and its application in medical image processing.

The local fuzzy fractal dimension (LFFD) is proposed to extract local fractal feature of medical images. The definition of LFFD is an extension of the pixel-covering method by incorporating the fuzzy set. Multi-feature edge detection is implemented with the LFFD and the Sobel operator. The LFFD can also serve as a characteristic of motion in medical image sequences. The experimental results show that the LFFD is an important feature of edge areas in medical images and can provide information for segmentation of echocardiogram image sequences.