Multidimensional Databases Research Articles

Study on the Use of Multidimensional Database Systems for Demography Data Analysis

The demography database system should be very useful for various purposes in overcoming the socio-economic problems of the population, such as the distribution of charity, especially for people affected by the Covid-19 pandemic. For example, those who have lost their jobs, those who have difficulty in accessing medical services, etc., or for general election data, for example, presidential election, legislator election, or governor election. But unfortunately, the demography database system in our country, Indonesia still has many problems. Things to consider are that there is no integration between databases at the central and local governments, besides that there is no synchronization of data between institutions. As a result, there are many mistakes in the distribution of charity for people affected by the Covid-19 pandemic in 2020, such as those who should receive do not receive it, and those who should not receive it receive it. This problem can be caused by the absence of a definite reference in the population data. As an example, On the time dimension, one year's data can be multi-interpreted both between the central and the regions government as well as between agencies. This study aims to build an integrated demography database system through a multidimensional database system model. In the multidimensional database system, the data is a cell that can show information from multi-dimensions (such as spatial, temporal, and variable). Each dimension in a multidimensional database system is interconnected in a system so that it can eliminate multiple interpretations of data in one dimension or between dimensions. Each dimension in a multidimensional database system can be arranged in a hierarchy. In the spatial dimension, it can be arranged based on a hierarchy of administrative units, such as from state down to province, then district/city, sub-district, village, and so on. So that it can eliminate the non-integration between data on the central and local governments. In the dimension of variable, it can be arranged based on the level of detail of demography data, and the total population as the top level of detail. While the next level of detail can be arranged based on criteria from demographic data such as sex, age, occupation, education, etc. So that it can eliminate the non-synchronization of data between institutions.

A hypoglycemia early alarm method for patients with type 1 diabetes based on multi-dimensional sequential pattern mining

Hypoglycemia is a limiting factor for blood glucose management. Serious symptoms such as seizures, and coma may occur during severe hypoglycemia, and nocturnal hypoglycemia is particularly dangerous for patients with type 1 diabetes (T1D). An effective early alarm method is essential for hypoglycemia prevention but challenging, as blood glucose is affected by many factors and the hypoglycemia sequence patterns vary from person to person. In this paper, we proposed a hypoglycemia early alarm method for mining the hidden information in blood glucose based on multi-dimensional sequential pattern mining. The blood glucose, meal, and insulin time series information were used to construct a multi-dimensional database, then the UniSeq algorithm was used to extract multi-dimensional hypoglycemia sequence patterns. Hypoglycemia early alarm was realized through pattern matching with real-time blood glucose. The public OhioT1DM dataset was used for performance evaluation. The experiment results were: 75.76% Sensitivity, 75% Precision, 75.38% F1 score, and 25.17 minutes early alarm time. The result verified that multi-dimensional sequential pattern mining can extract more hidden information and demonstrate more potential significance in providing comprehensive diagnosis support for personalized treatment. Furthermore, early alarms for potential hypoglycemia can also reserve sufficient time for blood glucose management.

A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach.

The talent development processes in youth football are both complex and multidimensional. The purpose of this two-fold study was to apply a multidisciplinary, machine learning approach to examine: (a) the developmental characteristics of under-9 to under-16 academy players (n = 98; Study 1), and (b) the characteristics of selected and deselected under-18 academy players (n = 18; Study 2). A combined total of 53 factors cumulated from eight data collection methods across two seasons were analysed. A cross-validated Lasso regression was implemented, using the glmnet package in R, to analyse the factors that contributed to: (a) player review ratings (Study 1), and (b) achieving a professional contract (Study 2). Results showed non-zero coefficients for improvement in subjective performance in 15 out of the 53 analysed features, with key findings revealing advanced percentage of predicted adult height (0.196), greater lob pass (0.160) and average dribble completion percentage (0.124), more total match-play hours (0.145), and an older relative age (BQ1 vs. BQ2: −0.133; BQ1 vs. BQ4: −0.060) were the most important features that contributed towards player review ratings. Moreover, PCDEQ Factor 3 and an ability to organise and engage in quality practice (PCDEQ Factor 4) were important contributing factors towards achieving a professional contract. Overall, it appears the key factors associated with positive developmental outcomes are not always technical and tactical in nature, where coaches often have their expertise. Indeed, the relative importance of these factors is likely to change over time, and with age, although psychological attributes appear to be key to reaching potential across the academy journey. The methodological techniques used here also serve as an impetus for researchers to adopt a machine learning approach when analysing multidimensional databases.

Happiness maximizing sets under group fairness constraints

Finding a happiness maximizing set (HMS) from a database, i.e., selecting a small subset of tuples that preserves the best score with respect to any nonnegative linear utility function, is an important problem in multi-criteria decision-making. When an HMS is extracted from a set of individuals to assist data-driven algorithmic decisions such as hiring and admission, it is crucial to ensure that the HMS can fairly represent different groups of candidates without bias and discrimination. However, although the HMS problem was extensively studied in the database community, existing algorithms do not take group fairness into account and may provide solutions that under-represent some groups. In this paper, we propose and investigate a fair variant of HMS (FairHMS) that not only maximizes the minimum happiness ratio but also guarantees that the number of tuples chosen from each group falls within predefined lower and upper bounds. Similar to the vanilla HMS problem, we show that FairHMS is NP-hard in three and higher dimensions. Therefore, we first propose an exact interval cover-based algorithm called IntCov for FairHMS on two-dimensional databases. Then, we propose a bicriteria approximation algorithm called BiGreedy for FairHMS on multi-dimensional databases by transforming it into a submodular maximization problem under a matroid constraint. We also design an adaptive sampling strategy to improve the practical efficiency of BiGreedy. Extensive experiments on real-world and synthetic datasets confirm the efficacy and efficiency of our proposal.

Application of Data Warehouse Technology Based on Neural Network in Physical Education Quality Management

Data warehouse technology has been created because of China’s technological advancement and the increasing requirements of the educational sector. Physical assessments are treated as tests by many students. Institutions spend plenty of time every year through physical tests, yet the results are rarely shared with students. Teachers are impeded by the size and complexity of physical test data, finding it challenging to support experiments or judge individual students’ development. Students have trouble following up and delivering test-based feedback after instruction. In recent years, various researchers have offered insightful advice on how to build multidimensional database structures for such trouble. However, quality requirements alone are not adequate to guarantee quality in reality. So, this paper presents a novel Hypertuned wide polynet convolutional neural network (HWPCNN) framework in the data warehouse technology to attain the greatest performance in physical education quality management. In this paper, we first apply HWPCNNs for physical education quality management to analyze the accuracy and recall of the model. It is no secret that HWPCNN is now one of the most widely used deep learning techniques. When it comes to managing the quality of physical education, the HWPCNN’s local perception feature in the data warehouse technology allows it to achieve the best possible results. To validate the model’s performance, it is compared to other models and then improved further to increase its accuracy. The physical education resources are gathered as a raw dataset for this inquiry. The raw dataset is cleaned using the Z-score approach to get it ready for further data processing. Then, a sparse matrix approach is employed to build a data cube, while the proposed method is used to index multidimensional databases. To demonstrate that our work is of the best quality in managing physical education, performance metrics of the suggested method are also evaluated and compared with other traditional methods.

Mass data processing and multidimensional database management based on deep learning

Abstract With the rapid development of the Internet of Things, the requirements for massive data processing technology are getting higher and higher. Traditional computer data processing capabilities can no longer deliver fast, simple, and efficient data analysis and processing for today’s massive data processing due to the real-time, massive, polymorphic, and heterogeneous characteristics of Internet of Things data. Mass heterogeneous data of different types of subsystems in the Internet of Things need to be processed and stored uniformly, so the mass data processing method is required to be able to integrate multiple different networks, multiple data sources, and heterogeneous mass data and be able to perform processing on these data. Therefore, this article proposes massive data processing and multidimensional database management based on deep learning to meet the needs of contemporary society for massive data processing. This article has deeply studied the basic technical methods of massive data processing, including MapReduce technology, parallel data technology, database technology based on distributed memory databases, and distributed real-time database technology based on cloud computing technology, and constructed a massive data fusion algorithm based on deep learning. The model and the multidimensional online analytical processing model of the multidimensional database based on deep learning analyze the performance, scalability, load balancing, data query, and other aspects of the multidimensional database based on deep learning. It is concluded that the accuracy of multidimensional database query data is as high as 100%, and the accuracy of the average data query time is only 0.0053 s, which is much lower than the general database query time.

MSR1 characterized by chromatin accessibility mediates M2 macrophage polarization to promote gastric cancer progression

BackgroundM2 macrophages play an important role in gastric cancer progression and metastasis, but the underlying tumor-promoting mechanisms are largely unknown. MethodsThe TCIA database was used to identify the infiltration profile of macrophages. Integrated ATAC-seq, RNA-seq, and single-cell RNA sequencing (scRNA-seq) data from GC samples were used for the analysis. Using ATAC-seq profiles and RNA-seq datasets, combined with cox univariate survival analysis, we identified prognosis-related differentially expressed genes (DEGs) with chromatin accessibility, which were identified as hub genes. The CIBERSORTx algorithm was utilized to estimate the relative infiltration level of M2 macrophages, and Pearson correlation analysis was performed to investigate the relationship between hub genes and M2 macrophages. Multidimensional database validations were carried out to avoid biases. The expression level and function of hub genes in the clusters of macrophages were evaluated by using scRNA-seq data. The role of hub genes in the alternative activation of macrophages and gastric cancer malignant behaviors, as well as their potential regulatory mechanism in gastric cancer progression, were further explored. Results17,334 genes were acquired with chromatin accessibility in promoter regions by ATAC-seq. 2,714 genes were identified with both chromatin accessibility and differential expression based on the gene expression profiles (RNA-seq). By performing Cox univariate survival analysis, 171 survival-related DEGs with chromatin accessibility were identified as hub genes. Through the CIBERSORTx algorithm and Pearson correlation analysis, the gene MSR1 most associated with M2 macrophages was screened out. According to the scRNA-seq analysis, MSR1 was highly expressed in the clusters of macrophages and may be involved in regulating M2 macrophage polarization. In vitro experiments confirmed that M2 macrophage polarization and its induced malignant behavior of gastric cancer cells were inhibited by knockdown of MSR1. Furthermore, MSR1 mediated M2 macrophage polarization by regulating arginine and proline metabolism, thereby activating the AMPK/mTOR pathway to promote gastric cancer progression. ConclusionWe identified a gene—MSR1—characterized by chromatin accessibility, associated with poor prognosis in gastric cancer. This gene dictates the progression of gastric cancer by facilitating M2 macrophage polarization.

AnFiSA: An open-source computational platform for the analysis of sequencing data for rare genetic disease

Despite genomic sequencing rapidly transforming from being a bench-side tool to a routine procedure in a hospital, there is a noticeable lack of genomic analysis software that supports both clinical and research workflows as well as crowdsourcing. Furthermore, most existing software packages are not forward-compatible in regards to supporting ever-changing diagnostic rules adopted by the genetics community. Regular updates of genomics databases pose challenges for reproducible and traceable automated genetic diagnostics tools. Lastly, most of the software tools score low on explainability amongst clinicians.We have created a fully open-source variant curation tool, AnFiSA, with the intention to invite and accept contributions from clinicians, researchers, and professional software developers. The design of AnFiSA addresses the aforementioned issues via the following architectural principles: using a multidimensional database management system (DBMS) for genomic data to address reproducibility, curated decision trees adaptable to changing clinical rules, and a crowdsourcing-friendly interface to address difficult-to-diagnose cases. We discuss how we have chosen our technology stack and describe the design and implementation of the software. Finally, we show in detail how selected workflows can be implemented using the current version of AnFiSA by a medical geneticist.

The E3 ubiquitin ligase HUWE1 acts through the N-Myc-DLL1-NOTCH1 signaling axis to suppress glioblastoma progression.

BackgroundElucidation of the post‐transcriptional modification has led to novel strategies to treat intractable tumors, especially glioblastoma (GBM). The ubiquitin‐proteasome system (UPS) mediates a reversible, stringent and stepwise post‐translational modification which is closely associated with malignant processes of GBM. To this end, developing novel therapeutic approaches to target the UPS may contribute to the treatment of this disease. This study aimed to screen the vital and aberrantly regulated component of the UPS in GBM. Based on the molecular identification, functional characterization, and mechanism investigation, we sought to elaborate a novel therapeutic strategy to target this vital factor to combat GBM.MethodsWe combined glioma datasets and human patient samples to screen and identify aberrantly regulated E3 ubiquitin ligase. Multidimensional database analysis and molecular and functional experiments in vivo and in vitro were used to evaluate the roles of HECT, UBA and WWE domain‐containing E3 ubiquitin ligase 1 (HUWE1) in GBM. dCas9 synergistic activation mediator system and recombinant adeno‐associated virus (rAAV) were used to endogenously overexpress full‐length HUWE1 in vitro and in glioma orthotopic xenografts.ResultsLow expression of HUWE1 was closely associated with worse prognosis of GBM patients. The ubiquitination and subsequent degradation of N‐Myc mediated by HUWE1, leading to the inactivation of downstream Delta‐like 1 (DLL1)‐NOTCH1 signaling pathways, inhibited the proliferation, invasion, and migration of GBM cells in vitro and in vivo. A rAAV dual‐vector system for packaging and delivery of dCas9‐VP64 was used to augment endogenous HUWE1 expression in vivo and showed an antitumor activity in glioma orthotopic xenografts.ConclusionsThe E3 ubiquitin ligase HUWE1 acts through the N‐Myc‐DLL1‐NOTCH1 signaling axis to suppress GBM progression. Antitumor activity of rAAV dual‐vector delivering dCas9‐HUWE1 system uncovers a promising therapeutic strategy for GBM.

USING THE ANALYTIC HIERARCHY PROCESS WITH FUZZY LOGIC ELEMENTS TO OPTIMIZE THE DATABASE STRUCTURE

Context. Informational systems are very common and use databases to store information that users need. Many different data models can be used but the relational model is still relevant. The last decade show tendency of using distributed databases while working with relational data model and this approach requires a specially designed module to synchronize data of all separate databases. Considering optimizing the database structure, researchers didn’t pay much attention to the potential of users’ SQL-queries history. The optimal structure of all the distributed nodes could reduce the necessity of synchronization while the data access speed and its actuality would remain stable. The object of the research is the process of optimizing the structure of the distributed database of corporate information systems, which are based on the relational database’s model. Objective. The research aims at improving the accuracy of the data representation marker’s value on the distributed corporate information system’s (DCIS) node, obtained using the analytic hierarchy process by applying the fuzzy logic elements while processing the alternatives’ global priority vector. Method. The research’s authors in the set of their previous works emphasize the potential of using the collected history of users’ SQL queries. Firstly presented technology of users’ queries parsing. Then, the idea of using the multidimensional database for analyzing users’ queries by slices of workstation type, application, user, and his/her position was considered. Finally, the authors gave the full-scaled mathematical model for formalizing database and query models, and criteria of database structure’s optimality.The current research continues the given sequence and tries to increase the efficiency of the decision support system, by introducing elements of fuzzy logic to the analytic hierarchy process algorithm. The approach’s main idea is in presenting the global priorities vector in the form of a series of fuzzy sets of one variable with subsequent transformation to the exact value. This approach made it possible to maintain the accuracy of the obtained result while decreasing the number of solution alternatives. For new tuples added to the database’s tables after all calculations had been performed, the problem was formalized. After obtaining the probability of a tuple belonging to the class “needed” and performing the normalization of the value, it is taken as the level of the representation marker. Accordingly, the data is loaded onto the node if this value is greater than the optimal level of the representation marker for the DCIS node. Results. After calculating and obtaining the alternatives global priorities’ vector in order to improve the accuracy of the obtained result, the apparatus of fuzzy sets was used. The obtained vector of global priorities was presented as a vector of fuzzy digits for the data representation marker with subsequent transformation to the exact value. This approach made it possible to maintain the accuracy of the obtained result while decreasing the number of solution alternatives. Conclusions. While working on the research, the concept of a data representation marker on the DCIS node for the elements of the SQL query model was introduced. An aggregation function has been developed that allows determining the level of need for attributes and tuples in the database’s relation for the DCIS node based on the statistics of SQL queries. A model of the dependence of the database structure’s optimality criteria on the value of the data representation marker is built. Received further development method of analytic hierarchy process. The initialization of the alternatives’ pairwise comparisons matrix can be performed automatically according to the obtained mathematical models. Representation of the obtained result in the form of the vector of fuzzy numbers with the reduction to the exact value allows increasing the accuracy of the obtained results.

User Comprehension of Complexity Design Graph Reports.

Decision makers spend significant time and effort interpreting information derived from large multidimensional databases; data are usually represented by several dashboard diagrams. The Complexity Design (CoDe) methodology provides a technique modeling graphical reports on data extracted by a data warehouse, where the charts composing the dashboard diagrams are integrated with a visual representation of the logical relationships among them. The generated visualizations (CoDe Graphs) are automatically obtained by connecting dashboard diagrams through graphical conceptual links. After analyzing the state of the art regarding the evaluation of graphical representation comprehensibility, we propose a classification of those evaluation approaches and evaluate the comprehensibility of CoDe Graphs concerning dashboard reports through a controlled experiment, involving 47 participants. Results show that CoDe Graphs reduce participants effort while improving effectiveness and efficiency in comprehension tasks.

MULTIDIMENSIONAL DATABASES IN INFORMATION SYSTEMS OF UNIVERSITIES

The article is devoted to the description of the method of multidimensional database,which is an effective method of data storage, which allows analyzing data qualitatively, andmost importantly in a short time. The article discusses the capabilities of multidimensionaldatabases, in particular, multidimensional OLAP (On-Line Analytical Processing) cubes whenanalyzing large amounts of data. Provides an overview and features of a multidimensionaldatabase and discusses the steps you need to take with a multidimensional database tounderstand the structure and capabilities of an OLAP cube. To create a knowledge base, itdescribes the steps you can take to create and execute a multidimensional database thatyou can collect from various sources, save to a database, and then prepare a report usingOLAP analysis. Various information system data processing technologies such as OLTP andOLAP were considered. The algorithm of the data storage process for analysis purposes wasstudied. A model of a multidimensional database in the form of a three-dimensional cubewas presented. Examples of analysis and ways of obtaining information from the data cubewere also given. The use of a multidimensional database in higher education institutions as asimple and effective method of data storage is considered. There are also illustrations of thestructure of a higher educational institution to see the bulkiness of information, and what kindof information database operates in the educational institution.

Multi-Omics Analysis for Transcriptional Regulation of Immune-Related Targets Using Epigenetic Data: A New Research Direction.

BackgroundCurrently, a comprehensive method for exploration of transcriptional regulation has not been well established. We explored a novel pipeline to analyze transcriptional regulation using co-analysis of RNA sequencing (RNA-seq), assay for transposase-accessible chromatin using sequencing (ATAC-seq), and chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq).MethodsThe G protein-coupled receptors (GPCRs) possibly associated with macrophages were further filtered using a reduced-Cox regression model. ATAC-seq profiles were used to map the chromatin accessibility of the GPRC5B promoter region. Pearson analysis was performed to identify the transcription factor (TF) whose expression was correlated with open chromatin regions of GPRC5B promoter. ChIP-seq profiles were obtained to confirm the physical binding of GATA4 and its predicted binding regions. For verification, quantitative polymerase chain reaction (qPCR) and multidimensional database validations were performed.ResultsThe reduced-Cox regression model revealed the prognostic value of GPRC5B. A novel pipeline for TF exploration was proposed. With our novel pipeline, we first identified chr16:19884686-19885185 as a reproducible open chromatin region in the GPRC5B promoter. Thereafter, we confirmed the correlation between GATA4 expression and the accessibility of this region, confirmed its physical binding, and proved in vitro how its overexpression could regulate GPRC5B. GPRC5B was significantly downregulated in colon adenocarcinoma (COAD) as seen in 28 patient samples. The correlation between GPRC5B and macrophages in COAD was validated using multiple databases.ConclusionGPRC5B, correlated with macrophages, was a key GPCR affecting COAD prognosis. Further, with our novel pipeline, TF GATA4 was identified as a direct upstream of GPRC5B. This study proposed a novel pipeline for TF exploration and provided a theoretical basis for COAD therapy.

73 Characterization of tumor-infiltrating T-cell repertoire in human cancers

BackgroundTCR and BCR repertoire profiling is a promising technique that can provide a clinically useful window into the complex interactions between tumor cells and infiltrating lymphocytes. Despite recent advances in repertoire sequencing methods, the characterization of tumor-infiltrating T-cell repertoires has been limited to small sample sizes due to technical and material constraints. In this study, we constructed a large multidimensional database of repertoire data covering a diverse landscape of HLA genotypes and tumor neoantigens from routine clinical sequencing. We present a descriptive summary of repertoire profiles derived from tens of thousands of tumor samples from over fifty different cancer cohorts and characterize the associations between T-cell repertoires and various clinical and molecular features.MethodsTo enrich immune receptor transcripts detected by the Tempus RNA-sequencing workflow, hybrid capture probes tiling TCR and BCR genes were used. Repertoire profiling reads were aligned, assembled, and annotated against IMGT reference sequences. Repertoires are profiled as a component of Tempus|xT RNA sequencing and are summarized here for >25 thousand tumor samples from over 50 different cancer cohorts.ResultsWe demonstrate that the use of TCR/BCR hybrid capture probes is an effective method for enriching immune receptor transcripts in RNA-sequencing data without interfering with downstream transcriptomic analysis. These repertoires were profiled as part of a larger, multimodal DNA/RNA-sequencing pipeline that quantifies a variety of tumor clinical and molecular features. We explored the correlation between high-level repertoire metrics like richness (the number of unique receptor clonotypes in a given repertoire) and clonality/evenness (Shannon entropy) against both gene expression-based metrics (i.e. immune cell infiltration estimates, etc.) and mutational patterns (mutational burden and neoantigen load). Finally, we observed that the repertoire clonality of B-cell and T-cell driven cancers frequently exhibits clear monoclonal dominance for the tumor cells’ lymphoid receptors.ConclusionsTCR/BCR repertoire profiling can be incorporated into high-volume clinical RNA sequencing to generate a diverse multimodal dataset for studying the tumor-immune microenvironment. By creating a large-scale database of TCR/BCR repertoire profiles from a variety of tissue, HLA genotypes, and mutational contexts, we can better resolve the molecular and clinical correlates of cancer with host adaptive immunity.

Desain Business Intelligence untuk Manajemen Rumah Sakit

Organizational management, as well as hospital management, could not work precisely without defining the performance indicators to control all business process. This situation encourages the need for information and data analysis availability. BI includes applications, infrastructure, tools and practices that enable organizations to access and analyze data and information to improve and optimize the decisions and organization performance. BI has the potential to improve the quality, efficiency and effectiveness of hospital health services as well. The objective of this study was to design business intelligence prototype for the hospital. BI design was carried out with a Business Intelligence Roadmap approach which has 6 main stages, namely: (1) Justification, (2) Planning, (3) Business Analysis, (4) Design, (5) Construction and (6) Deployment. Data were collected from hospital activities includes registration, Electronic Medical Record (EMR) in the Imaging, Laboratory, Pharmacy, Operating Theater and Medical Check-Up departments activities. Designing BI was preceded by identifying technical and non-technical needs, then continued by designing BI itself. BI roadmap approach was used for this propose. Technical requirements for designing BI include hardware and software infrastructure readiness, while non-technical requirements include Business Analysis which consists of Project Requirements Definition, Data Analysis, Application Prototyping and Metadata repository Analysis. Designing BI itself includes: Designing a multidimensional database and designing ETL. The user interfaces for BI was shown in the Performance Dashboard, which allows organizations to track all aspects of their daily business activities and performance.

PLXNC1: A Novel Potential Immune-Related Target for Stomach Adenocarcinoma.

BackgroundGastric cancer is associated with tumor microenvironment and chronic inflammation, but the underlying tumor-promoting mechanisms still remain unknown.MethodsThe ATAC-seq was used to identify genes with chromatin accessibilities in promoter regions. The RNA-seq datasets were performed to identify differentially expressed genes (DEGs). Pearson correlation analysis with the mRNA expression of three families of tumor-related inflammation TFs was used to filter downstream DEGs. Cox univariate survival analysis was performed to identify the prognostic value. The ImmPort database and CIBERSORTx algorithm were used to investigate the regulatory relationship between hub DEGs and immune cells. Immunohistochemistry (IHC) and multidimensional database were performed to verification.ResultsIn this case, we require 2,454 genes with chromatin accessibility in promoter regions by ATAC-seq. Based on the gene expression profiles (RNA-seq), we identified 365 genes with chromatin accessibility and differential expression. Combined with the Cox univariate survival analysis, we identified 32 survival-related DEGs with chromatin accessibility. According to ImmPort database, CXCL3, PLXNC1, and EDN2 were identified as immune- related genes in STAD. By applying the CIBERSORTx algorithm and Pearson correlation, PLXNC1 was the only gene correlated with various immune cells, significantly associated with M2 macrophages. Furthermore, gene set variation analysis (GSVA) suggests the “hallmark_interferon_gamma_response” pathway was most significantly correlated with PLXNC1. Immunohistochemistry results revealed that PLXNC1 protein level was significantly higher in STAD tissues than in normal tissues (p < 0.001).ConclusionPLXNC1, regulated by IRF5, is an immune-related gene that was significantly associated with M2 macrophages and poor outcome in stomach adenocarcinoma.

Using Tumor-Infiltrating Immune Cells and a ceRNA Network Model to Construct a Prognostic Analysis Model of Thyroid Carcinoma.

Thyroid carcinoma is a solid malignant tumor that has had a fast-growing incidence in recent years. Our research used thyroid carcinoma gene expression profiling from TCGA (The Cancer Genome Atlas) database to identify differentially expressed ceRNAs. Using the gene expression profiling from 502 carcinoma thyroid tissues and 58 normal thyroid tissues from the TCGA database, we established the thyroid carcinoma-specific competitive endogenous RNA (ceRNA) network and found nine overall survival (OS)-associated genes (PRDM1, TGFBR3, E2F1, FGF1, ADAM12, ALPL, RET, AL928654.2, AC128688.2). We quantified the proportions of immune cells using the algorithm “CIBERSORT”, found three OS-associated immune cells (memory B cells, M0 macrophages, and activated dendritic cells), and established a thyroid carcinoma-specific immune cell network based on that. The good reliabilities AUC (area under the curve) of 10-year survival (0.955, 0.944, respectively) were accessed from the nomograms of genes and immune cells. Subsequently, by conducting co-expression analyses, we found a potential regulation network among ceRNAs and immune cells. Besides, we found that ALPL (alkaline phosphatase) and hsa-miR-204-5p were significantly correlated and that ALPL was related to activated dendritic cells. We took advantage of multi-dimensional databases to verify our discovery. Besides, immunohistochemistry (IHC) assays were conducted to detect the expression of a dendritic cell marker (CD11c) and ALPL in thyroid carcinoma (TC) and paracancerous tissues. In summary, our study found a potential mechanism in which hsa-miR-204-5p regulated ALPL in activated dendritic cells, which may allow them to play a critical role in thyroid carcinoma. These findings provide potential prognostic biomarkers and therapeutic targets for thyroid carcinoma.

Amphibole control on copper systematics in arcs: Insights from the analysis of global datasets

Copper, obtained from porphyry deposits formed in arc settings, is a critical resource, and is primarily sourced from magmas. However, the processes that shape the copper contents of arc magmas are up for debate. Existing models place emphasis on different petrological agents that explain large-scale trends in copper systematics. Previous studies have noted the ’Cu paradox,’ where the magmas with high Sr/Y ratios, indicative of ore-forming potential, have the lowest copper concentrations. Here we compile a multidimensional database of volcanic whole rock compositions and couple it with simple petrological models to elucidate the controls on volcanic whole rock compositions with respect to Cu. We show that calc-alkaline, high Sr/Y magmas undergo major element modification caused by extensive amphibole and/or garnet fractionation, which promotes sulphide precipitation and copper depletion. We demonstrate the importance of amphibole fractionation as a globally important process that promotes both calc-alkaline differentiation and sulphide fractionation in arc magmas, as well as its role in signalling the right set of chemical conditions in magmas that ultimately feed copper porphyry deposits. This work also raises the possibility of amphibole as a geochemical and petrological indicator of potential porphyry-forming conditions in a magma, which we show should be readily detectable by a combination of different geochemical metrics. Despite their paucity in copper, high Sr/Y magmas are associated with porphyry deposits, implying that the propensity of magmas to form such deposits depends on factors other than a magma’s bulk copper content.

Influence of Strategic Interrelationships and Decision-Making in Chilean Port Networks on Their Degree of Sustainability

An extensive literary review is carried out to determine the strategic and business advantages, and difficulties that non-smart ports must face to develop sustainability. Based on a two-port case study, the strategic text of the corporate missions of port administrators and operators is analyzed and classified in order to understand to what extend economic, social and environmental aspects are fulfilled. A conceptual model is designed for an information system based on indicators that can determine the state or degree of sustainability in the critical operational activities of the ports studied. A system is proposed that is based on a data warehouse core and a multidimensional database, which can be implemented in the ROLAP mode, allowing taking advantage of the good characteristics of relational databases without losing the OLAP approach. A discussion of the strategic feasibility of implementing this conceptual model of case study monitoring and its long-term benefits is delivered. The conclusions state that the development of this green technology requires cultural changes, public policy initiatives and the incorporation of new actors. In addition, more research is needed in this area to identify other relevant sustainable variables.

IDRA: An In-Storage Data Reorganization Accelerator for Multidimensional Databases

In the multidimensional databases, various storage methods for the main memory have been proposed for efficient data processing. On the other hand, storage devices such as SSD still leverage the conventional row-oriented storage method to avoid complicated operating system modification. Since there is a difference in storage methods between the main memory and storage device, it is necessary to reorganize the data between them. However, data reorganization in the conventional CPU-based systems causes a long latency due to a large number of memory accesses. In addition, it also causes high dynamic power consumption of a CPU. In this letter, we propose an in-storage data reorganization accelerator for multidimensional databases (IDRA). We place the IDRA in an SSD to reorganize data on the fly while loading it into the main memory without intervention of the CPU. In our evaluation on the off-the-shelf system (not simulation), the IDRA-based system improves the performance by 78.6% and reduces the system-wide energy by 30.3%, on average, compared to the conventional CPU-based system.