Geo-data Analysis Research Articles

Sodium octanoate mediates GPR84-dependent and independent protection against sepsis-induced myocardial dysfunction

IntroductionThis study aims to evaluate the therapeutic effects of sodium octanoate (SO), a medium-chain fatty acid salt, on SIMD in a murine model and to explore its underlying mechanisms. MethodsMale mice were subjected to sepsis models through two methods: intraperitoneal injection of lipopolysaccharide (LPS) and cecal ligation and punction (CLP). Mice received interval doses of SO every 2 hours or 4 hours for a total of six times or three times after LPS treatment. The relationship between SO and G protein-coupled receptor 84 (GPR84) was evaluated through GEO data analysis and molecular docking studies. DBA/2 mice were used to study the role of the GPR84 protein in the SO-mediated protection. Energy metabolomics was utilized to comprehensively assess the impact of SO on the levels of cardiac energy metabolic products in septic mice. histone modification identification techniques were used to further identify the specific sites of histone modification in the hearts of SO-treated septic mice. ResultsSO treatment significantly improved myocardial contractile function, restored the oxidative stress imbalance and enhanced the myocardium's resistance to oxidative injury. SO significantly promotes the expression of GPR84. The loss of GPR84 function markedly attenuates the protective effects of SO. SO enhanced myocardial energy metabolism by promoting the synthesis of acetyl-CoA and upregulating genes involved in fatty acid β-oxidation which were abolished by medium-chain acyl-CoA dehydrogenase (MCAD) knockdown. SO induced histone acetylation, particularly at H3K123 and H3K80. ConclusionOur study demonstrates that SO exerts protective effects against SIMD through both GPR84-mediated anti-inflammatory and antioxidant actions and GPR84-independent enhancement of myocardial energy metabolism, possibly mediated by MCAD.

Mechanisms Underlying the Protective Effects of Obeticholic Acid-Activated FXR in Valproic Acid-Induced Hepatotoxicity via Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations.

Valproic acid (VPA)-induced hepatotoxicity is among the most common and severe adverse drug reactions, limiting its clinical application. Recent studies have suggested that activating the farnesoid X receptor (FXR) could be a promising therapeutic approach to alleviate VPA-induced hepatotoxicity; however, related research remains limited. This study aims to comprehensively investigate the mechanisms underlying FXR activation by obeticholic acid (OCA) for the treatment of VPA-induced hepatotoxicity. Network pharmacology was performed to identify potential targets and pathways underlying the amelioration of VPA-induced hepatotoxicity by OCA. The identified pathways were validated through GEO data analysis, and the affinities between OCA and potential key targets were predicted using molecular docking as well as molecular dynamics simulations. A total of 462 targets associated with VPA-induced hepatotoxicity and 288 targets of OCA were identified, with 81 shared targets. KEGG pathway and GO enrichment analysis indicated that the effect of OCA on VPA-induced hepatotoxicity primarily involved lipid metabolism, as well as oxidative stress and inflammation. The results from GEO data analysis, molecular docking, and molecular dynamics simulations revealed a close association between bile secretion, the PPAR signaling pathway, and the treatment of VPA-induced hepatotoxicity by OCA. Our findings suggest that OCA exhibits potential therapeutic efficacy against VPAinduced hepatotoxicity through multiple targets and pathways, thereby highlighting the therapeutic potential of FXR as a target for treating VPA-induced hepatotoxicity.

Kinesin Family Member C1: Function in liver hepatocellular carcinoma and potential target for chemotherapeutic

MiR-105 exerts inhibitory effects on the development and progression of various cancers, including breast cancer, lung cancer, and gastric cancer. Through GEO data analysis, we observed decreased expression of miR-105 in liver cancer tissues compared to adjacent tissues. Furthermore, miR-105 downregulates KIFC1 expression levels by targeting its 3' UTR. KIFC1 (Kinesin Family Member C1), a Protein Coding gene, may play a role in mitotic metaphase plate polymerization and mitotic spindle assembly. However, our findings suggest that this gene could serve as a potential chemotherapeutic target for Liver hepatocellular carcinoma (LIHC). We obtained the LIHC dataset from the TCGA database and genotype Tissue Expression Project (GTEx) normal tissue data for differential analysis. Additionally, we utilized the cBioPortal database, tumor immune single-cell center (TISCH) database, gene set enrichment analysis (GSEA), and R software to investigate the possible functions and mechanisms of KIFC1. These findings were further validated through experiments such as immunohistochemistry and wound healing assays. Our results indicate that KIFC1 might be involved in DNA repair and cell cycle regulation in LIHC cells which subsequently impacts tumor cell proliferation; moreover, miR-105 influences hepatoma cell line proliferation via its interaction with KIFC1. Collectively, these results highlight the potential therapeutic significance of targeting KIFC1 for chemotherapy treatment in LIHC patients.

COL6A3 Exosomes Promote Tumor Dissemination and Metastasis in Epithelial Ovarian Cancer.

Our study explores the role of cancer-derived extracellular exosomes (EXs), particularly focusing on collagen alpha-3 (VI; COL6A3), in facilitating tumor dissemination and metastasis in epithelial ovarian cancer (EOC). We found that COL6A3 is expressed in aggressive ES2 derivatives, SKOV3 overexpressing COL6A3 (SKOV3/COL6A3), and mesenchymal-type ovarian carcinoma stromal progenitor cells (MSC-OCSPCs), as well as their EXs, but not in less aggressive SKOV3 cells or ES2 cells with COL6A3 knockdown (ES2/shCOL6A3). High COL6A3 expression correlates with worse overall survival among EOC patients, as evidenced by TCGA and GEO data analysis. In vitro experiments showed that EXs from MSC-OCSPCs or SKOV3/COL6A3 cells significantly enhance invasion ability in ES2 or SKOV3/COL6A3 cells, respectively (both, p <0.001). In contrast, ES2 cells with ES2/shCOL6A3 EXs exhibited reduced invasion ability (p < 0.001). In vivo, the average disseminated tumor numbers in the peritoneal cavity were significantly greater in mice receiving intraperitoneally injected SKOV3/COL6A3 cells than in SKOV3 cells (p < 0.001). Furthermore, mice intravenously (IV) injected with SKOV3/COL6A3 cells and SKOV3/COL6A3-EXs showed increased lung colonization compared to mice injected with SKOV3 cells and PBS (p = 0.007) or SKOV3/COL6A3 cells and PBS (p = 0.039). Knockdown of COL6A3 or treatment with EX inhibitor GW4869 or rapamycin-abolished COL6A3-EXs may suppress the aggressiveness of EOC.

Identification of Prognostic Genes in Acute Myeloid Leukemia Microenvironment: A Bioinformatic and Experimental Analysis.

Acute myeloid leukemia (AML) is a lethal hematologic malignancy with a variable prognosis that is highly dependent on the bone marrow microenvironment. Consequently, a better understanding of the AML microenvironment is crucial for early diagnosis, risk stratification, and personalized therapy. In recent years, the role of bioinformatics as a powerful tool in clarifying the complexities of cancer has become more prominent. Gene expression profile and clinical data of 173 AML patients were downloaded from the TCGA database, and the xCell algorithm was applied to calculate the microenvironment score (MS). Then, the correlation of MS with FAB classification, and CALGB cytogenetic risk category was investigated. Differentially expressed genes (DEGs) were identified, and the correlation analysis of DEGs with patient survival was done using univariate cox. The prognostic value of candidate prognostic DEGs was confirmed based on the GEO database. In the last step, real-time PCR was used to compare the expression of the top three prognostic genes between patients and the control group. During TCGA data analysis, 716 DEGs were identified, and survival analysis results showed that 152 DEGs had survival-related changes. In addition, the prognostic value of 31 candidate prognostic genes was confirmed by GEO data analysis. Finally, the expression analysis of FLVCR2, SMO, and CREB5 genes, the most related genes to patients' survival, was significantly different between patients and control groups. In summary, we identified key microenvironment-related genes that influence the survival of AML patients and may serve as prognostic and therapeutic targets.

Network mechanism of opioids in treating abdominal pain caused by T-cell lymphoma

Objective: To investigate the therapeutic mechanism of opioids in abdominal pain caused by T-cell lymphoma with a combination of network pharmacology and bioinformatics analysis. Methods: Initially, we collected T-cell lymphoma, abdominal pain, and opioid-related target genes from the Genecards database. The STRING database was then utilized to construct a protein-protein interaction (PPI) network and drug-disease-hub gene-signal pathway diagram was generated using Cytoscape. Furthermore, GO and KEGG enrichment analyses were performed using the bioinformatics website. To validate the results, the predictive hub genes were validated by download data from GEO database, which had been visualized by heatmap and histogram by using R, SPSS, and GraphPad. Results: There were 857 identified cross-targets. PPI revealed 9 hub targets. GO and KEGG analysis focused on PI3K/AKT, MAPK signaling factors, and their interactions. GEO data analysis using SPSS showed upregulation of SRC, MAPK1, HRAS, and AKT1 in tumor tissue. Additionally, SPSS univariate analysis revealed statistical significance in SRC, STAT3, AKT1, and TP53. Conclusions: This study confirms the effectiveness of opioids in preclinical studies on abdominal pain caused by T-cell lymphoma, which is associated with 10 genes. Besides, the involved function and signal pathway is associated with SRC, STAT3, AKT1, and TP53. These findings may contribute to the mechanism explanation of abdominal pain in T-cell lymphoma treated with opioids.

A Novel Indirubin-3-Monoxime Derivative Functions As Regulator of Proteostasis Via Targeting TRIM28 in Multiple Myeloma

Introduction: Multiple myeloma (MM) remains an incurable plasma cell malignancy. Agents disturbing the proteostasis play indispensable roles in MM treatment. Our previous studies have shown that the indirubin-3-monoxime (I3MO) works as a novel proteasome inhibitor that effectively inhibits MM cell growth. In this study, we aim to investigate the improvement of I3MO combined with histone deacetylase 6 (HDAC6) inhibitors, and clarified the molecular mechanisms. Methods: We constructed a compound 8b combined with I3MO and HADC6 inhibitor. The anti-MM effects of compound 8b were investigated in vivo and in vitro studies. Western blots, immunofluorescence, and proteasome activity assay were utilized to investigate the effects of 8b on MM cell proteasome activity, aggresome and autophagosome formation. RNA-seq and Dual-luciferase reporter gene assay were performed to identify the downstream targets of 8b treatment. We constructed MM cell lines with TRIM28 overexpression or shRNA knockdown. We further explored the function of TRIM28, one of the targets of 8b treatment, by ChIP-seq and IP-MS. Results: The novel I3MO derivative 8b more efficiently suppressed myeloma cell growth in vitro and in vivo. Compound 8b not decreased proteasome activity but inhibited the formation of aggresomes and autophagosomes. Interesting, we found that compound 8b efficiently inhibited TRIM28 promoter activity and repressed TRIM28 transcription level further. GEO data analysis demonstrated a positive correlation between TRIM28 and the expression of proteasome subunits as well as autophagy-related genes in MM. Further, we utilized ChIP-seq to confirm that TRIM28 could bind to the promoter region of multiple proteasome subunits-encoding genes, including PSMB1. Our data indicated that TRIM28 knockdown not only decreased the proteasome activity, but inhibited the formation of autophagosomes. Knockdown TRIM28 induced a higher sensitivity to BTZ treatment in MM cells. Furthermore, IP-MS with Co-IP analysis showed that TRIM28 interacts with 14-3-3ζ. TRIM28 acts as an E3 ligase in mediating the ubiquitination and degradation of 14-3-3ζ, a well-known negative regulator of autophagy. 14-3-3ζ degradation induced by TRIM28 activates autophagy in MM cells. These data suggested that TRIM28 suppressed by compound 8b would involve in proteostasis by regulation of proteasome activity and autophagy. Conclusions: Our study identified a novel compound 8b, I3MO combined with HDAC6i, efficiently suppressed MM cells survival. Additionally, we for the first time provided novel important insights for the roles of TRIM28 in MM pathobiology by regulation of proteasome activity and autophagy.

DATABASE USAGE IN SEMANTIC ONTOLOGIES

Semantic ontology languages are a way for experts to write down their knowledge in a commonly accepted way, it allows information to be understood by humans and machines. However, ontology tools do not provide the ability to use this data in a database for geodata-based analysis easily. The project is focused on ontology web language (OWL) usage for web content generation for business-to-business communication using geodata analysis. This specific paper is focused on selecting a database that could support the ontology tools. In the research scope, different databases were checked (relationship, document, and graph databases). In conclusion, graph databases were the most similar to the structure of the ontology, so it was chosen to use Neo4j as the database.

Prominin 2 decreases cisplatin sensitivity in non-small cell lung cancer and is modulated by CTCC binding factor.

Non-small cell lung cancer (NSCLC) is the major pathological type of lung cancer and accounts for the majority of lung cancer-related deaths worldwide. We investigated the molecular mechanism of prominin 2 (PROM2) involved in cisplatin resistance in NSCLC. The GEO database was analyzed to obtain differential genes to target PROM2. Immunohistochemistry and western blotting were used to detect protein expression levels. To examine the role of PROM2 in NSCLC, we overexpressed or knocked down PROM2 by transfection of plasmid or small interfering RNA. In functional experiments, CCK8 was used to detect cell viability. Cell migration and invasion and apoptosis were detected by transwell assay and flow cytometry, respectively. Mechanistically, the regulation of PROM2 by CTCF was detected by ChIP-PCR. In vivo experiments confirmed the role of PROM2 in NSCLC. GEO data analysis revealed that PROM2 was up-regulated in NSCLC, but its role in NSCLC remains unclear. Our clinical samples confirmed that the expression of PROM2 was markedly increased in NSCLC tissue. Functionally, Overexpression of PROM2 promotes cell proliferation, migration and invasion, and cisplatin resistance. CTCF up-regulates PROM2 expression by binding to its promoter region. In vivo experiments confirmed that PROM2 knockdown could inhibit tumor growth and increase the sensitivity of tumor cells to cisplatin. PROM2 up-regulation in NSCLC can attenuate the sensitivity of NSCLC cells to cisplatin and promote the proliferation, migration and invasion of tumor cells. PROM2 may provide a new target for the treatment of NSCLC.

Synthesizing differentially private location traces including co-locations

Privacy-preserving location synthesizers have been widely studied to perform private geo-data analysis. They have also been used for generating datasets for research or competitions. However, existing location synthesizers do not take into account the friendship information of users. Because friends tend to visit the same place at the same time in practice, a location synthesizer should consider such co-locations of friends to generate a more realistic dataset. In this paper, we propose a novel location synthesizer that generates location traces including co-locations of friends. Our location synthesizer models the information about the co-locations with two parameters: friendship probability and co-location count matrix. Our synthesizer generates a synthetic graph based on the friendship probability and then generates synthetic co-locations using the synthetic graph and the co-location count matrix. The two parameters in our synthesizer provide strong privacy guarantees—the friendship probability provides node differential privacy (DP) and the co-location count matrix provides user-level DP. We evaluate our synthesizer using two real datasets. Our experimental results show that our synthesizer preserves co-locations and other statistical features while providing DP with reasonable privacy budgets, e.g., 0.2-node DP and 2-user-level DP.

Mapping overwintering grain stubbles using machine-learning methods and image compositions for common agriculture policy-control and water framework directive connected activities

By leaving grain stubble on the field over the winter, farmers can actively contribute to nature conservation. Animals receive food and living space in these areas, endangered herbs too, but at the same time, it is reducing the input of substances into water and function as covered ground to protect the fertile soil from erosion. Remote sensing can deliver information on this type of land cover, which is also important for the agricultural administration. The farmer can be compensated by the government to let stubbles on the fields. The results of this study show that there is a high potential to detect overwintering stubbles fields area for common agriculture policy (CAP)-control to verify agricultural funding with Support Vector Machine (96.5%) by combining remote sensing classification methods with geodata analysis techniques using geoinformation systems (GIS), negative buffering (-20 m), and using threshold settings for correct classified pixel per field. These results give occasion for optimism for the further processing of this land cover by using it as an application for the government to support CAP- and water framework directive-activities.

ROS induced the Rab26 promoter hypermethylation to promote cigarette smoking-induced airway epithelial inflammation of COPD through activation of MAPK signaling

Cigarette smoking (CS) exposure-induced airway inflammatory responses drive the occurrence and development of emphysema and chronic obstructive pulmonary disease (COPD). However, its precise mechanisms have not been fully elucidated. In this study, we explore the role of Rab26 in CS exposure modulating the inflammatory response of airway epithelium and the novel mechanism of CS exposure regulation Rab26. These data showed that CS exposure and H2O2 (a type of ROS) suppressed the expression of Rab26 and increased the expression of DNMT3b in vivo and in vitro. GEO data analysis found the level of Rab26 was decreased in the lung tissue of COPD patients. CSE-induced ROS promoted DNA methylation of the Rab26 promoter and inhibited its promoter activity by elevating the DNMT3b level. Antioxidants N-Acetyl-l-cysteine (NAC), 5-Aza-2′-deoxycytidine (5-AZA) (DNA methylation inhibitor) and DNMT3B siRNA alleviated CSE's inhibitory effect on Rab26 expression in vitro. Importantly, NAC alleviated the improved expression of Rab26 and reduced DNMT3B expression, in the airway of smoking exposure as well as attenuated the inflammatory response in vivo. Overexpression of Rab26 attenuated CSE-induced production of inflammatory mediators through part inactivation of p38 and JNK MAPK. On the contrary, silencing Rab26 enhanced p38 and JNK activation and aggravated inflammatory response. These findings suggest that ROS-mediated Rab26 promoter hypermethylation is a critical step in cigarette smoking-induced airway epithelial inflammatory response. Restoring Rab26 in the airway epithelium might be a potential strategy for treating airway inflammation and COPD.

Designing a Location Trace Anonymization Contest

For a better understanding of anonymization methods for location traces, we have designed and held a location trace anonymization contest that deals with a long trace (400 events per user) and fine-grained locations (1024 regions). In our contest, each team anonymizes her original traces, and then the other teams perform privacy attacks against the anonymized traces. In other words, both defense and attack compete together, which is close to what happens in real life. Prior to our contest, we show that re-identification alone is insufficient as a privacy risk and that trace inference should be added as an additional risk. Specifically, we show an example of anonymization that is perfectly secure against re-identification and is not secure against trace inference. Based on this, our contest evaluates both the re-identification risk and trace inference risk and analyzes their relationship. Through our contest, we show several findings in a situation where both defense and attack compete together. In particular, we show that an anonymization method secure against trace inference is also secure against re-identification under the presence of appropriate pseudonymization. We also report defense and attack algorithms that won first place, and analyze the utility of anonymized traces submitted by teams in various applications such as POI recommendation and geo-data analysis.

Circ_0011129 Encapsulated by the Small Extracellular Vesicles Derived from Human Stem Cells Ameliorate Skin Photoaging

Photoaging is not only the main cause of skin aging caused by exogenous factors, it is also related to a variety of skin diseases and even malignant tumors. Excessive and repeated exposure to ultraviolet radiation, especially UVA induces oxidative stress, DNA damage, inflammation, and collagen and elastin degeneration, ultimately leads to skin photoaging, manifested by skin redness, coarse wrinkles, and pigmentation even skin cancer. There has been a large demand of effective prevention and medications but approaches in the current management of photoaging are very limited. In the previous study, we found that a non-coding circular RNA circ_0011129 acts as a miR-6732-5p adsorption sponge to inhibit the reduction of type I collagen and the denaturation and accumulation of elastin in UVA-induced HDF cells photoaging model. However, in vivo instability and efficient delivery to the target cell of circRNA is a major challenge for its clinical application. Therefore, improving its stability and delivery efficiency are desired. In this study, we proposed a strategy of delivering circ_0011129 with small extracellular vesicles (sEVs) from human adipose-derived stem cells (hADSCs) to intervene in the photoaging process. The results showed that sEVs from hADSCs in 3D bioreactor culture (3D-sEVs) can prevent photoaging. Consequently, by overexpressing circ_0011129 in hADSCs, we successfully loaded it into 3D-sEVs (3D-circ-sEVs) and its protective effect was better. Our studies provide a novel approach to preventing skin photoaging, which has important clinical significance and application value for the development of non-coding RNA drugs to treat skin photoaging. We first screened out hADSCs-derived sEVs with excellent anti-oxidant effects. We then compared the sEVs collected from traditional 2D culture with 3D bioreactor culture. By miRNA-seq and GEO data analysis, we found that miRNAs in 3D-sEVs were enriched in cell activities related to apoptosis, cellular senescence, and inflammation. Subsequently, we prepared circ_0011129-loaded 3D-sEVs (3D-circ-sEVs) by overexpressing it in hADSCs for the treatment of photoaging in vitro. We proved that 3D-circ-sEVs can interfere with the process of cell photoaging and protect cells from UVA radiation damage, as well as in a H2O2-induced oxidative stress model.

The integration of single-cell sequencing, TCGA, and GEO data analysis revealed that PRRT3-AS1 is a biomarker and therapeutic target of SKCM.

IntroductionSkin cutaneous melanoma (SKCM) is the world’s fourth deadliest cancer, and advanced SKCM leads to a poor prognosis. Novel biomarkers for SKCM diagnosis and prognosis are urgently needed. Long non-coding RNAs (lncRNAs) provide various biological functions and have been proved to play a significant role in tumor progression. Single-cell RNA sequencing (scRNA-seq) enables genome analysis at the single-cell level. This study explored prognostic lncRNAs in SKCM based on scRNA-seq and bulk RNA sequencing data.Materials and methodsThe TCGA cohort and melanoma samples in the GEO database (GSE72056, GSE19234, GSE15605, GSE7553, and GSE81383) were included in this study. Marker genes were filtered, and ensemble lncRNAs were annotated. The clinical significance of selected lncRNAs was verified through TCGA and GEO dataset analysis. SiRNA transfection, wound−healing and transwell assays were performed to evaluate the effect of PRRT3-AS1 on cellular function. Immune infiltration of the selected lncRNAs was also exhibited.ResultsA 5-marker-lncRNAs model of significant prognostic value was constructed based on GSE72056 and the TCGA cohort. PRRT3-AS1 combined with DANCR was then found to provide significant prognostic value in SKCM. PRRT3-AS1 was filtered for its higher expression in more advanced melanoma and significant prognosis value. Cellular function experiments in vitro revealed that PRRT3-AS1 may be required for cancer cell migration in SKCM. PRRT3-AS1 was found to be related to epithelial-mesenchymal transition (EMT) signaling pathways. DNA methylation of PRRT3-AS1 was negatively related to PRRT3-AS1 expression and showed significant prognosis value. In addition, PRRT3-AS1 may suppress immune infiltration and be involved in immunotherapy resistance.ConclusionPRRT3-AS1 may be a diagnostic and prognostic biomarker of SKCM.

ЦИФРОВЕ ГЕОЛОГІЧНЕ КАРТУВАННЯ ТА АНАЛІЗ ПОЛЬОВИХ ГЕОДАНИХ ІНСТРУМЕНТАМИ ТА ПЛАГІНАМИ QGIS

Purpose. The article aims is to demonstrate the possibilities, methods and advantages of using Quantum GIS (QGIS) plugins and mobile applications for digital geological mapping, input and primary analysis of field geodata in Earth Sciences – Structural Geology, Engineering Geology, Hydrogeology, Ecology, Geophysics, etc. Methodology. The most important tools and plugins of QGIS, an open source GIS program, that works on the most widespread platforms – Windows, MacOS X, Linux and applications on Android, iOS for digital mapping, structural geodata analysis, and visualization are used. Results. The most important tools and plugins of QGIS (Georeferencer GDAL, GarminCustomMap, Profile Tool, VoGIS-ProfilTool, qProf, qgSurf, Stereonet, qgis2web, and QField mobile application) are analyzed. Their using for digital geological mapping, input and analysis of structural and other geodata, construction of 2D topographic profiles, visualization of geodata in web browsers are briefly described. Scientific novelty. Tools and plugins of QGIS, which are necessary for certain purposes for working with various types of data on geological maps, their analysis, and construction of geological and geophysical profiles are defined. QGIS software applications allow to add new plugins, create your own notations for digital mapping that can be used to solve specific geological tasks and analyze geospatial and geological data, or add ready-made specialized geological notations according to geological standards. Practical significance. Complex using of basic, additional external plugins of QGIS and specialized geological markings contributes to effective field digital mapping, modern visualization of various types of geological maps with spatial reference, creation of new digital electronic and complex demonstration maps for printing and visualization in web browsers, construction topographic and geological 2D profiles, GIS analysis of structural geodata, slope analysis, etc. Data obtained in QGIS can be imported into such specialized programs as Petrel Exploration &amp; Production Software Platform (Shlumberger), MOVE Software (Midland Valley), etc. Keywords: Quantum GIS or QGIS, QGIS tools and plugins, digital geological mapping, QField, GIS analysis of structural geodata, slope analysis.

Оцінка ризиків втрати екосистемної послуги з регулювання ерозії міськими зеленими зонами

The paper presents the methodology and results of assessment the amount of erosion control ecosystem service (ES) and the risks of it loss. Quantitative risk assessment was carried out on the basis of the fact that the green zone is currently performing or will provide its service in the future in smaller volumes. Accordingly, the assessment consisted of: a) determining the loss of ES today and b) calculating the probability of loss in the future. For the assessment, the factors that affect the volume of ES have been determined and a set of relevant estimation parameters has been substantiated. They were included as attributes to the database of Kyiv green areas. The assessment of ES volumes was based on the comparison of erosion rates caused by natural and anthropogenic factors with the rate of soil formation. Estimation of risk probabilities was calculated based on Harrington desirability function adapted to the ecosystem services assessment. The source materials in the work were vector geodata sets: “Soils”, “Vegetation”, a digital elevation model. The sets were created based on information about city landscapes, remote sensing data, field and laboratory research. The assessment was carried out for 50 urban Kyiv green spaces. As a result of geodata analysis, it was determined that most of the studied objects provide their maximum ES and, accordingly, have very low risks of loss it today. However, 30% studied areas have medium risks of ES loss in the future. The assessment can be an effective tool in environmental decision-making. Firstly, it allows to identify the areas that need the most active actions to improve the possibilities of providing ES today. Secondly, the presented methodology allows to determine the factors that are cause a danger to ES, and therefore the risks of it loss in the future. Thus, this assessment, unlike others, is aimed at solving the issue of ensuring the sustainable functioning of green areas. Thirdly, this methodology is applicable to any territory and is a necessary component in the process of determining the total amount of ES provided by urban green zones. After all, the “separate” methods of each ES calculating when converting indicators into normalized values make it possible to avoid difficulties in the general assessment of ES, which arise due to the diversity and incomparability of measurement units.

A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 1: Pre-Processing of Scenario Based Flood Characteristics for the Current State of Land Use

Land use changes influence the water balance and often increase surface runoff. The resulting impacts on river flow, water level, and flood should be identified beforehand in the phase of spatial planning. In two consecutive papers, we develop a model-based decision support system for quantifying the hydrological and stream hydraulic impacts of land use changes. Part 1 presents the semi-automatic set-up of physically based hydrological and hydraulic models on the basis of geodata analysis for the current state. Appropriate hydrological model parameters for ungauged catchments are derived by a transfer from a calibrated model. In the regarded lowland river basins, parameters of surface and groundwater inflow turned out to be particularly important. While the calibration delivers very good to good model results for flow (Evol =2.4%, R = 0.84, NSE = 0.84), the model performance is good to satisfactory (Evol = −9.6%, R = 0.88, NSE = 0.59) in a different river system parametrized with the transfer procedure. After transferring the concept to a larger area with various small rivers, the current state is analyzed by running simulations based on statistical rainfall scenarios. Results include watercourse section-specific capacities and excess volumes in case of flooding. The developed approach can relatively quickly generate physically reliable and spatially high-resolution results. Part 2 builds on the data generated in part 1 and presents the subsequent approach to assess hydrologic/hydrodynamic impacts of potential land use changes.

Integrated bioinformatic analysis of miR-15a/16-1 cluster network in cervical cancer

The miR-15a/16-1 cluster is abnormally expressed in cervical cancer (CC) tissues and plays a vital role in cervical carcinogenesis. We aimed to evaluate the miR-15a/16-1 expression in healthy and cancerous cervical tissues, identify the associated networks, and to test its prognostic significance. miR-15a/16-1-MC expressions were analyzed in TCGA-CESC datasets by UALCAN, GEPIA2, and Datasetviewer. miR-15a/16-1 validated targets were extracted from mirTarBase and in silico functional analysis of the target genes were performed using WebGestalt. The interaction networks were constructed by the miRNet, STRING, and NetworkAnalyst tools. The prognostic significance and metastatic potential of the target genes were predicted using UALCAN and HCMDB. The FDA approved drugs to target miR-15a/16-1 and target gene network in CC were performed using DGIdb, STITCH and PanDrugs. TCGA-CESC and GEO data analysis suggested significant overexpression of miR-15a/16-1 in CC samples. The Kaplan-Meier survival analysis showed that miR-15a and its four target genes (BCL2, CCNE1, NUP50, and RBPJ) influence the overall survival of CC patients. Among the 66 differentially expressed target genes, 12 of them are linked to head, neck, or lung metastasis. Functional enrichment analysis predicted the association of this cluster with p53 signaling, human papillomavirus infection, PI3-AKT signaling pathway, and pathways in cancer. Drug-gene interaction analysis showed 52 potential FDA approved drugs to interact with the miR-15a/16-1 target genes. Nine of the 52 drugs are currently used as a chemotherapeutic agent for the treatment of CC patients. The present study shows that miR-15a/16-1 expression can be used as a clinical marker and target for therapy in CC.

Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features

AbstractWith the widespread use of LBSs (Location-based Services), synthesizing location traces plays an increasingly important role in analyzing spatial big data while protecting user privacy. In particular, a synthetic trace that preserves a feature specific to a cluster of users (e.g., those who commute by train, those who go shopping) is important for various geo-data analysis tasks and for providing a synthetic location dataset. Although location synthesizers have been widely studied, existing synthesizers do not provide su˚cient utility, privacy, or scalability, hence are not practical for large-scale location traces. To overcome this issue, we propose a novel location synthesizer calledPPMTF (Privacy-Preserving Multiple Tensor Factorization). We model various statistical features of the original traces by a transition-count tensor and a visit-count tensor. We factorize these two tensors simultaneously via multiple tensor factorization, and train factor matrices via posterior sampling. Then we synthesize traces from reconstructed tensors, and perform a plausible deniability test for a synthetic trace. We comprehensively evaluate PPMTF using two datasets. Our experimental results show that PPMTF preserves various statistical features including cluster-specific features, protects user privacy, and synthesizes large-scale location traces in practical time. PPMTF also significantly outperforms the state-of-theart methods in terms of utility and scalability at the same level of privacy.